Modern meta-heuristics based on nonlinear physics processes: A review of models and design procedures

NASA Astrophysics Data System (ADS)

Salcedo-Sanz, S.

2016-10-01

Meta-heuristic algorithms are problem-solving methods which try to find good-enough solutions to very hard optimization problems, at a reasonable computation time, where classical approaches fail, or cannot even been applied. Many existing meta-heuristics approaches are nature-inspired techniques, which work by simulating or modeling different natural processes in a computer. Historically, many of the most successful meta-heuristic approaches have had a biological inspiration, such as evolutionary computation or swarm intelligence paradigms, but in the last few years new approaches based on nonlinear physics processes modeling have been proposed and applied with success. Non-linear physics processes, modeled as optimization algorithms, are able to produce completely new search procedures, with extremely effective exploration capabilities in many cases, which are able to outperform existing optimization approaches. In this paper we review the most important optimization algorithms based on nonlinear physics, how they have been constructed from specific modeling of a real phenomena, and also their novelty in terms of comparison with alternative existing algorithms for optimization. We first review important concepts on optimization problems, search spaces and problems' difficulty. Then, the usefulness of heuristics and meta-heuristics approaches to face hard optimization problems is introduced, and some of the main existing classical versions of these algorithms are reviewed. The mathematical framework of different nonlinear physics processes is then introduced as a preparatory step to review in detail the most important meta-heuristics based on them. A discussion on the novelty of these approaches, their main computational implementation and design issues, and the evaluation of a novel meta-heuristic based on Strange Attractors mutation will be carried out to complete the review of these techniques. We also describe some of the most important application areas, in broad sense, of meta-heuristics, and describe free-accessible software frameworks which can be used to make easier the implementation of these algorithms.

Triplet supertree heuristics for the tree of life

PubMed Central

Lin, Harris T; Burleigh, J Gordon; Eulenstein, Oliver

2009-01-01

Background There is much interest in developing fast and accurate supertree methods to infer the tree of life. Supertree methods combine smaller input trees with overlapping sets of taxa to make a comprehensive phylogenetic tree that contains all of the taxa in the input trees. The intrinsically hard triplet supertree problem takes a collection of input species trees and seeks a species tree (supertree) that maximizes the number of triplet subtrees that it shares with the input trees. However, the utility of this supertree problem has been limited by a lack of efficient and effective heuristics. Results We introduce fast hill-climbing heuristics for the triplet supertree problem that perform a step-wise search of the tree space, where each step is guided by an exact solution to an instance of a local search problem. To realize time efficient heuristics we designed the first nontrivial algorithms for two standard search problems, which greatly improve on the time complexity to the best known (naïve) solutions by a factor of n and n2 (the number of taxa in the supertree). These algorithms enable large-scale supertree analyses based on the triplet supertree problem that were previously not possible. We implemented hill-climbing heuristics that are based on our new algorithms, and in analyses of two published supertree data sets, we demonstrate that our new heuristics outperform other standard supertree methods in maximizing the number of triplets shared with the input trees. Conclusion With our new heuristics, the triplet supertree problem is now computationally more tractable for large-scale supertree analyses, and it provides a potentially more accurate alternative to existing supertree methods. PMID:19208181

Testing process predictions of models of risky choice: a quantitative model comparison approach

PubMed Central

Pachur, Thorsten; Hertwig, Ralph; Gigerenzer, Gerd; Brandstätter, Eduard

2013-01-01

This article presents a quantitative model comparison contrasting the process predictions of two prominent views on risky choice. One view assumes a trade-off between probabilities and outcomes (or non-linear functions thereof) and the separate evaluation of risky options (expectation models). Another view assumes that risky choice is based on comparative evaluation, limited search, aspiration levels, and the forgoing of trade-offs (heuristic models). We derived quantitative process predictions for a generic expectation model and for a specific heuristic model, namely the priority heuristic (Brandstätter et al., 2006), and tested them in two experiments. The focus was on two key features of the cognitive process: acquisition frequencies (i.e., how frequently individual reasons are looked up) and direction of search (i.e., gamble-wise vs. reason-wise). In Experiment 1, the priority heuristic predicted direction of search better than the expectation model (although neither model predicted the acquisition process perfectly); acquisition frequencies, however, were inconsistent with both models. Additional analyses revealed that these frequencies were primarily a function of what Rubinstein (1988) called “similarity.” In Experiment 2, the quantitative model comparison approach showed that people seemed to rely more on the priority heuristic in difficult problems, but to make more trade-offs in easy problems. This finding suggests that risky choice may be based on a mental toolbox of strategies. PMID:24151472

A comparative study of the A* heuristic search algorithm used to solve efficiently a puzzle game

NASA Astrophysics Data System (ADS)

Iordan, A. E.

2018-01-01

The puzzle game presented in this paper consists in polyhedra (prisms, pyramids or pyramidal frustums) which can be moved using the free available spaces. The problem requires to be found the minimum number of movements in order the game reaches to a goal configuration starting from an initial configuration. Because the problem is enough complex, the principal difficulty in solving it is given by dimension of search space, that leads to necessity of a heuristic search. The improving of the search method consists into determination of a strong estimation by the heuristic function which will guide the search process to the most promising side of the search tree. The comparative study is realized among Manhattan heuristic and the Hamming heuristic using A* search algorithm implemented in Java. This paper also presents the necessary stages in object oriented development of a software used to solve efficiently this puzzle game. The modelling of the software is achieved through specific UML diagrams representing the phases of analysis, design and implementation, the system thus being described in a clear and practical manner. With the purpose to confirm the theoretical results which demonstrates that Manhattan heuristic is more efficient was used space complexity criterion. The space complexity was measured by the number of generated nodes from the search tree, by the number of the expanded nodes and by the effective branching factor. From the experimental results obtained by using the Manhattan heuristic, improvements were observed regarding space complexity of A* algorithm versus Hamming heuristic.

Aggregate age-at-marriage patterns from individual mate-search heuristics.

PubMed

Todd, Peter M; Billari, Francesco C; Simão, Jorge

2005-08-01

The distribution of age at first marriage shows well-known strong regularities across many countries and recent historical periods. We accounted for these patterns by developing agent-based models that simulate the aggregate behavior of individuals who are searching for marriage partners. Past models assumed fully rational agents with complete knowledge of the marriage market; our simulated agents used psychologically plausible simple heuristic mate search rules that adjust aspiration levels on the basis of a sequence of encounters with potential partners. Substantial individual variation must be included in the models to account for the demographically observed age-at-marriage patterns.

Meta-heuristic algorithms as tools for hydrological science

NASA Astrophysics Data System (ADS)

Yoo, Do Guen; Kim, Joong Hoon

2014-12-01

In this paper, meta-heuristic optimization techniques are introduced and their applications to water resources engineering, particularly in hydrological science are introduced. In recent years, meta-heuristic optimization techniques have been introduced that can overcome the problems inherent in iterative simulations. These methods are able to find good solutions and require limited computation time and memory use without requiring complex derivatives. Simulation-based meta-heuristic methods such as Genetic algorithms (GAs) and Harmony Search (HS) have powerful searching abilities, which can occasionally overcome the several drawbacks of traditional mathematical methods. For example, HS algorithms can be conceptualized from a musical performance process and used to achieve better harmony; such optimization algorithms seek a near global optimum determined by the value of an objective function, providing a more robust determination of musical performance than can be achieved through typical aesthetic estimation. In this paper, meta-heuristic algorithms and their applications (focus on GAs and HS) in hydrological science are discussed by subject, including a review of existing literature in the field. Then, recent trends in optimization are presented and a relatively new technique such as Smallest Small World Cellular Harmony Search (SSWCHS) is briefly introduced, with a summary of promising results obtained in previous studies. As a result, previous studies have demonstrated that meta-heuristic algorithms are effective tools for the development of hydrological models and the management of water resources.

Automated detection of heuristics and biases among pathologists in a computer-based system.

PubMed

Crowley, Rebecca S; Legowski, Elizabeth; Medvedeva, Olga; Reitmeyer, Kayse; Tseytlin, Eugene; Castine, Melissa; Jukic, Drazen; Mello-Thoms, Claudia

2013-08-01

The purpose of this study is threefold: (1) to develop an automated, computer-based method to detect heuristics and biases as pathologists examine virtual slide cases, (2) to measure the frequency and distribution of heuristics and errors across three levels of training, and (3) to examine relationships of heuristics to biases, and biases to diagnostic errors. The authors conducted the study using a computer-based system to view and diagnose virtual slide cases. The software recorded participant responses throughout the diagnostic process, and automatically classified participant actions based on definitions of eight common heuristics and/or biases. The authors measured frequency of heuristic use and bias across three levels of training. Biases studied were detected at varying frequencies, with availability and search satisficing observed most frequently. There were few significant differences by level of training. For representativeness and anchoring, the heuristic was used appropriately as often or more often than it was used in biased judgment. Approximately half of the diagnostic errors were associated with one or more biases. We conclude that heuristic use and biases were observed among physicians at all levels of training using the virtual slide system, although their frequencies varied. The system can be employed to detect heuristic use and to test methods for decreasing diagnostic errors resulting from cognitive biases.

Using tree diversity to compare phylogenetic heuristics.

PubMed

Sul, Seung-Jin; Matthews, Suzanne; Williams, Tiffani L

2009-04-29

Evolutionary trees are family trees that represent the relationships between a group of organisms. Phylogenetic heuristics are used to search stochastically for the best-scoring trees in tree space. Given that better tree scores are believed to be better approximations of the true phylogeny, traditional evaluation techniques have used tree scores to determine the heuristics that find the best scores in the fastest time. We develop new techniques to evaluate phylogenetic heuristics based on both tree scores and topologies to compare Pauprat and Rec-I-DCM3, two popular Maximum Parsimony search algorithms. Our results show that although Pauprat and Rec-I-DCM3 find the trees with the same best scores, topologically these trees are quite different. Furthermore, the Rec-I-DCM3 trees cluster distinctly from the Pauprat trees. In addition to our heatmap visualizations of using parsimony scores and the Robinson-Foulds distance to compare best-scoring trees found by the two heuristics, we also develop entropy-based methods to show the diversity of the trees found. Overall, Pauprat identifies more diverse trees than Rec-I-DCM3. Overall, our work shows that there is value to comparing heuristics beyond the parsimony scores that they find. Pauprat is a slower heuristic than Rec-I-DCM3. However, our work shows that there is tremendous value in using Pauprat to reconstruct trees-especially since it finds identical scoring but topologically distinct trees. Hence, instead of discounting Pauprat, effort should go in improving its implementation. Ultimately, improved performance measures lead to better phylogenetic heuristics and will result in better approximations of the true evolutionary history of the organisms of interest.

A multilevel probabilistic beam search algorithm for the shortest common supersequence problem.

PubMed

Gallardo, José E

2012-01-01

The shortest common supersequence problem is a classical problem with many applications in different fields such as planning, Artificial Intelligence and especially in Bioinformatics. Due to its NP-hardness, we can not expect to efficiently solve this problem using conventional exact techniques. This paper presents a heuristic to tackle this problem based on the use at different levels of a probabilistic variant of a classical heuristic known as Beam Search. The proposed algorithm is empirically analysed and compared to current approaches in the literature. Experiments show that it provides better quality solutions in a reasonable time for medium and large instances of the problem. For very large instances, our heuristic also provides better solutions, but required execution times may increase considerably.

Relevancy Ranking of Satellite Dataset Search Results

NASA Technical Reports Server (NTRS)

Lynnes, Christopher; Quinn, Patrick; Norton, James

2017-01-01

As the Variety of Earth science datasets increases, science researchers find it more challenging to discover and select the datasets that best fit their needs. The most common way of search providers to address this problem is to rank the datasets returned for a query by their likely relevance to the user. Large web page search engines typically use text matching supplemented with reverse link counts, semantic annotations and user intent modeling. However, this produces uneven results when applied to dataset metadata records simply externalized as a web page. Fortunately, data and search provides have decades of experience in serving data user communities, allowing them to form heuristics that leverage the structure in the metadata together with knowledge about the user community. Some of these heuristics include specific ways of matching the user input to the essential measurements in the dataset and determining overlaps of time range and spatial areas. Heuristics based on the novelty of the datasets can prioritize later, better versions of data over similar predecessors. And knowledge of how different user types and communities use data can be brought to bear in cases where characteristics of the user (discipline, expertise) or their intent (applications, research) can be divined. The Earth Observing System Data and Information System has begun implementing some of these heuristics in the relevancy algorithm of its Common Metadata Repository search engine.

Heuristic approach to image registration

NASA Astrophysics Data System (ADS)

Gertner, Izidor; Maslov, Igor V.

2000-08-01

Image registration, i.e. correct mapping of images obtained from different sensor readings onto common reference frame, is a critical part of multi-sensor ATR/AOR systems based on readings from different types of sensors. In order to fuse two different sensor readings of the same object, the readings have to be put into a common coordinate system. This task can be formulated as optimization problem in a space of all possible affine transformations of an image. In this paper, a combination of heuristic methods is explored to register gray- scale images. The modification of Genetic Algorithm is used as the first step in global search for optimal transformation. It covers the entire search space with (randomly or heuristically) scattered probe points and helps significantly reduce the search space to a subspace of potentially most successful transformations. Due to its discrete character, however, Genetic Algorithm in general can not converge while coming close to the optimum. Its termination point can be specified either as some predefined number of generations or as achievement of a certain acceptable convergence level. To refine the search, potential optimal subspaces are searched using more delicate and efficient for local search Taboo and Simulated Annealing methods.

Comparison of Decisions Quality of Heuristic Methods with Limited Depth-First Search Techniques in the Graph Shortest Path Problem

NASA Astrophysics Data System (ADS)

Vatutin, Eduard

2017-12-01

The article deals with the problem of analysis of effectiveness of the heuristic methods with limited depth-first search techniques of decision obtaining in the test problem of getting the shortest path in graph. The article briefly describes the group of methods based on the limit of branches number of the combinatorial search tree and limit of analyzed subtree depth used to solve the problem. The methodology of comparing experimental data for the estimation of the quality of solutions based on the performing of computational experiments with samples of graphs with pseudo-random structure and selected vertices and arcs number using the BOINC platform is considered. It also shows description of obtained experimental results which allow to identify the areas of the preferable usage of selected subset of heuristic methods depending on the size of the problem and power of constraints. It is shown that the considered pair of methods is ineffective in the selected problem and significantly inferior to the quality of solutions that are provided by ant colony optimization method and its modification with combinatorial returns.

Simple heuristics in over-the-counter drug choices: a new hint for medical education and practice.

PubMed

Riva, Silvia; Monti, Marco; Antonietti, Alessandro

2011-01-01

Over-the-counter (OTC) drugs are widely available and often purchased by consumers without advice from a health care provider. Many people rely on self-management of medications to treat common medical conditions. Although OTC medications are regulated by the National and the International Health and Drug Administration, many people are unaware of proper dosing, side effects, adverse drug reactions, and possible medication interactions. This study examined how subjects make their decisions to select an OTC drug, evaluating the role of cognitive heuristics which are simple and adaptive rules that help the decision-making process of people in everyday contexts. By analyzing 70 subjects' information-search and decision-making behavior when selecting OTC drugs, we examined the heuristics they applied in order to assess whether simple decision-making processes were also accurate and relevant. Subjects were tested with a sequence of two experimental tests based on a computerized Java system devised to analyze participants' choices in a virtual environment. We found that subjects' information-search behavior reflected the use of fast and frugal heuristics. In addition, although the heuristics which correctly predicted subjects' decisions implied significantly fewer cues on average than the subjects did in the information-search task, they were accurate in describing order of information search. A simple combination of a fast and frugal tree and a tallying rule predicted more than 78% of subjects' decisions. The current emphasis in health care is to shift some responsibility onto the consumer through expansion of self medication. To know which cognitive mechanisms are behind the choice of OTC drugs is becoming a relevant purpose of current medical education. These findings have implications both for the validity of simple heuristics describing information searches in the field of OTC drug choices and for current medical education, which has to prepare competent health specialists to orientate and support the choices of their patients.

Simple heuristics in over-the-counter drug choices: a new hint for medical education and practice

PubMed Central

Riva, Silvia; Monti, Marco; Antonietti, Alessandro

2011-01-01

Introduction Over-the-counter (OTC) drugs are widely available and often purchased by consumers without advice from a health care provider. Many people rely on self-management of medications to treat common medical conditions. Although OTC medications are regulated by the National and the International Health and Drug Administration, many people are unaware of proper dosing, side effects, adverse drug reactions, and possible medication interactions. Purpose This study examined how subjects make their decisions to select an OTC drug, evaluating the role of cognitive heuristics which are simple and adaptive rules that help the decision-making process of people in everyday contexts. Subjects and methods By analyzing 70 subjects’ information-search and decision-making behavior when selecting OTC drugs, we examined the heuristics they applied in order to assess whether simple decision-making processes were also accurate and relevant. Subjects were tested with a sequence of two experimental tests based on a computerized Java system devised to analyze participants’ choices in a virtual environment. Results We found that subjects’ information-search behavior reflected the use of fast and frugal heuristics. In addition, although the heuristics which correctly predicted subjects’ decisions implied significantly fewer cues on average than the subjects did in the information-search task, they were accurate in describing order of information search. A simple combination of a fast and frugal tree and a tallying rule predicted more than 78% of subjects’ decisions. Conclusion The current emphasis in health care is to shift some responsibility onto the consumer through expansion of self medication. To know which cognitive mechanisms are behind the choice of OTC drugs is becoming a relevant purpose of current medical education. These findings have implications both for the validity of simple heuristics describing information searches in the field of OTC drug choices and for current medical education, which has to prepare competent health specialists to orientate and support the choices of their patients. PMID:23745077

A Tabu-Search Heuristic for Deterministic Two-Mode Blockmodeling of Binary Network Matrices.

PubMed

Brusco, Michael; Steinley, Douglas

2011-10-01

Two-mode binary data matrices arise in a variety of social network contexts, such as the attendance or non-attendance of individuals at events, the participation or lack of participation of groups in projects, and the votes of judges on cases. A popular method for analyzing such data is two-mode blockmodeling based on structural equivalence, where the goal is to identify partitions for the row and column objects such that the clusters of the row and column objects form blocks that are either complete (all 1s) or null (all 0s) to the greatest extent possible. Multiple restarts of an object relocation heuristic that seeks to minimize the number of inconsistencies (i.e., 1s in null blocks and 0s in complete blocks) with ideal block structure is the predominant approach for tackling this problem. As an alternative, we propose a fast and effective implementation of tabu search. Computational comparisons across a set of 48 large network matrices revealed that the new tabu-search heuristic always provided objective function values that were better than those of the relocation heuristic when the two methods were constrained to the same amount of computation time.

An Update on Teaching the Employment Search.

ERIC Educational Resources Information Center

Andrews, Deborah, Ed.; Dyrud, Marilyn A., Ed.

1997-01-01

Presents five articles dealing with teaching job search strategies: (1) "Preparing a Scannable Resume" (Carol Roever); (2) "Preparing an Online Resume" (Tim Krause); (3) "Using the World Wide Web to Teach Employment Communication" (K. Virginia Hemby); (4) "A Visual Heuristic for Promoting a Rhetorically Based Job Search" (Helen Foster); and (5)…

AM: An Artificial Intelligence Approach to Discovery in Mathematics as Heuristic Search

DTIC Science & Technology

1976-07-01

Artificial Intelligence Approach to Discovery in Mathematics as Heuristic Search by Douglas B. Len-t APPROVED FOR PUBLIC RELEASE; DISTRIBUTION IS UNLIMITED (A...570 AM: An Artificial Intelligence Approach to Discovery in Mathematics as Heuristic Search by Douglas B. Lenat ABSTRACT A program, called "AM", is...While AM’s " approach " to empirical research may be used in other scientific domains, the main limitation (reliance on hindsight) will probably recur

A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem.

PubMed

Drake, John H; Özcan, Ender; Burke, Edmund K

2016-01-01

Hyper-heuristics are high-level methodologies for solving complex problems that operate on a search space of heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an existing set of low-level heuristics and applied to the current solution to produce a new solution at each point in the search. The use of crossover low-level heuristics is possible in an increasing number of general-purpose hyper-heuristic tools such as HyFlex and Hyperion. However, little work has been undertaken to assess how best to utilise it. Since a single-point search hyper-heuristic operates on a single candidate solution, and two candidate solutions are required for crossover, a mechanism is required to control the choice of the other solution. The frameworks we propose maintain a list of potential solutions for use in crossover. We investigate the use of such lists at two conceptual levels. First, crossover is controlled at the hyper-heuristic level where no problem-specific information is required. Second, it is controlled at the problem domain level where problem-specific information is used to produce good-quality solutions to use in crossover. A number of selection hyper-heuristics are compared using these frameworks over three benchmark libraries with varying properties for an NP-hard optimisation problem: the multidimensional 0-1 knapsack problem. It is shown that allowing crossover to be managed at the domain level outperforms managing crossover at the hyper-heuristic level in this problem domain.

QuickVina: accelerating AutoDock Vina using gradient-based heuristics for global optimization.

PubMed

Handoko, Stephanus Daniel; Ouyang, Xuchang; Su, Chinh Tran To; Kwoh, Chee Keong; Ong, Yew Soon

2012-01-01

Predicting binding between macromolecule and small molecule is a crucial phase in the field of rational drug design. AutoDock Vina, one of the most widely used docking software released in 2009, uses an empirical scoring function to evaluate the binding affinity between the molecules and employs the iterated local search global optimizer for global optimization, achieving a significantly improved speed and better accuracy of the binding mode prediction compared its predecessor, AutoDock 4. In this paper, we propose further improvement in the local search algorithm of Vina by heuristically preventing some intermediate points from undergoing local search. Our improved version of Vina-dubbed QVina-achieved a maximum acceleration of about 25 times with the average speed-up of 8.34 times compared to the original Vina when tested on a set of 231 protein-ligand complexes while maintaining the optimal scores mostly identical. Using our heuristics, larger number of different ligands can be quickly screened against a given receptor within the same time frame.

Hybridization of decomposition and local search for multiobjective optimization.

PubMed

Ke, Liangjun; Zhang, Qingfu; Battiti, Roberto

2014-10-01

Combining ideas from evolutionary algorithms, decomposition approaches, and Pareto local search, this paper suggests a simple yet efficient memetic algorithm for combinatorial multiobjective optimization problems: memetic algorithm based on decomposition (MOMAD). It decomposes a combinatorial multiobjective problem into a number of single objective optimization problems using an aggregation method. MOMAD evolves three populations: 1) population P(L) for recording the current solution to each subproblem; 2) population P(P) for storing starting solutions for Pareto local search; and 3) an external population P(E) for maintaining all the nondominated solutions found so far during the search. A problem-specific single objective heuristic can be applied to these subproblems to initialize the three populations. At each generation, a Pareto local search method is first applied to search a neighborhood of each solution in P(P) to update P(L) and P(E). Then a single objective local search is applied to each perturbed solution in P(L) for improving P(L) and P(E), and reinitializing P(P). The procedure is repeated until a stopping condition is met. MOMAD provides a generic hybrid multiobjective algorithmic framework in which problem specific knowledge, well developed single objective local search and heuristics and Pareto local search methods can be hybridized. It is a population based iterative method and thus an anytime algorithm. Extensive experiments have been conducted in this paper to study MOMAD and compare it with some other state-of-the-art algorithms on the multiobjective traveling salesman problem and the multiobjective knapsack problem. The experimental results show that our proposed algorithm outperforms or performs similarly to the best so far heuristics on these two problems.

A computing method for spatial accessibility based on grid partition

NASA Astrophysics Data System (ADS)

Ma, Linbing; Zhang, Xinchang

2007-06-01

An accessibility computing method and process based on grid partition was put forward in the paper. As two important factors impacting on traffic, density of road network and relative spatial resistance for difference land use was integrated into computing traffic cost in each grid. A* algorithms was inducted to searching optimum traffic cost of grids path, a detailed searching process and definition of heuristic evaluation function was described in the paper. Therefore, the method can be implemented more simply and its data source is obtained more easily. Moreover, by changing heuristic searching information, more reasonable computing result can be obtained. For confirming our research, a software package was developed with C# language under ArcEngine9 environment. Applying the computing method, a case study on accessibility of business districts in Guangzhou city was carried out.

Fixing Dataset Search

NASA Technical Reports Server (NTRS)

Lynnes, Chris

2014-01-01

Three current search engines are queried for ozone data at the GES DISC. The results range from sub-optimal to counter-intuitive. We propose a method to fix dataset search by implementing a robust relevancy ranking scheme. The relevancy ranking scheme is based on several heuristics culled from more than 20 years of helping users select datasets.

Memory-based decision-making with heuristics: evidence for a controlled activation of memory representations.

PubMed

Khader, Patrick H; Pachur, Thorsten; Meier, Stefanie; Bien, Siegfried; Jost, Kerstin; Rösler, Frank

2011-11-01

Many of our daily decisions are memory based, that is, the attribute information about the decision alternatives has to be recalled. Behavioral studies suggest that for such decisions we often use simple strategies (heuristics) that rely on controlled and limited information search. It is assumed that these heuristics simplify decision-making by activating long-term memory representations of only those attributes that are necessary for the decision. However, from behavioral studies alone, it is unclear whether using heuristics is indeed associated with limited memory search. The present study tested this assumption by monitoring the activation of specific long-term-memory representations with fMRI while participants made memory-based decisions using the "take-the-best" heuristic. For different decision trials, different numbers and types of information had to be retrieved and processed. The attributes consisted of visual information known to be represented in different parts of the posterior cortex. We found that the amount of information required for a decision was mirrored by a parametric activation of the dorsolateral PFC. Such a parametric pattern was also observed in all posterior areas, suggesting that activation was not limited to those attributes required for a decision. However, the posterior increases were systematically modulated by the relative importance of the information for making a decision. These findings suggest that memory-based decision-making is mediated by the dorsolateral PFC, which selectively controls posterior storage areas. In addition, the systematic modulations of the posterior activations indicate a selective boosting of activation of decision-relevant attributes.

Local search heuristic for the discrete leader-follower problem with multiple follower objectives

NASA Astrophysics Data System (ADS)

Kochetov, Yury; Alekseeva, Ekaterina; Mezmaz, Mohand

2016-10-01

We study a discrete bilevel problem, called as well as leader-follower problem, with multiple objectives at the lower level. It is assumed that constraints at the upper level can include variables of both levels. For such ill-posed problem we define feasible and optimal solutions for pessimistic case. A central point of this work is a two stage method to get a feasible solution under the pessimistic case, given a leader decision. The target of the first stage is a follower solution that violates the leader constraints. The target of the second stage is a pessimistic feasible solution. Each stage calls a heuristic and a solver for a series of particular mixed integer programs. The method is integrated inside a local search based heuristic that is designed to find near-optimal leader solutions.

Boolean Reasoning and Informed Search in the Minimization of Logic Circuits

DTIC Science & Technology

1992-03-01

motivation of this project as well as a definition of the problem. The scope of the effort was presented, as well as the assumptions found to be...in the resulting formula than the expansion-based product operation. The primary motive for using the expansion-based product versus a cross-product...eliminant is formed is the least-binate-variable heuristic described in Chapter 2. The motivation for this heuristic was illustrated in Example 3.3. The

Implied alignment: a synapomorphy-based multiple-sequence alignment method and its use in cladogram search

NASA Technical Reports Server (NTRS)

Wheeler, Ward C.

2003-01-01

A method to align sequence data based on parsimonious synapomorphy schemes generated by direct optimization (DO; earlier termed optimization alignment) is proposed. DO directly diagnoses sequence data on cladograms without an intervening multiple-alignment step, thereby creating topology-specific, dynamic homology statements. Hence, no multiple-alignment is required to generate cladograms. Unlike general and globally optimal multiple-alignment procedures, the method described here, implied alignment (IA), takes these dynamic homologies and traces them back through a single cladogram, linking the unaligned sequence positions in the terminal taxa via DO transformation series. These "lines of correspondence" link ancestor-descendent states and, when displayed as linearly arrayed columns without hypothetical ancestors, are largely indistinguishable from standard multiple alignment. Since this method is based on synapomorphy, the treatment of certain classes of insertion-deletion (indel) events may be different from that of other alignment procedures. As with all alignment methods, results are dependent on parameter assumptions such as indel cost and transversion:transition ratios. Such an IA could be used as a basis for phylogenetic search, but this would be questionable since the homologies derived from the implied alignment depend on its natal cladogram and any variance, between DO and IA + Search, due to heuristic approach. The utility of this procedure in heuristic cladogram searches using DO and the improvement of heuristic cladogram cost calculations are discussed. c2003 The Willi Hennig Society. Published by Elsevier Science (USA). All rights reserved.

Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems.

PubMed

Ortiz-Bayliss, José Carlos; Amaya, Ivan; Conant-Pablos, Santiago Enrique; Terashima-Marín, Hugo

2018-01-01

When solving constraint satisfaction problems (CSPs), it is a common practice to rely on heuristics to decide which variable should be instantiated at each stage of the search. But, this ordering influences the search cost. Even so, and to the best of our knowledge, no earlier work has dealt with how first variable orderings affect the overall cost. In this paper, we explore the cost of finding high-quality orderings of variables within constraint satisfaction problems. We also study differences among the orderings produced by some commonly used heuristics and the way bad first decisions affect the search cost. One of the most important findings of this work confirms the paramount importance of first decisions. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. We propose a simple method to improve early decisions of heuristics. By using it, performance of heuristics increases.

Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems

PubMed Central

Amaya, Ivan

2018-01-01

When solving constraint satisfaction problems (CSPs), it is a common practice to rely on heuristics to decide which variable should be instantiated at each stage of the search. But, this ordering influences the search cost. Even so, and to the best of our knowledge, no earlier work has dealt with how first variable orderings affect the overall cost. In this paper, we explore the cost of finding high-quality orderings of variables within constraint satisfaction problems. We also study differences among the orderings produced by some commonly used heuristics and the way bad first decisions affect the search cost. One of the most important findings of this work confirms the paramount importance of first decisions. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. We propose a simple method to improve early decisions of heuristics. By using it, performance of heuristics increases. PMID:29681923

A novel heuristic algorithm for capacitated vehicle routing problem

NASA Astrophysics Data System (ADS)

Kır, Sena; Yazgan, Harun Reşit; Tüncel, Emre

2017-09-01

The vehicle routing problem with the capacity constraints was considered in this paper. It is quite difficult to achieve an optimal solution with traditional optimization methods by reason of the high computational complexity for large-scale problems. Consequently, new heuristic or metaheuristic approaches have been developed to solve this problem. In this paper, we constructed a new heuristic algorithm based on the tabu search and adaptive large neighborhood search (ALNS) with several specifically designed operators and features to solve the capacitated vehicle routing problem (CVRP). The effectiveness of the proposed algorithm was illustrated on the benchmark problems. The algorithm provides a better performance on large-scaled instances and gained advantage in terms of CPU time. In addition, we solved a real-life CVRP using the proposed algorithm and found the encouraging results by comparison with the current situation that the company is in.

An adaptive random search for short term generation scheduling with network constraints.

PubMed

Marmolejo, J A; Velasco, Jonás; Selley, Héctor J

2017-01-01

This paper presents an adaptive random search approach to address a short term generation scheduling with network constraints, which determines the startup and shutdown schedules of thermal units over a given planning horizon. In this model, we consider the transmission network through capacity limits and line losses. The mathematical model is stated in the form of a Mixed Integer Non Linear Problem with binary variables. The proposed heuristic is a population-based method that generates a set of new potential solutions via a random search strategy. The random search is based on the Markov Chain Monte Carlo method. The main key of the proposed method is that the noise level of the random search is adaptively controlled in order to exploring and exploiting the entire search space. In order to improve the solutions, we consider coupling a local search into random search process. Several test systems are presented to evaluate the performance of the proposed heuristic. We use a commercial optimizer to compare the quality of the solutions provided by the proposed method. The solution of the proposed algorithm showed a significant reduction in computational effort with respect to the full-scale outer approximation commercial solver. Numerical results show the potential and robustness of our approach.

Feature selection methods for big data bioinformatics: A survey from the search perspective.

PubMed

Wang, Lipo; Wang, Yaoli; Chang, Qing

2016-12-01

This paper surveys main principles of feature selection and their recent applications in big data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and embedded approaches to feature selection, we formulate feature selection as a combinatorial optimization or search problem and categorize feature selection methods into exhaustive search, heuristic search, and hybrid methods, where heuristic search methods may further be categorized into those with or without data-distilled feature ranking measures. Copyright © 2016 Elsevier Inc. All rights reserved.

Hyper-heuristics with low level parameter adaptation.

PubMed

Ren, Zhilei; Jiang, He; Xuan, Jifeng; Luo, Zhongxuan

2012-01-01

Recent years have witnessed the great success of hyper-heuristics applying to numerous real-world applications. Hyper-heuristics raise the generality of search methodologies by manipulating a set of low level heuristics (LLHs) to solve problems, and aim to automate the algorithm design process. However, those LLHs are usually parameterized, which may contradict the domain independent motivation of hyper-heuristics. In this paper, we show how to automatically maintain low level parameters (LLPs) using a hyper-heuristic with LLP adaptation (AD-HH), and exemplify the feasibility of AD-HH by adaptively maintaining the LLPs for two hyper-heuristic models. Furthermore, aiming at tackling the search space expansion due to the LLP adaptation, we apply a heuristic space reduction (SAR) mechanism to improve the AD-HH framework. The integration of the LLP adaptation and the SAR mechanism is able to explore the heuristic space more effectively and efficiently. To evaluate the performance of the proposed algorithms, we choose the p-median problem as a case study. The empirical results show that with the adaptation of the LLPs and the SAR mechanism, the proposed algorithms are able to achieve competitive results over the three heterogeneous classes of benchmark instances.

Solving Inverse Kinematics of Robot Manipulators by Means of Meta-Heuristic Optimisation

NASA Astrophysics Data System (ADS)

Wichapong, Kritsada; Bureerat, Sujin; Pholdee, Nantiwat

2018-05-01

This paper presents the use of meta-heuristic algorithms (MHs) for solving inverse kinematics of robot manipulators based on using forward kinematic. Design variables are joint angular displacements used to move a robot end-effector to the target in the Cartesian space while the design problem is posed to minimize error between target points and the positions of the robot end-effector. The problem is said to be a dynamic problem as the target points always changed by a robot user. Several well established MHs are used to solve the problem and the results obtained from using different meta-heuristics are compared based on the end-effector error and searching speed of the algorithms. From the study, the best performer will be obtained for setting as the baseline for future development of MH-based inverse kinematic solving.

Microscopy as a statistical, Rényi-Ulam, half-lie game: a new heuristic search strategy to accelerate imaging.

PubMed

Drumm, Daniel W; Greentree, Andrew D

2017-11-07

Finding a fluorescent target in a biological environment is a common and pressing microscopy problem. This task is formally analogous to the canonical search problem. In ideal (noise-free, truthful) search problems, the well-known binary search is optimal. The case of half-lies, where one of two responses to a search query may be deceptive, introduces a richer, Rényi-Ulam problem and is particularly relevant to practical microscopy. We analyse microscopy in the contexts of Rényi-Ulam games and half-lies, developing a new family of heuristics. We show the cost of insisting on verification by positive result in search algorithms; for the zero-half-lie case bisectioning with verification incurs a 50% penalty in the average number of queries required. The optimal partitioning of search spaces directly following verification in the presence of random half-lies is determined. Trisectioning with verification is shown to be the most efficient heuristic of the family in a majority of cases.

Fluency Heuristic: A Model of How the Mind Exploits a By-Product of Information Retrieval

ERIC Educational Resources Information Center

Hertwig, Ralph; Herzog, Stefan M.; Schooler, Lael J.; Reimer, Torsten

2008-01-01

Boundedly rational heuristics for inference can be surprisingly accurate and frugal for several reasons. They can exploit environmental structures, co-opt complex capacities, and elude effortful search by exploiting information that automatically arrives on the mental stage. The fluency heuristic is a prime example of a heuristic that makes the…

A Hidden Markov Model Approach to the Problem of Heuristic Selection in Hyper-Heuristics with a Case Study in High School Timetabling Problems.

PubMed

Kheiri, Ahmed; Keedwell, Ed

2017-01-01

Operations research is a well-established field that uses computational systems to support decisions in business and public life. Good solutions to operations research problems can make a large difference to the efficient running of businesses and organisations and so the field often searches for new methods to improve these solutions. The high school timetabling problem is an example of an operations research problem and is a challenging task which requires assigning events and resources to time slots subject to a set of constraints. In this article, a new sequence-based selection hyper-heuristic is presented that produces excellent results on a suite of high school timetabling problems. In this study, we present an easy-to-implement, easy-to-maintain, and effective sequence-based selection hyper-heuristic to solve high school timetabling problems using a benchmark of unified real-world instances collected from different countries. We show that with sequence-based methods, it is possible to discover new best known solutions for a number of the problems in the timetabling domain. Through this investigation, the usefulness of sequence-based selection hyper-heuristics has been demonstrated and the capability of these methods has been shown to exceed the state of the art.

Efficient heuristics for maximum common substructure search.

PubMed

Englert, Péter; Kovács, Péter

2015-05-26

Maximum common substructure search is a computationally hard optimization problem with diverse applications in the field of cheminformatics, including similarity search, lead optimization, molecule alignment, and clustering. Most of these applications have strict constraints on running time, so heuristic methods are often preferred. However, the development of an algorithm that is both fast enough and accurate enough for most practical purposes is still a challenge. Moreover, in some applications, the quality of a common substructure depends not only on its size but also on various topological features of the one-to-one atom correspondence it defines. Two state-of-the-art heuristic algorithms for finding maximum common substructures have been implemented at ChemAxon Ltd., and effective heuristics have been developed to improve both their efficiency and the relevance of the atom mappings they provide. The implementations have been thoroughly evaluated and compared with existing solutions (KCOMBU and Indigo). The heuristics have been found to greatly improve the performance and applicability of the algorithms. The purpose of this paper is to introduce the applied methods and present the experimental results.

Hierarchical heuristic search using a Gaussian mixture model for UAV coverage planning.

PubMed

Lin, Lanny; Goodrich, Michael A

2014-12-01

During unmanned aerial vehicle (UAV) search missions, efficient use of UAV flight time requires flight paths that maximize the probability of finding the desired subject. The probability of detecting the desired subject based on UAV sensor information can vary in different search areas due to environment elements like varying vegetation density or lighting conditions, making it likely that the UAV can only partially detect the subject. This adds another dimension of complexity to the already difficult (NP-Hard) problem of finding an optimal search path. We present a new class of algorithms that account for partial detection in the form of a task difficulty map and produce paths that approximate the payoff of optimal solutions. The algorithms use the mode goodness ratio heuristic that uses a Gaussian mixture model to prioritize search subregions. The algorithms search for effective paths through the parameter space at different levels of resolution. We compare the performance of the new algorithms against two published algorithms (Bourgault's algorithm and LHC-GW-CONV algorithm) in simulated searches with three real search and rescue scenarios, and show that the new algorithms outperform existing algorithms significantly and can yield efficient paths that yield payoffs near the optimal.

Fitness landscapes, heuristics and technological paradigms: A critique on random search models in evolutionary economics

NASA Astrophysics Data System (ADS)

Frenken, Koen

2001-06-01

The biological evolution of complex organisms, in which the functioning of genes is interdependent, has been analyzed as "hill-climbing" on NK fitness landscapes through random mutation and natural selection. In evolutionary economics, NK fitness landscapes have been used to simulate the evolution of complex technological systems containing elements that are interdependent in their functioning. In these models, economic agents randomly search for new technological design by trial-and-error and run the risk of ending up in sub-optimal solutions due to interdependencies between the elements in a complex system. These models of random search are legitimate for reasons of modeling simplicity, but remain limited as these models ignore the fact that agents can apply heuristics. A specific heuristic is one that sequentially optimises functions according to their ranking by users of the system. To model this heuristic, a generalized NK-model is developed. In this model, core elements that influence many functions can be distinguished from peripheral elements that affect few functions. The concept of paradigmatic search can then be analytically defined as search that leaves core elements in tact while concentrating on improving functions by mutation of peripheral elements.

SPARSE: quadratic time simultaneous alignment and folding of RNAs without sequence-based heuristics.

PubMed

Will, Sebastian; Otto, Christina; Miladi, Milad; Möhl, Mathias; Backofen, Rolf

2015-08-01

RNA-Seq experiments have revealed a multitude of novel ncRNAs. The gold standard for their analysis based on simultaneous alignment and folding suffers from extreme time complexity of [Formula: see text]. Subsequently, numerous faster 'Sankoff-style' approaches have been suggested. Commonly, the performance of such methods relies on sequence-based heuristics that restrict the search space to optimal or near-optimal sequence alignments; however, the accuracy of sequence-based methods breaks down for RNAs with sequence identities below 60%. Alignment approaches like LocARNA that do not require sequence-based heuristics, have been limited to high complexity ([Formula: see text] quartic time). Breaking this barrier, we introduce the novel Sankoff-style algorithm 'sparsified prediction and alignment of RNAs based on their structure ensembles (SPARSE)', which runs in quadratic time without sequence-based heuristics. To achieve this low complexity, on par with sequence alignment algorithms, SPARSE features strong sparsification based on structural properties of the RNA ensembles. Following PMcomp, SPARSE gains further speed-up from lightweight energy computation. Although all existing lightweight Sankoff-style methods restrict Sankoff's original model by disallowing loop deletions and insertions, SPARSE transfers the Sankoff algorithm to the lightweight energy model completely for the first time. Compared with LocARNA, SPARSE achieves similar alignment and better folding quality in significantly less time (speedup: 3.7). At similar run-time, it aligns low sequence identity instances substantially more accurate than RAF, which uses sequence-based heuristics. © The Author 2015. Published by Oxford University Press.

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis.

PubMed

Tashkova, Katerina; Korošec, Peter; Silc, Jurij; Todorovski, Ljupčo; Džeroski, Sašo

2011-10-11

We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and artificial data, for all observability scenarios considered, and for all amounts of noise added to the artificial data. In sum, the meta-heuristic methods considered are suitable for estimating the parameters in the ODE model of the dynamics of endocytosis under a range of conditions: With the model and conditions being representative of parameter estimation tasks in ODE models of biochemical systems, our results clearly highlight the promise of bio-inspired meta-heuristic methods for parameter estimation in dynamic system models within system biology.

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis

PubMed Central

2011-01-01

Background We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. Results We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Conclusions Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and artificial data, for all observability scenarios considered, and for all amounts of noise added to the artificial data. In sum, the meta-heuristic methods considered are suitable for estimating the parameters in the ODE model of the dynamics of endocytosis under a range of conditions: With the model and conditions being representative of parameter estimation tasks in ODE models of biochemical systems, our results clearly highlight the promise of bio-inspired meta-heuristic methods for parameter estimation in dynamic system models within system biology. PMID:21989196

Approximation algorithms for the min-power symmetric connectivity problem

NASA Astrophysics Data System (ADS)

Plotnikov, Roman; Erzin, Adil; Mladenovic, Nenad

2016-10-01

We consider the NP-hard problem of synthesis of optimal spanning communication subgraph in a given arbitrary simple edge-weighted graph. This problem occurs in the wireless networks while minimizing the total transmission power consumptions. We propose several new heuristics based on the variable neighborhood search metaheuristic for the approximation solution of the problem. We have performed a numerical experiment where all proposed algorithms have been executed on the randomly generated test samples. For these instances, on average, our algorithms outperform the previously known heuristics.

Kepler: Analogies in the search for the law of refraction.

PubMed

Cardona, Carlos Alberto

2016-10-01

This paper examines the methodology used by Kepler to discover a quantitative law of refraction. The aim is to argue that this methodology follows a heuristic method based on the following two Pythagorean principles: (1) sameness is made known by sameness, and (2) harmony arises from establishing a limit to what is unlimited. We will analyse some of the author's proposed analogies to find the aforementioned law and argue that the investigation's heuristic pursues such principles. Copyright © 2016 Elsevier Ltd. All rights reserved.

DETECTORS AND EXPERIMENTAL METHODS: Heuristic approach for peak regions estimation in gamma-ray spectra measured by a NaI detector

NASA Astrophysics Data System (ADS)

Zhu, Meng-Hua; Liu, Liang-Gang; You, Zhong; Xu, Ao-Ao

2009-03-01

In this paper, a heuristic approach based on Slavic's peak searching method has been employed to estimate the width of peak regions for background removing. Synthetic and experimental data are used to test this method. With the estimated peak regions using the proposed method in the whole spectrum, we find it is simple and effective enough to be used together with the Statistics-sensitive Nonlinear Iterative Peak-Clipping method.

Multiobjective immune algorithm with nondominated neighbor-based selection.

PubMed

Gong, Maoguo; Jiao, Licheng; Du, Haifeng; Bo, Liefeng

2008-01-01

Abstract Nondominated Neighbor Immune Algorithm (NNIA) is proposed for multiobjective optimization by using a novel nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators, and elitism. The unique selection technique of NNIA only selects minority isolated nondominated individuals in the population. The selected individuals are then cloned proportionally to their crowding-distance values before heuristic search. By using the nondominated neighbor-based selection and proportional cloning, NNIA pays more attention to the less-crowded regions of the current trade-off front. We compare NNIA with NSGA-II, SPEA2, PESA-II, and MISA in solving five DTLZ problems, five ZDT problems, and three low-dimensional problems. The statistical analysis based on three performance metrics including the coverage of two sets, the convergence metric, and the spacing, show that the unique selection method is effective, and NNIA is an effective algorithm for solving multiobjective optimization problems. The empirical study on NNIA's scalability with respect to the number of objectives shows that the new algorithm scales well along the number of objectives.

Heuristic Inquiry: A Personal Journey of Acculturation and Identity Reconstruction

ERIC Educational Resources Information Center

Djuraskovic, Ivana; Arthur, Nancy

2010-01-01

Heuristic methodology attempts to discover the nature and meaning of phenomenon through internal self-search, exploration, and discovery. Heuristic methodology encourages the researcher to explore and pursue the creative journey that begins inside one's being and ultimately uncovers its direction and meaning through internal discovery (Douglass &…

A human-machine cooperation route planning method based on improved A* algorithm

NASA Astrophysics Data System (ADS)

Zhang, Zhengsheng; Cai, Chao

2011-12-01

To avoid the limitation of common route planning method to blindly pursue higher Machine Intelligence and autoimmunization, this paper presents a human-machine cooperation route planning method. The proposed method includes a new A* path searing strategy based on dynamic heuristic searching and a human cooperated decision strategy to prune searching area. It can overcome the shortage of A* algorithm to fall into a local long term searching. Experiments showed that this method can quickly plan a feasible route to meet the macro-policy thinking.

Regional Value Analysis at Threat Evaluation

DTIC Science & Technology

2014-06-01

targets based on information entropy and fuzzy optimization theory. in Industrial Engineering and Engineering Management (IEEM), 2011 IEEE...Assignment by Virtual Permutation and Tabu Search Heuristics. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 2010

Coordinated distribution network control of tap changer transformers, capacitors and PV inverters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ceylan, Oğuzhan; Liu, Guodong; Tomsovic, Kevin

A power distribution system operates most efficiently with voltage deviations along a feeder kept to a minimum and must ensure all voltages remain within specified limits. Recently with the increased integration of photovoltaics, the variable power output has led to increased voltage fluctuations and violation of operating limits. This study proposes an optimization model based on a recently developed heuristic search method, grey wolf optimization, to coordinate the various distribution controllers. Several different case studies on IEEE 33 and 69 bus test systems modified by including tap changing transformers, capacitors and photovoltaic solar panels are performed. Simulation results are comparedmore » to two other heuristic-based optimization methods: harmony search and differential evolution. Finally, the simulation results show the effectiveness of the method and indicate the usage of reactive power outputs of PVs facilitates better voltage magnitude profile.« less

Coordinated distribution network control of tap changer transformers, capacitors and PV inverters

DOE PAGES

Ceylan, Oğuzhan; Liu, Guodong; Tomsovic, Kevin

2017-06-08

A power distribution system operates most efficiently with voltage deviations along a feeder kept to a minimum and must ensure all voltages remain within specified limits. Recently with the increased integration of photovoltaics, the variable power output has led to increased voltage fluctuations and violation of operating limits. This study proposes an optimization model based on a recently developed heuristic search method, grey wolf optimization, to coordinate the various distribution controllers. Several different case studies on IEEE 33 and 69 bus test systems modified by including tap changing transformers, capacitors and photovoltaic solar panels are performed. Simulation results are comparedmore » to two other heuristic-based optimization methods: harmony search and differential evolution. Finally, the simulation results show the effectiveness of the method and indicate the usage of reactive power outputs of PVs facilitates better voltage magnitude profile.« less

An Improved Heuristic Method for Subgraph Isomorphism Problem

NASA Astrophysics Data System (ADS)

Xiang, Yingzhuo; Han, Jiesi; Xu, Haijiang; Guo, Xin

2017-09-01

This paper focus on the subgraph isomorphism (SI) problem. We present an improved genetic algorithm, a heuristic method to search the optimal solution. The contribution of this paper is that we design a dedicated crossover algorithm and a new fitness function to measure the evolution process. Experiments show our improved genetic algorithm performs better than other heuristic methods. For a large graph, such as a subgraph of 40 nodes, our algorithm outperforms the traditional tree search algorithms. We find that the performance of our improved genetic algorithm does not decrease as the number of nodes in prototype graphs.

Heuristics in Problem Solving: The Role of Direction in Controlling Search Space

ERIC Educational Resources Information Center

Chu, Yun; Li, Zheng; Su, Yong; Pizlo, Zygmunt

2010-01-01

Isomorphs of a puzzle called m+m resulted in faster solution times and an easily reproduced solution path in a labeled version of the problem compared to a more difficult binary version. We conjecture that performance is related to a type of heuristic called direction that not only constrains search space in the labeled version, but also…

An Empirical Comparison of Seven Iterative and Evolutionary Function Optimization Heuristics

NASA Technical Reports Server (NTRS)

Baluja, Shumeet

1995-01-01

This report is a repository of the results obtained from a large scale empirical comparison of seven iterative and evolution-based optimization heuristics. Twenty-seven static optimization problems, spanning six sets of problem classes which are commonly explored in genetic algorithm literature, are examined. The problem sets include job-shop scheduling, traveling salesman, knapsack, binpacking, neural network weight optimization, and standard numerical optimization. The search spaces in these problems range from 2368 to 22040. The results indicate that using genetic algorithms for the optimization of static functions does not yield a benefit, in terms of the final answer obtained, over simpler optimization heuristics. Descriptions of the algorithms tested and the encodings of the problems are described in detail for reproducibility.

A Geographical Heuristic Routing Protocol for VANETs

PubMed Central

Urquiza-Aguiar, Luis; Tripp-Barba, Carolina; Aguilar Igartua, Mónica

2016-01-01

Vehicular ad hoc networks (VANETs) leverage the communication system of Intelligent Transportation Systems (ITS). Recently, Delay-Tolerant Network (DTN) routing protocols have increased their popularity among the research community for being used in non-safety VANET applications and services like traffic reporting. Vehicular DTN protocols use geographical and local information to make forwarding decisions. However, current proposals only consider the selection of the best candidate based on a local-search. In this paper, we propose a generic Geographical Heuristic Routing (GHR) protocol that can be applied to any DTN geographical routing protocol that makes forwarding decisions hop by hop. GHR includes in its operation adaptations simulated annealing and Tabu-search meta-heuristics, which have largely been used to improve local-search results in discrete optimization. We include a complete performance evaluation of GHR in a multi-hop VANET simulation scenario for a reporting service. Our study analyzes all of the meaningful configurations of GHR and offers a statistical analysis of our findings by means of MANOVA tests. Our results indicate that the use of a Tabu list contributes to improving the packet delivery ratio by around 5% to 10%. Moreover, if Tabu is used, then the simulated annealing routing strategy gets a better performance than the selection of the best node used with carry and forwarding (default operation). PMID:27669254

A Geographical Heuristic Routing Protocol for VANETs.

PubMed

Urquiza-Aguiar, Luis; Tripp-Barba, Carolina; Aguilar Igartua, Mónica

2016-09-23

Vehicular ad hoc networks (VANETs) leverage the communication system of Intelligent Transportation Systems (ITS). Recently, Delay-Tolerant Network (DTN) routing protocols have increased their popularity among the research community for being used in non-safety VANET applications and services like traffic reporting. Vehicular DTN protocols use geographical and local information to make forwarding decisions. However, current proposals only consider the selection of the best candidate based on a local-search. In this paper, we propose a generic Geographical Heuristic Routing (GHR) protocol that can be applied to any DTN geographical routing protocol that makes forwarding decisions hop by hop. GHR includes in its operation adaptations simulated annealing and Tabu-search meta-heuristics, which have largely been used to improve local-search results in discrete optimization. We include a complete performance evaluation of GHR in a multi-hop VANET simulation scenario for a reporting service. Our study analyzes all of the meaningful configurations of GHR and offers a statistical analysis of our findings by means of MANOVA tests. Our results indicate that the use of a Tabu list contributes to improving the packet delivery ratio by around 5% to 10%. Moreover, if Tabu is used, then the simulated annealing routing strategy gets a better performance than the selection of the best node used with carry and forwarding (default operation).

Properties of heuristic search strategies

NASA Technical Reports Server (NTRS)

Vanderbrug, G. J.

1973-01-01

A directed graph is used to model the search space of a state space representation with single input operators, an AND/OR is used for problem reduction representations, and a theorem proving graph is used for state space representations with multiple input operators. These three graph models and heuristic strategies for searching them are surveyed. The completeness, admissibility, and optimality properties of search strategies which use the evaluation function f = (1 - omega)g = omega(h) are presented and interpreted using a representation of the search process in the plane. The use of multiple output operators to imply dependent successors, and thus obtain a formalism which includes all three types of representations, is discussed.

Knowledge Based Engineering for Spatial Database Management and Use

NASA Technical Reports Server (NTRS)

Peuquet, D. (Principal Investigator)

1984-01-01

The use of artificial intelligence techniques that are applicable to Geographic Information Systems (GIS) are examined. Questions involving the performance and modification to the database structure, the definition of spectra in quadtree structures and their use in search heuristics, extension of the knowledge base, and learning algorithm concepts are investigated.

A dynamic multiarmed bandit-gene expression programming hyper-heuristic for combinatorial optimization problems.

PubMed

Sabar, Nasser R; Ayob, Masri; Kendall, Graham; Qu, Rong

2015-02-01

Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. In this paper, we propose a new high level strategy for a hyper-heuristic framework. The proposed high-level strategy utilizes a dynamic multiarmed bandit-extreme value-based reward as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration. In addition, we propose a gene expression programming framework to automatically generate the acceptance criterion for each problem instance, instead of using human-designed criteria. Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. Compared with state-of-the-art hyper-heuristics and other bespoke methods, empirical results demonstrate that the proposed framework is able to generalize well across both domains. We obtain competitive, if not better results, when compared to the best known results obtained from other methods that have been presented in the scientific literature. We also compare our approach against the recently released hyper-heuristic competition test suite. We again demonstrate the generality of our approach when we compare against other methods that have utilized the same six benchmark datasets from this test suite.

Location-based Web Search

NASA Astrophysics Data System (ADS)

Ahlers, Dirk; Boll, Susanne

In recent years, the relation of Web information to a physical location has gained much attention. However, Web content today often carries only an implicit relation to a location. In this chapter, we present a novel location-based search engine that automatically derives spatial context from unstructured Web resources and allows for location-based search: our focused crawler applies heuristics to crawl and analyze Web pages that have a high probability of carrying a spatial relation to a certain region or place; the location extractor identifies the actual location information from the pages; our indexer assigns a geo-context to the pages and makes them available for a later spatial Web search. We illustrate the usage of our spatial Web search for location-based applications that provide information not only right-in-time but also right-on-the-spot.

Solving Energy-Aware Real-Time Tasks Scheduling Problem with Shuffled Frog Leaping Algorithm on Heterogeneous Platforms

PubMed Central

Zhang, Weizhe; Bai, Enci; He, Hui; Cheng, Albert M.K.

2015-01-01

Reducing energy consumption is becoming very important in order to keep battery life and lower overall operational costs for heterogeneous real-time multiprocessor systems. In this paper, we first formulate this as a combinatorial optimization problem. Then, a successful meta-heuristic, called Shuffled Frog Leaping Algorithm (SFLA) is proposed to reduce the energy consumption. Precocity remission and local optimal avoidance techniques are proposed to avoid the precocity and improve the solution quality. Convergence acceleration significantly reduces the search time. Experimental results show that the SFLA-based energy-aware meta-heuristic uses 30% less energy than the Ant Colony Optimization (ACO) algorithm, and 60% less energy than the Genetic Algorithm (GA) algorithm. Remarkably, the running time of the SFLA-based meta-heuristic is 20 and 200 times less than ACO and GA, respectively, for finding the optimal solution. PMID:26110406

A novel hybrid meta-heuristic technique applied to the well-known benchmark optimization problems

NASA Astrophysics Data System (ADS)

Abtahi, Amir-Reza; Bijari, Afsane

2017-03-01

In this paper, a hybrid meta-heuristic algorithm, based on imperialistic competition algorithm (ICA), harmony search (HS), and simulated annealing (SA) is presented. The body of the proposed hybrid algorithm is based on ICA. The proposed hybrid algorithm inherits the advantages of the process of harmony creation in HS algorithm to improve the exploitation phase of the ICA algorithm. In addition, the proposed hybrid algorithm uses SA to make a balance between exploration and exploitation phases. The proposed hybrid algorithm is compared with several meta-heuristic methods, including genetic algorithm (GA), HS, and ICA on several well-known benchmark instances. The comprehensive experiments and statistical analysis on standard benchmark functions certify the superiority of the proposed method over the other algorithms. The efficacy of the proposed hybrid algorithm is promising and can be used in several real-life engineering and management problems.

Augmented neural networks and problem structure-based heuristics for the bin-packing problem

NASA Astrophysics Data System (ADS)

Kasap, Nihat; Agarwal, Anurag

2012-08-01

In this article, we report on a research project where we applied augmented-neural-networks (AugNNs) approach for solving the classical bin-packing problem (BPP). AugNN is a metaheuristic that combines a priority rule heuristic with the iterative search approach of neural networks to generate good solutions fast. This is the first time this approach has been applied to the BPP. We also propose a decomposition approach for solving harder BPP, in which subproblems are solved using a combination of AugNN approach and heuristics that exploit the problem structure. We discuss the characteristics of problems on which such problem structure-based heuristics could be applied. We empirically show the effectiveness of the AugNN and the decomposition approach on many benchmark problems in the literature. For the 1210 benchmark problems tested, 917 problems were solved to optimality and the average gap between the obtained solution and the upper bound for all the problems was reduced to under 0.66% and computation time averaged below 33 s per problem. We also discuss the computational complexity of our approach.

WS-BP: An efficient wolf search based back-propagation algorithm

NASA Astrophysics Data System (ADS)

Nawi, Nazri Mohd; Rehman, M. Z.; Khan, Abdullah

2015-05-01

Wolf Search (WS) is a heuristic based optimization algorithm. Inspired by the preying and survival capabilities of the wolves, this algorithm is highly capable to search large spaces in the candidate solutions. This paper investigates the use of WS algorithm in combination with back-propagation neural network (BPNN) algorithm to overcome the local minima problem and to improve convergence in gradient descent. The performance of the proposed Wolf Search based Back-Propagation (WS-BP) algorithm is compared with Artificial Bee Colony Back-Propagation (ABC-BP), Bat Based Back-Propagation (Bat-BP), and conventional BPNN algorithms. Specifically, OR and XOR datasets are used for training the network. The simulation results show that the WS-BP algorithm effectively avoids the local minima and converge to global minima.

How the twain can meet: Prospect theory and models of heuristics in risky choice.

PubMed

Pachur, Thorsten; Suter, Renata S; Hertwig, Ralph

2017-03-01

Two influential approaches to modeling choice between risky options are algebraic models (which focus on predicting the overt decisions) and models of heuristics (which are also concerned with capturing the underlying cognitive process). Because they rest on fundamentally different assumptions and algorithms, the two approaches are usually treated as antithetical, or even incommensurable. Drawing on cumulative prospect theory (CPT; Tversky & Kahneman, 1992) as the currently most influential instance of a descriptive algebraic model, we demonstrate how the two modeling traditions can be linked. CPT's algebraic functions characterize choices in terms of psychophysical (diminishing sensitivity to probabilities and outcomes) as well as psychological (risk aversion and loss aversion) constructs. Models of heuristics characterize choices as rooted in simple information-processing principles such as lexicographic and limited search. In computer simulations, we estimated CPT's parameters for choices produced by various heuristics. The resulting CPT parameter profiles portray each of the choice-generating heuristics in psychologically meaningful ways-capturing, for instance, differences in how the heuristics process probability information. Furthermore, CPT parameters can reflect a key property of many heuristics, lexicographic search, and track the environment-dependent behavior of heuristics. Finally, we show, both in an empirical and a model recovery study, how CPT parameter profiles can be used to detect the operation of heuristics. We also address the limits of CPT's ability to capture choices produced by heuristics. Our results highlight an untapped potential of CPT as a measurement tool to characterize the information processing underlying risky choice. Copyright © 2017 Elsevier Inc. All rights reserved.

The min-conflicts heuristic: Experimental and theoretical results

NASA Technical Reports Server (NTRS)

Minton, Steven; Philips, Andrew B.; Johnston, Mark D.; Laird, Philip

1991-01-01

This paper describes a simple heuristic method for solving large-scale constraint satisfaction and scheduling problems. Given an initial assignment for the variables in a problem, the method operates by searching through the space of possible repairs. The search is guided by an ordering heuristic, the min-conflicts heuristic, that attempts to minimize the number of constraint violations after each step. We demonstrate empirically that the method performs orders of magnitude better than traditional backtracking techniques on certain standard problems. For example, the one million queens problem can be solved rapidly using our approach. We also describe practical scheduling applications where the method has been successfully applied. A theoretical analysis is presented to explain why the method works so well on certain types of problems and to predict when it is likely to be most effective.

Decentralized Bayesian search using approximate dynamic programming methods.

PubMed

Zhao, Yijia; Patek, Stephen D; Beling, Peter A

2008-08-01

We consider decentralized Bayesian search problems that involve a team of multiple autonomous agents searching for targets on a network of search points operating under the following constraints: 1) interagent communication is limited; 2) the agents do not have the opportunity to agree in advance on how to resolve equivalent but incompatible strategies; and 3) each agent lacks the ability to control or predict with certainty the actions of the other agents. We formulate the multiagent search-path-planning problem as a decentralized optimal control problem and introduce approximate dynamic heuristics that can be implemented in a decentralized fashion. After establishing some analytical properties of the heuristics, we present computational results for a search problem involving two agents on a 5 x 5 grid.

Heuristic Search for Planning with Different Forced Goal-Ordering Constraints

PubMed Central

Zhang, Weiming; Cui, Jing; Zhu, Cheng; Huang, Jincai; Liu, Zhong

2013-01-01

Planning with forced goal-ordering (FGO) constraints has been proposed many times over the years, but there are still major difficulties in realizing these FGOs in plan generation. In certain planning domains, all the FGOs exist in the initial state. No matter which approach is adopted to achieve a subgoal, all the subgoals should be achieved in a given sequence from the initial state. Otherwise, the planning may arrive at a deadlock. For some other planning domains, there is no FGO in the initial state. However, FGO may occur during the planning process if certain subgoal is achieved by an inappropriate approach. This paper contributes to illustrate that it is the excludable constraints among the goal achievement operations (GAO) of different subgoals that introduce the FGOs into the planning problem, and planning with FGO is still a challenge for the heuristic search based planners. Then, a novel multistep forward search algorithm is proposed which can solve the planning problem with different FGOs efficiently. PMID:23935443

Approximation, abstraction and decomposition in search and optimization

NASA Technical Reports Server (NTRS)

Ellman, Thomas

1992-01-01

In this paper, I discuss four different areas of my research. One portion of my research has focused on automatic synthesis of search control heuristics for constraint satisfaction problems (CSPs). I have developed techniques for automatically synthesizing two types of heuristics for CSPs: Filtering functions are used to remove portions of a search space from consideration. Another portion of my research is focused on automatic synthesis of hierarchic algorithms for solving constraint satisfaction problems (CSPs). I have developed a technique for constructing hierarchic problem solvers based on numeric interval algebra. Another portion of my research is focused on automatic decomposition of design optimization problems. We are using the design of racing yacht hulls as a testbed domain for this research. Decomposition is especially important in the design of complex physical shapes such as yacht hulls. Another portion of my research is focused on intelligent model selection in design optimization. The model selection problem results from the difficulty of using exact models to analyze the performance of candidate designs.

SPARSE: quadratic time simultaneous alignment and folding of RNAs without sequence-based heuristics

PubMed Central

Will, Sebastian; Otto, Christina; Miladi, Milad; Möhl, Mathias; Backofen, Rolf

2015-01-01

Motivation: RNA-Seq experiments have revealed a multitude of novel ncRNAs. The gold standard for their analysis based on simultaneous alignment and folding suffers from extreme time complexity of O(n6). Subsequently, numerous faster ‘Sankoff-style’ approaches have been suggested. Commonly, the performance of such methods relies on sequence-based heuristics that restrict the search space to optimal or near-optimal sequence alignments; however, the accuracy of sequence-based methods breaks down for RNAs with sequence identities below 60%. Alignment approaches like LocARNA that do not require sequence-based heuristics, have been limited to high complexity (≥ quartic time). Results: Breaking this barrier, we introduce the novel Sankoff-style algorithm ‘sparsified prediction and alignment of RNAs based on their structure ensembles (SPARSE)’, which runs in quadratic time without sequence-based heuristics. To achieve this low complexity, on par with sequence alignment algorithms, SPARSE features strong sparsification based on structural properties of the RNA ensembles. Following PMcomp, SPARSE gains further speed-up from lightweight energy computation. Although all existing lightweight Sankoff-style methods restrict Sankoff’s original model by disallowing loop deletions and insertions, SPARSE transfers the Sankoff algorithm to the lightweight energy model completely for the first time. Compared with LocARNA, SPARSE achieves similar alignment and better folding quality in significantly less time (speedup: 3.7). At similar run-time, it aligns low sequence identity instances substantially more accurate than RAF, which uses sequence-based heuristics. Availability and implementation: SPARSE is freely available at http://www.bioinf.uni-freiburg.de/Software/SPARSE. Contact: backofen@informatik.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25838465

Symbolic Heuristic Search for Factored Markov Decision Processes

NASA Technical Reports Server (NTRS)

Morris, Robert (Technical Monitor); Feng, Zheng-Zhu; Hansen, Eric A.

2003-01-01

We describe a planning algorithm that integrates two approaches to solving Markov decision processes with large state spaces. State abstraction is used to avoid evaluating states individually. Forward search from a start state, guided by an admissible heuristic, is used to avoid evaluating all states. We combine these two approaches in a novel way that exploits symbolic model-checking techniques and demonstrates their usefulness for decision-theoretic planning.

Decision theory for computing variable and value ordering decisions for scheduling problems

NASA Technical Reports Server (NTRS)

Linden, Theodore A.

1993-01-01

Heuristics that guide search are critical when solving large planning and scheduling problems, but most variable and value ordering heuristics are sensitive to only one feature of the search state. One wants to combine evidence from all features of the search state into a subjective probability that a value choice is best, but there has been no solid semantics for merging evidence when it is conceived in these terms. Instead, variable and value ordering decisions should be viewed as problems in decision theory. This led to two key insights: (1) The fundamental concept that allows heuristic evidence to be merged is the net incremental utility that will be achieved by assigning a value to a variable. Probability distributions about net incremental utility can merge evidence from the utility function, binary constraints, resource constraints, and other problem features. The subjective probability that a value is the best choice is then derived from probability distributions about net incremental utility. (2) The methods used for rumor control in Bayesian Networks are the primary way to prevent cycling in the computation of probable net incremental utility. These insights lead to semantically justifiable ways to compute heuristic variable and value ordering decisions that merge evidence from all available features of the search state.

Adaptive neuro-heuristic hybrid model for fruit peel defects detection.

PubMed

Woźniak, Marcin; Połap, Dawid

2018-02-01

Fusion of machine learning methods benefits in decision support systems. A composition of approaches gives a possibility to use the most efficient features composed into one solution. In this article we would like to present an approach to the development of adaptive method based on fusion of proposed novel neural architecture and heuristic search into one co-working solution. We propose a developed neural network architecture that adapts to processed input co-working with heuristic method used to precisely detect areas of interest. Input images are first decomposed into segments. This is to make processing easier, since in smaller images (decomposed segments) developed Adaptive Artificial Neural Network (AANN) processes less information what makes numerical calculations more precise. For each segment a descriptor vector is composed to be presented to the proposed AANN architecture. Evaluation is run adaptively, where the developed AANN adapts to inputs and their features by composed architecture. After evaluation, selected segments are forwarded to heuristic search, which detects areas of interest. As a result the system returns the image with pixels located over peel damages. Presented experimental research results on the developed solution are discussed and compared with other commonly used methods to validate the efficacy and the impact of the proposed fusion in the system structure and training process on classification results. Copyright © 2017 Elsevier Ltd. All rights reserved.

A heuristic approach to incremental and reactive scheduling

NASA Technical Reports Server (NTRS)

Odubiyi, Jide B.; Zoch, David R.

1989-01-01

An heuristic approach to incremental and reactive scheduling is described. Incremental scheduling is the process of modifying an existing schedule if the initial schedule does not meet its stated initial goals. Reactive scheduling occurs in near real-time in response to changes in available resources or the occurrence of targets of opportunity. Only minor changes are made during both incremental and reactive scheduling because a goal of re-scheduling procedures is to minimally impact the schedule. The described heuristic search techniques, which are employed by the Request Oriented Scheduling Engine (ROSE), a prototype generic scheduler, efficiently approximate the cost of reaching a goal from a given state and effective mechanisms for controlling search.

A meta-heuristic method for solving scheduling problem: crow search algorithm

NASA Astrophysics Data System (ADS)

Adhi, Antono; Santosa, Budi; Siswanto, Nurhadi

2018-04-01

Scheduling is one of the most important processes in an industry both in manufacturingand services. The scheduling process is the process of selecting resources to perform an operation on tasks. Resources can be machines, peoples, tasks, jobs or operations.. The selection of optimum sequence of jobs from a permutation is an essential issue in every research in scheduling problem. Optimum sequence becomes optimum solution to resolve scheduling problem. Scheduling problem becomes NP-hard problem since the number of job in the sequence is more than normal number can be processed by exact algorithm. In order to obtain optimum results, it needs a method with capability to solve complex scheduling problems in an acceptable time. Meta-heuristic is a method usually used to solve scheduling problem. The recently published method called Crow Search Algorithm (CSA) is adopted in this research to solve scheduling problem. CSA is an evolutionary meta-heuristic method which is based on the behavior in flocks of crow. The calculation result of CSA for solving scheduling problem is compared with other algorithms. From the comparison, it is found that CSA has better performance in term of optimum solution and time calculation than other algorithms.

A quantum heuristic algorithm for the traveling salesman problem

NASA Astrophysics Data System (ADS)

Bang, Jeongho; Ryu, Junghee; Lee, Changhyoup; Yoo, Seokwon; Lim, James; Lee, Jinhyoung

2012-12-01

We propose a quantum heuristic algorithm to solve the traveling salesman problem by generalizing the Grover search. Sufficient conditions are derived to greatly enhance the probability of finding the tours with the cheapest costs reaching almost to unity. These conditions are characterized by the statistical properties of tour costs and are shown to be automatically satisfied in the large-number limit of cities. In particular for a continuous distribution of the tours along the cost, we show that the quantum heuristic algorithm exhibits a quadratic speedup compared to its classical heuristic algorithm.

An AI-based approach to structural damage identification by modal analysis

NASA Technical Reports Server (NTRS)

Glass, B. J.; Hanagud, S.

1990-01-01

Flexible-structure damage is presently addressed by a combined model- and parameter-identification approach which employs the AI methodologies of classification, heuristic search, and object-oriented model knowledge representation. The conditions for model-space search convergence to the best model are discussed in terms of search-tree organization and initial model parameter error. In the illustrative example of a truss structure presented, the use of both model and parameter identification is shown to lead to smaller parameter corrections than would be required by parameter identification alone.

Hybrid real-code ant colony optimisation for constrained mechanical design

NASA Astrophysics Data System (ADS)

Pholdee, Nantiwat; Bureerat, Sujin

2016-01-01

This paper proposes a hybrid meta-heuristic based on integrating a local search simplex downhill (SDH) method into the search procedure of real-code ant colony optimisation (ACOR). This hybridisation leads to five hybrid algorithms where a Monte Carlo technique, a Latin hypercube sampling technique (LHS) and a translational propagation Latin hypercube design (TPLHD) algorithm are used to generate an initial population. Also, two numerical schemes for selecting an initial simplex are investigated. The original ACOR and its hybrid versions along with a variety of established meta-heuristics are implemented to solve 17 constrained test problems where a fuzzy set theory penalty function technique is used to handle design constraints. The comparative results show that the hybrid algorithms are the top performers. Using the TPLHD technique gives better results than the other sampling techniques. The hybrid optimisers are a powerful design tool for constrained mechanical design problems.

A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules.

PubMed

Nguyen, Su; Mei, Yi; Xue, Bing; Zhang, Mengjie

2018-06-04

Designing effective dispatching rules for production systems is a difficult and timeconsuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This paper develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

Path Planning For A Class Of Cutting Operations

NASA Astrophysics Data System (ADS)

Tavora, Jose

1989-03-01

Optimizing processing time in some contour-cutting operations requires solving the so-called no-load path problem. This problem is formulated and an approximate resolution method (based on heuristic search techniques) is described. Results for real-life instances (clothing layouts in the apparel industry) are presented and evaluated.

Simple heuristics and rules of thumb: where psychologists and behavioural biologists might meet.

PubMed

Hutchinson, John M C; Gigerenzer, Gerd

2005-05-31

The Centre for Adaptive Behaviour and Cognition (ABC) has hypothesised that much human decision-making can be described by simple algorithmic process models (heuristics). This paper explains this approach and relates it to research in biology on rules of thumb, which we also review. As an example of a simple heuristic, consider the lexicographic strategy of Take The Best for choosing between two alternatives: cues are searched in turn until one discriminates, then search stops and all other cues are ignored. Heuristics consist of building blocks, and building blocks exploit evolved or learned abilities such as recognition memory; it is the complexity of these abilities that allows the heuristics to be simple. Simple heuristics have an advantage in making decisions fast and with little information, and in avoiding overfitting. Furthermore, humans are observed to use simple heuristics. Simulations show that the statistical structures of different environments affect which heuristics perform better, a relationship referred to as ecological rationality. We contrast ecological rationality with the stronger claim of adaptation. Rules of thumb from biology provide clearer examples of adaptation because animals can be studied in the environments in which they evolved. The range of examples is also much more diverse. To investigate them, biologists have sometimes used similar simulation techniques to ABC, but many examples depend on empirically driven approaches. ABC's theoretical framework can be useful in connecting some of these examples, particularly the scattered literature on how information from different cues is integrated. Optimality modelling is usually used to explain less detailed aspects of behaviour but might more often be redirected to investigate rules of thumb.

Towards improving searches for optimal phylogenies.

PubMed

Ford, Eric; St John, Katherine; Wheeler, Ward C

2015-01-01

Finding the optimal evolutionary history for a set of taxa is a challenging computational problem, even when restricting possible solutions to be "tree-like" and focusing on the maximum-parsimony optimality criterion. This has led to much work on using heuristic tree searches to find approximate solutions. We present an approach for finding exact optimal solutions that employs and complements the current heuristic methods for finding optimal trees. Given a set of taxa and a set of aligned sequences of characters, there may be subsets of characters that are compatible, and for each such subset there is an associated (possibly partially resolved) phylogeny with edges corresponding to each character state change. These perfect phylogenies serve as anchor trees for our constrained search space. We show that, for sequences with compatible sites, the parsimony score of any tree [Formula: see text] is at least the parsimony score of the anchor trees plus the number of inferred changes between [Formula: see text] and the anchor trees. As the maximum-parsimony optimality score is additive, the sum of the lower bounds on compatible character partitions provides a lower bound on the complete alignment of characters. This yields a region in the space of trees within which the best tree is guaranteed to be found; limiting the search for the optimal tree to this region can significantly reduce the number of trees that must be examined in a search of the space of trees. We analyze this method empirically using four different biological data sets as well as surveying 400 data sets from the TreeBASE repository, demonstrating the effectiveness of our technique in reducing the number of steps in exact heuristic searches for trees under the maximum-parsimony optimality criterion. © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Making decisions at the end of life when caring for a person with dementia: a literature review to explore the potential use of heuristics in difficult decision-making.

PubMed

Mathew, R; Davies, N; Manthorpe, J; Iliffe, S

2016-07-19

Decision-making, when providing care and treatment for a person with dementia at the end of life, can be complex and challenging. There is a lack of guidance available to support practitioners and family carers, and even those experienced in end of life dementia care report a lack of confidence in decision-making. It is thought that the use of heuristics (rules of thumb) may aid decision-making. The aim of this study is to identify whether heuristics are used in end of life dementia care, and if so, to identify the context in which they are being used. A narrative literature review was conducted taking a systematic approach to the search strategy, using the Centre for Reviews and Dissemination guidelines. Rapid appraisal methodology was used in order to source specific and relevant literature regarding the use of heuristics in end of life dementia care. A search using terms related to dementia, palliative care and decision-making was conducted across 4 English language electronic databases (MEDLINE, EMBASE, PsycINFO and CINAHL) in 2015. The search identified 12 papers that contained an algorithm, guideline, decision tool or set of principles that we considered compatible with heuristic decision-making. The papers addressed swallowing and feeding difficulties, the treatment of pneumonia, management of pain and agitation, rationalising medication, ending life-sustaining treatment, and ensuring a good death. The use of heuristics in palliative or end of life dementia care is not described in the research literature. However, this review identified important decision-making principles, which are largely a reflection of expert opinion. These principles may have the potential to be developed into simple heuristics that could be used in practice. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Making decisions at the end of life when caring for a person with dementia: a literature review to explore the potential use of heuristics in difficult decision-making

PubMed Central

Mathew, R; Davies, N; Manthorpe, J; Iliffe, S

2016-01-01

Objective Decision-making, when providing care and treatment for a person with dementia at the end of life, can be complex and challenging. There is a lack of guidance available to support practitioners and family carers, and even those experienced in end of life dementia care report a lack of confidence in decision-making. It is thought that the use of heuristics (rules of thumb) may aid decision-making. The aim of this study is to identify whether heuristics are used in end of life dementia care, and if so, to identify the context in which they are being used. Design A narrative literature review was conducted taking a systematic approach to the search strategy, using the Centre for Reviews and Dissemination guidelines. Rapid appraisal methodology was used in order to source specific and relevant literature regarding the use of heuristics in end of life dementia care. Data sources A search using terms related to dementia, palliative care and decision-making was conducted across 4 English language electronic databases (MEDLINE, EMBASE, PsycINFO and CINAHL) in 2015. Results The search identified 12 papers that contained an algorithm, guideline, decision tool or set of principles that we considered compatible with heuristic decision-making. The papers addressed swallowing and feeding difficulties, the treatment of pneumonia, management of pain and agitation, rationalising medication, ending life-sustaining treatment, and ensuring a good death. Conclusions The use of heuristics in palliative or end of life dementia care is not described in the research literature. However, this review identified important decision-making principles, which are largely a reflection of expert opinion. These principles may have the potential to be developed into simple heuristics that could be used in practice. PMID:27436665

SARDA Surface Schedulers

NASA Technical Reports Server (NTRS)

Malik, Waqar

2016-01-01

Provide an overview of algorithms used in SARDA (Spot and Runway Departure Advisor) HITL (Human-in-the-Loop) simulation for Dallas Fort-Worth International Airport and Charlotte Douglas International airport. Outline a multi-objective dynamic programming (DP) based algorithm that finds the exact solution to the single runway scheduling (SRS) problem, and discuss heuristics to restrict the search space for the DP based algorithm and provide improvements.

Micro-seismic waveform matching inversion based on gravitational search algorithm and parallel computation

NASA Astrophysics Data System (ADS)

Jiang, Y.; Xing, H. L.

2016-12-01

Micro-seismic events induced by water injection, mining activity or oil/gas extraction are quite informative, the interpretation of which can be applied for the reconstruction of underground stress and monitoring of hydraulic fracturing progress in oil/gas reservoirs. The source characterises and locations are crucial parameters that required for these purposes, which can be obtained through the waveform matching inversion (WMI) method. Therefore it is imperative to develop a WMI algorithm with high accuracy and convergence speed. Heuristic algorithm, as a category of nonlinear method, possesses a very high convergence speed and good capacity to overcome local minimal values, and has been well applied for many areas (e.g. image processing, artificial intelligence). However, its effectiveness for micro-seismic WMI is still poorly investigated; very few literatures exits that addressing this subject. In this research an advanced heuristic algorithm, gravitational search algorithm (GSA) , is proposed to estimate the focal mechanism (angle of strike, dip and rake) and source locations in three dimension. Unlike traditional inversion methods, the heuristic algorithm inversion does not require the approximation of green function. The method directly interacts with a CPU parallelized finite difference forward modelling engine, and updating the model parameters under GSA criterions. The effectiveness of this method is tested with synthetic data form a multi-layered elastic model; the results indicate GSA can be well applied on WMI and has its unique advantages. Keywords: Micro-seismicity, Waveform matching inversion, gravitational search algorithm, parallel computation

An almost-parameter-free harmony search algorithm for groundwater pollution source identification.

PubMed

Jiang, Simin; Zhang, Yali; Wang, Pei; Zheng, Maohui

2013-01-01

The spatiotemporal characterization of unknown sources of groundwater pollution is frequently encountered in environmental problems. This study adopts a simulation-optimization approach that combines a contaminant transport simulation model with a heuristic harmony search algorithm to identify unknown pollution sources. In the proposed methodology, an almost-parameter-free harmony search algorithm is developed. The performance of this methodology is evaluated on an illustrative groundwater pollution source identification problem, and the identified results indicate that the proposed almost-parameter-free harmony search algorithm-based optimization model can give satisfactory estimations, even when the irregular geometry, erroneous monitoring data, and prior information shortage of potential locations are considered.

A new distributed systems scheduling algorithm: a swarm intelligence approach

NASA Astrophysics Data System (ADS)

Haghi Kashani, Mostafa; Sarvizadeh, Raheleh; Jameii, Mahdi

2011-12-01

The scheduling problem in distributed systems is known as an NP-complete problem, and methods based on heuristic or metaheuristic search have been proposed to obtain optimal and suboptimal solutions. The task scheduling is a key factor for distributed systems to gain better performance. In this paper, an efficient method based on memetic algorithm is developed to solve the problem of distributed systems scheduling. With regard to load balancing efficiently, Artificial Bee Colony (ABC) has been applied as local search in the proposed memetic algorithm. The proposed method has been compared to existing memetic-Based approach in which Learning Automata method has been used as local search. The results demonstrated that the proposed method outperform the above mentioned method in terms of communication cost.

A Multi-Start Evolutionary Local Search for the Two-Echelon Location Routing Problem

NASA Astrophysics Data System (ADS)

Nguyen, Viet-Phuong; Prins, Christian; Prodhon, Caroline

This paper presents a new hybrid metaheuristic between a greedy randomized adaptive search procedure (GRASP) and an evolutionary/iterated local search (ELS/ILS), using Tabu list to solve the two-echelon location routing problem (LRP-2E). The GRASP uses in turn three constructive heuristics followed by local search to generate the initial solutions. From a solution of GRASP, an intensification strategy is carried out by a dynamic alternation between ELS and ILS. In this phase, each child is obtained by mutation and evaluated through a splitting procedure of giant tour followed by a local search. The tabu list, defined by two characteristics of solution (total cost and number of trips), is used to avoid searching a space already explored. The results show that our metaheuristic clearly outperforms all previously published methods on LRP-2E benchmark instances. Furthermore, it is competitive with the best meta-heuristic published for the single-echelon LRP.

The Robust Beauty of Ordinary Information

ERIC Educational Resources Information Center

Katsikopoulos, Konstantinos V.; Schooler, Lael J.; Hertwig, Ralph

2010-01-01

Heuristics embodying limited information search and noncompensatory processing of information can yield robust performance relative to computationally more complex models. One criticism raised against heuristics is the argument that complexity is hidden in the calculation of the cue order used to make predictions. We discuss ways to order cues…

Fast or Frugal, but Not Both: Decision Heuristics Under Time Pressure

PubMed Central

2017-01-01

Heuristics are simple, yet effective, strategies that people use to make decisions. Because heuristics do not require all available information, they are thought to be easy to implement and to not tax limited cognitive resources, which has led heuristics to be characterized as fast-and-frugal. We question this monolithic conception of heuristics by contrasting the cognitive demands of two popular heuristics, Tallying and Take-the-Best. We contend that heuristics that are frugal in terms of information usage may not always be fast because of the attentional control required to implement this focus in certain contexts. In support of this hypothesis, we find that Take-the-Best, while being more frugal in terms of information usage, is slower to implement and fares worse under time pressure manipulations than Tallying. This effect is then reversed when search costs for Take-the-Best are reduced by changing the format of the stimuli. These findings suggest that heuristics are heterogeneous and should be unpacked according to their cognitive demands to determine the circumstances a heuristic best applies. PMID:28557503

Fast or frugal, but not both: Decision heuristics under time pressure.

PubMed

Bobadilla-Suarez, Sebastian; Love, Bradley C

2018-01-01

Heuristics are simple, yet effective, strategies that people use to make decisions. Because heuristics do not require all available information, they are thought to be easy to implement and to not tax limited cognitive resources, which has led heuristics to be characterized as fast-and-frugal. We question this monolithic conception of heuristics by contrasting the cognitive demands of two popular heuristics, Tallying and Take-the-Best. We contend that heuristics that are frugal in terms of information usage may not always be fast because of the attentional control required to implement this focus in certain contexts. In support of this hypothesis, we find that Take-the-Best, while being more frugal in terms of information usage, is slower to implement and fares worse under time pressure manipulations than Tallying. This effect is then reversed when search costs for Take-the-Best are reduced by changing the format of the stimuli. These findings suggest that heuristics are heterogeneous and should be unpacked according to their cognitive demands to determine the circumstances a heuristic best applies. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

An iterative bidirectional heuristic placement algorithm for solving the two-dimensional knapsack packing problem

NASA Astrophysics Data System (ADS)

Shiangjen, Kanokwatt; Chaijaruwanich, Jeerayut; Srisujjalertwaja, Wijak; Unachak, Prakarn; Somhom, Samerkae

2018-02-01

This article presents an efficient heuristic placement algorithm, namely, a bidirectional heuristic placement, for solving the two-dimensional rectangular knapsack packing problem. The heuristic demonstrates ways to maximize space utilization by fitting the appropriate rectangle from both sides of the wall of the current residual space layer by layer. The iterative local search along with a shift strategy is developed and applied to the heuristic to balance the exploitation and exploration tasks in the solution space without the tuning of any parameters. The experimental results on many scales of packing problems show that this approach can produce high-quality solutions for most of the benchmark datasets, especially for large-scale problems, within a reasonable duration of computational time.

Heuristics for Scientific Experimentation: A Developmental Study.

ERIC Educational Resources Information Center

Klahr, David; And Others

1993-01-01

Studied developmental differences in the search constraint heuristics used in scientific reasoning using 12 undergraduates, 20 community college students, 17 fifth to seventh graders (grade 6), and 15 third graders taught to use a programmable robot. Adults use domain-general skills that go beyond the logic of confirmation and disconfirmation.…

Insight into the ten-penny problem: guiding search by constraints and maximization.

PubMed

Öllinger, Michael; Fedor, Anna; Brodt, Svenja; Szathmáry, Eörs

2017-09-01

For a long time, insight problem solving has been either understood as nothing special or as a particular class of problem solving. The first view implicates the necessity to find efficient heuristics that restrict the search space, the second, the necessity to overcome self-imposed constraints. Recently, promising hybrid cognitive models attempt to merge both approaches. In this vein, we were interested in the interplay of constraints and heuristic search, when problem solvers were asked to solve a difficult multi-step problem, the ten-penny problem. In three experimental groups and one control group (N = 4 × 30) we aimed at revealing, what constraints drive problem difficulty in this problem, and how relaxing constraints, and providing an efficient search criterion facilitates the solution. We also investigated how the search behavior of successful problem solvers and non-solvers differ. We found that relaxing constraints was necessary but not sufficient to solve the problem. Without efficient heuristics that facilitate the restriction of the search space, and testing the progress of the problem solving process, the relaxation of constraints was not effective. Relaxing constraints and applying the search criterion are both necessary to effectively increase solution rates. We also found that successful solvers showed promising moves earlier and had a higher maximization and variation rate across solution attempts. We propose that this finding sheds light on how different strategies contribute to solving difficult problems. Finally, we speculate about the implications of our findings for insight problem solving.

[Ambivalence--a key concept in gerontology? Elements of heuristics exemplified by identity formation in old age].

PubMed

Lüscher, Kurt; Haller, Miriam

2016-01-01

Ambivalence is a widely used concept in gerontology, mostly used in the common sense meaning. We propose that an elaborated notion based on the historical and systematic analysis, reveals important theoretical, methodological and practical potentials of the idea of ambivalence for the study of aging. We exemplify this view by proposing a heuristic perspective for the analysis of processes to constitute and reconstitute identities in old age using a model based on a multidimensional understanding of ambivalence. Ambivalence is defined as referring to the experiences of vacillating between polar contradictions of feeling, thinking, wanting and social structures in the search for the sense and meaning of social relationships, facts and texts, which are important for unfolding and altering facets of the self and agency.

On the asymptotic optimality and improved strategies of SPTB heuristic for open-shop scheduling problem

NASA Astrophysics Data System (ADS)

Bai, Danyu; Zhang, Zhihai

2014-08-01

This article investigates the open-shop scheduling problem with the optimal criterion of minimising the sum of quadratic completion times. For this NP-hard problem, the asymptotic optimality of the shortest processing time block (SPTB) heuristic is proven in the sense of limit. Moreover, three different improvements, namely, the job-insert scheme, tabu search and genetic algorithm, are introduced to enhance the quality of the original solution generated by the SPTB heuristic. At the end of the article, a series of numerical experiments demonstrate the convergence of the heuristic, the performance of the improvements and the effectiveness of the quadratic objective.

Escalated convergent artificial bee colony

NASA Astrophysics Data System (ADS)

Jadon, Shimpi Singh; Bansal, Jagdish Chand; Tiwari, Ritu

2016-03-01

Artificial bee colony (ABC) optimisation algorithm is a recent, fast and easy-to-implement population-based meta heuristic for optimisation. ABC has been proved a rival algorithm with some popular swarm intelligence-based algorithms such as particle swarm optimisation, firefly algorithm and ant colony optimisation. The solution search equation of ABC is influenced by a random quantity which helps its search process in exploration at the cost of exploitation. In order to find a fast convergent behaviour of ABC while exploitation capability is maintained, in this paper basic ABC is modified in two ways. First, to improve exploitation capability, two local search strategies, namely classical unidimensional local search and levy flight random walk-based local search are incorporated with ABC. Furthermore, a new solution search strategy, namely stochastic diffusion scout search is proposed and incorporated into the scout bee phase to provide more chance to abandon solution to improve itself. Efficiency of the proposed algorithm is tested on 20 benchmark test functions of different complexities and characteristics. Results are very promising and they prove it to be a competitive algorithm in the field of swarm intelligence-based algorithms.

Search asymmetries: parallel processing of uncertain sensory information.

PubMed

Vincent, Benjamin T

2011-08-01

What is the mechanism underlying search phenomena such as search asymmetry? Two-stage models such as Feature Integration Theory and Guided Search propose parallel pre-attentive processing followed by serial post-attentive processing. They claim search asymmetry effects are indicative of finding pairs of features, one processed in parallel, the other in serial. An alternative proposal is that a 1-stage parallel process is responsible, and search asymmetries occur when one stimulus has greater internal uncertainty associated with it than another. While the latter account is simpler, only a few studies have set out to empirically test its quantitative predictions, and many researchers still subscribe to the 2-stage account. This paper examines three separate parallel models (Bayesian optimal observer, max rule, and a heuristic decision rule). All three parallel models can account for search asymmetry effects and I conclude that either people can optimally utilise the uncertain sensory data available to them, or are able to select heuristic decision rules which approximate optimal performance. Copyright © 2011 Elsevier Ltd. All rights reserved.

Fluency heuristic: a model of how the mind exploits a by-product of information retrieval.

PubMed

Hertwig, Ralph; Herzog, Stefan M; Schooler, Lael J; Reimer, Torsten

2008-09-01

Boundedly rational heuristics for inference can be surprisingly accurate and frugal for several reasons. They can exploit environmental structures, co-opt complex capacities, and elude effortful search by exploiting information that automatically arrives on the mental stage. The fluency heuristic is a prime example of a heuristic that makes the most of an automatic by-product of retrieval from memory, namely, retrieval fluency. In 4 experiments, the authors show that retrieval fluency can be a proxy for real-world quantities, that people can discriminate between two objects' retrieval fluencies, and that people's inferences are in line with the fluency heuristic (in particular fast inferences) and with experimentally manipulated fluency. The authors conclude that the fluency heuristic may be one tool in the mind's repertoire of strategies that artfully probes memory for encapsulated frequency information that can veridically reflect statistical regularities in the world. (c) 2008 APA, all rights reserved.

Self-Adaptive Stepsize Search Applied to Optimal Structural Design

NASA Astrophysics Data System (ADS)

Nolle, L.; Bland, J. A.

Structural engineering often involves the design of space frames that are required to resist predefined external forces without exhibiting plastic deformation. The weight of the structure and hence the weight of its constituent members has to be as low as possible for economical reasons without violating any of the load constraints. Design spaces are usually vast and the computational costs for analyzing a single design are usually high. Therefore, not every possible design can be evaluated for real-world problems. In this work, a standard structural design problem, the 25-bar problem, has been solved using self-adaptive stepsize search (SASS), a relatively new search heuristic. This algorithm has only one control parameter and therefore overcomes the drawback of modern search heuristics, i.e. the need to first find a set of optimum control parameter settings for the problem at hand. In this work, SASS outperforms simulated-annealing, genetic algorithms, tabu search and ant colony optimization.

Characterizing the phylogenetic tree-search problem.

PubMed

Money, Daniel; Whelan, Simon

2012-03-01

Phylogenetic trees are important in many areas of biological research, ranging from systematic studies to the methods used for genome annotation. Finding the best scoring tree under any optimality criterion is an NP-hard problem, which necessitates the use of heuristics for tree-search. Although tree-search plays a major role in obtaining a tree estimate, there remains a limited understanding of its characteristics and how the elements of the statistical inferential procedure interact with the algorithms used. This study begins to answer some of these questions through a detailed examination of maximum likelihood tree-search on a wide range of real genome-scale data sets. We examine all 10,395 trees for each of the 106 genes of an eight-taxa yeast phylogenomic data set, then apply different tree-search algorithms to investigate their performance. We extend our findings by examining two larger genome-scale data sets and a large disparate data set that has been previously used to benchmark the performance of tree-search programs. We identify several broad trends occurring during tree-search that provide an insight into the performance of heuristics and may, in the future, aid their development. These trends include a tendency for the true maximum likelihood (best) tree to also be the shortest tree in terms of branch lengths, a weak tendency for tree-search to recover the best tree, and a tendency for tree-search to encounter fewer local optima in genes that have a high information content. When examining current heuristics for tree-search, we find that nearest-neighbor-interchange performs poorly, and frequently finds trees that are significantly different from the best tree. In contrast, subtree-pruning-and-regrafting tends to perform well, nearly always finding trees that are not significantly different to the best tree. Finally, we demonstrate that the precise implementation of a tree-search strategy, including when and where parameters are optimized, can change the character of tree-search, and that good strategies for tree-search may combine existing tree-search programs.

Object tracking based on harmony search: comparative study

NASA Astrophysics Data System (ADS)

Gao, Ming-Liang; He, Xiao-Hai; Luo, Dai-Sheng; Yu, Yan-Mei

2012-10-01

Visual tracking can be treated as an optimization problem. A new meta-heuristic optimal algorithm, Harmony Search (HS), was first applied to perform visual tracking by Fourie et al. As the authors point out, many subjects are still required in ongoing research. Our work is a continuation of Fourie's study, with four prominent improved variations of HS, namely Improved Harmony Search (IHS), Global-best Harmony Search (GHS), Self-adaptive Harmony Search (SHS) and Differential Harmony Search (DHS) adopted into the tracking system. Their performances are tested and analyzed on multiple challenging video sequences. Experimental results show that IHS is best, with DHS ranking second among the four improved trackers when the iteration number is small. However, the differences between all four reduced gradually, along with the increasing number of iterations.

Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems.

PubMed

Moreno-Scott, Jorge Humberto; Ortiz-Bayliss, José Carlos; Terashima-Marín, Hugo; Conant-Pablos, Santiago Enrique

2016-01-01

Constraint satisfaction problems are of special interest for the artificial intelligence and operations research community due to their many applications. Although heuristics involved in solving these problems have largely been studied in the past, little is known about the relation between instances and the respective performance of the heuristics used to solve them. This paper focuses on both the exploration of the instance space to identify relations between instances and good performing heuristics and how to use such relations to improve the search. Firstly, the document describes a methodology to explore the instance space of constraint satisfaction problems and evaluate the corresponding performance of six variable ordering heuristics for such instances in order to find regions on the instance space where some heuristics outperform the others. Analyzing such regions favors the understanding of how these heuristics work and contribute to their improvement. Secondly, we use the information gathered from the first stage to predict the most suitable heuristic to use according to the features of the instance currently being solved. This approach proved to be competitive when compared against the heuristics applied in isolation on both randomly generated and structured instances of constraint satisfaction problems.

Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems

PubMed Central

Moreno-Scott, Jorge Humberto; Ortiz-Bayliss, José Carlos; Terashima-Marín, Hugo; Conant-Pablos, Santiago Enrique

2016-01-01

Constraint satisfaction problems are of special interest for the artificial intelligence and operations research community due to their many applications. Although heuristics involved in solving these problems have largely been studied in the past, little is known about the relation between instances and the respective performance of the heuristics used to solve them. This paper focuses on both the exploration of the instance space to identify relations between instances and good performing heuristics and how to use such relations to improve the search. Firstly, the document describes a methodology to explore the instance space of constraint satisfaction problems and evaluate the corresponding performance of six variable ordering heuristics for such instances in order to find regions on the instance space where some heuristics outperform the others. Analyzing such regions favors the understanding of how these heuristics work and contribute to their improvement. Secondly, we use the information gathered from the first stage to predict the most suitable heuristic to use according to the features of the instance currently being solved. This approach proved to be competitive when compared against the heuristics applied in isolation on both randomly generated and structured instances of constraint satisfaction problems. PMID:26949383

A random-key encoded harmony search approach for energy-efficient production scheduling with shared resources

NASA Astrophysics Data System (ADS)

Garcia-Santiago, C. A.; Del Ser, J.; Upton, C.; Quilligan, F.; Gil-Lopez, S.; Salcedo-Sanz, S.

2015-11-01

When seeking near-optimal solutions for complex scheduling problems, meta-heuristics demonstrate good performance with affordable computational effort. This has resulted in a gravitation towards these approaches when researching industrial use-cases such as energy-efficient production planning. However, much of the previous research makes assumptions about softer constraints that affect planning strategies and about how human planners interact with the algorithm in a live production environment. This article describes a job-shop problem that focuses on minimizing energy consumption across a production facility of shared resources. The application scenario is based on real facilities made available by the Irish Center for Manufacturing Research. The formulated problem is tackled via harmony search heuristics with random keys encoding. Simulation results are compared to a genetic algorithm, a simulated annealing approach and a first-come-first-served scheduling. The superior performance obtained by the proposed scheduler paves the way towards its practical implementation over industrial production chains.

Implementation of an effective hybrid GA for large-scale traveling salesman problems.

PubMed

Nguyen, Hung Dinh; Yoshihara, Ikuo; Yamamori, Kunihito; Yasunaga, Moritoshi

2007-02-01

This correspondence describes a hybrid genetic algorithm (GA) to find high-quality solutions for the traveling salesman problem (TSP). The proposed method is based on a parallel implementation of a multipopulation steady-state GA involving local search heuristics. It uses a variant of the maximal preservative crossover and the double-bridge move mutation. An effective implementation of the Lin-Kernighan heuristic (LK) is incorporated into the method to compensate for the GA's lack of local search ability. The method is validated by comparing it with the LK-Helsgaun method (LKH), which is one of the most effective methods for the TSP. Experimental results with benchmarks having up to 316228 cities show that the proposed method works more effectively and efficiently than LKH when solving large-scale problems. Finally, the method is used together with the implementation of the iterated LK to find a new best tour (as of June 2, 2003) for a 1904711-city TSP challenge.

Investigation into the efficiency of different bionic algorithm combinations for a COBRA meta-heuristic

NASA Astrophysics Data System (ADS)

Akhmedova, Sh; Semenkin, E.

2017-02-01

Previously, a meta-heuristic approach, called Co-Operation of Biology-Related Algorithms or COBRA, for solving real-parameter optimization problems was introduced and described. COBRA’s basic idea consists of a cooperative work of five well-known bionic algorithms such as Particle Swarm Optimization, the Wolf Pack Search, the Firefly Algorithm, the Cuckoo Search Algorithm and the Bat Algorithm, which were chosen due to the similarity of their schemes. The performance of this meta-heuristic was evaluated on a set of test functions and its workability was demonstrated. Thus it was established that the idea of the algorithms’ cooperative work is useful. However, it is unclear which bionic algorithms should be included in this cooperation and how many of them. Therefore, the five above-listed algorithms and additionally the Fish School Search algorithm were used for the development of five different modifications of COBRA by varying the number of component-algorithms. These modifications were tested on the same set of functions and the best of them was found. Ways of further improving the COBRA algorithm are then discussed.

A linguistic geometry for space applications

NASA Technical Reports Server (NTRS)

Stilman, Boris

1994-01-01

We develop a formal theory, the so-called Linguistic Geometry, in order to discover the inner properties of human expert heuristics, which were successful in a certain class of complex control systems, and apply them to different systems. This research relies on the formalization of search heuristics of high-skilled human experts which allow for the decomposition of complex system into the hierarchy of subsystems, and thus solve intractable problems reducing the search. The hierarchy of subsystems is represented as a hierarchy of formal attribute languages. This paper includes a formal survey of the Linguistic Geometry, and new example of a solution of optimization problem for the space robotic vehicles. This example includes actual generation of the hierarchy of languages, some details of trajectory generation and demonstrates the drastic reduction of search in comparison with conventional search algorithms.

Prediction-based dynamic load-sharing heuristics

NASA Technical Reports Server (NTRS)

Goswami, Kumar K.; Devarakonda, Murthy; Iyer, Ravishankar K.

1993-01-01

The authors present dynamic load-sharing heuristics that use predicted resource requirements of processes to manage workloads in a distributed system. A previously developed statistical pattern-recognition method is employed for resource prediction. While nonprediction-based heuristics depend on a rapidly changing system status, the new heuristics depend on slowly changing program resource usage patterns. Furthermore, prediction-based heuristics can be more effective since they use future requirements rather than just the current system state. Four prediction-based heuristics, two centralized and two distributed, are presented. Using trace driven simulations, they are compared against random scheduling and two effective nonprediction based heuristics. Results show that the prediction-based centralized heuristics achieve up to 30 percent better response times than the nonprediction centralized heuristic, and that the prediction-based distributed heuristics achieve up to 50 percent improvements relative to their nonprediction counterpart.

Bounding the Resource Availability of Partially Ordered Events with Constant Resource Impact

NASA Technical Reports Server (NTRS)

Frank, Jeremy

2004-01-01

We compare existing techniques to bound the resource availability of partially ordered events. We first show that, contrary to intuition, two existing techniques, one due to Laborie and one due to Muscettola, are not strictly comparable in terms of the size of the search trees generated under chronological search with a fixed heuristic. We describe a generalization of these techniques called the Flow Balance Constraint to tightly bound the amount of available resource for a set of partially ordered events with piecewise constant resource impact We prove that the new technique generates smaller proof trees under chronological search with a fixed heuristic, at little increase in computational expense. We then show how to construct tighter resource bounds but at increased computational cost.

Parallel Harmony Search Based Distributed Energy Resource Optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ceylan, Oguzhan; Liu, Guodong; Tomsovic, Kevin

2015-01-01

This paper presents a harmony search based parallel optimization algorithm to minimize voltage deviations in three phase unbalanced electrical distribution systems and to maximize active power outputs of distributed energy resources (DR). The main contribution is to reduce the adverse impacts on voltage profile during a day as photovoltaics (PVs) output or electrical vehicles (EVs) charging changes throughout a day. The IEEE 123- bus distribution test system is modified by adding DRs and EVs under different load profiles. The simulation results show that by using parallel computing techniques, heuristic methods may be used as an alternative optimization tool in electricalmore » power distribution systems operation.« less

Automated sequence-specific protein NMR assignment using the memetic algorithm MATCH.

PubMed

Volk, Jochen; Herrmann, Torsten; Wüthrich, Kurt

2008-07-01

MATCH (Memetic Algorithm and Combinatorial Optimization Heuristics) is a new memetic algorithm for automated sequence-specific polypeptide backbone NMR assignment of proteins. MATCH employs local optimization for tracing partial sequence-specific assignments within a global, population-based search environment, where the simultaneous application of local and global optimization heuristics guarantees high efficiency and robustness. MATCH thus makes combined use of the two predominant concepts in use for automated NMR assignment of proteins. Dynamic transition and inherent mutation are new techniques that enable automatic adaptation to variable quality of the experimental input data. The concept of dynamic transition is incorporated in all major building blocks of the algorithm, where it enables switching between local and global optimization heuristics at any time during the assignment process. Inherent mutation restricts the intrinsically required randomness of the evolutionary algorithm to those regions of the conformation space that are compatible with the experimental input data. Using intact and artificially deteriorated APSY-NMR input data of proteins, MATCH performed sequence-specific resonance assignment with high efficiency and robustness.

Model Specification Searches Using Ant Colony Optimization Algorithms

ERIC Educational Resources Information Center

Marcoulides, George A.; Drezner, Zvi

2003-01-01

Ant colony optimization is a recently proposed heuristic procedure inspired by the behavior of real ants. This article applies the procedure to model specification searches in structural equation modeling and reports the results. The results demonstrate the capabilities of ant colony optimization algorithms for conducting automated searches.

Accelerated Profile HMM Searches

PubMed Central

Eddy, Sean R.

2011-01-01

Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the “multiple segment Viterbi” (MSV) algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call “sparse rescaling”. These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches. PMID:22039361

Balancing exploration and exploitation in population-based sampling improves fragment-based de novo protein structure prediction.

PubMed

Simoncini, David; Schiex, Thomas; Zhang, Kam Y J

2017-05-01

Conformational search space exploration remains a major bottleneck for protein structure prediction methods. Population-based meta-heuristics typically enable the possibility to control the search dynamics and to tune the balance between local energy minimization and search space exploration. EdaFold is a fragment-based approach that can guide search by periodically updating the probability distribution over the fragment libraries used during model assembly. We implement the EdaFold algorithm as a Rosetta protocol and provide two different probability update policies: a cluster-based variation (EdaRose c ) and an energy-based one (EdaRose en ). We analyze the search dynamics of our new Rosetta protocols and show that EdaRose c is able to provide predictions with lower C αRMSD to the native structure than EdaRose en and Rosetta AbInitio Relax protocol. Our software is freely available as a C++ patch for the Rosetta suite and can be downloaded from http://www.riken.jp/zhangiru/software/. Our protocols can easily be extended in order to create alternative probability update policies and generate new search dynamics. Proteins 2017; 85:852-858. © 2016 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

A Tabu-Search Heuristic for Deterministic Two-Mode Blockmodeling of Binary Network Matrices

ERIC Educational Resources Information Center

Brusco, Michael; Steinley, Douglas

2011-01-01

Two-mode binary data matrices arise in a variety of social network contexts, such as the attendance or non-attendance of individuals at events, the participation or lack of participation of groups in projects, and the votes of judges on cases. A popular method for analyzing such data is two-mode blockmodeling based on structural equivalence, where…

Clustering Qualitative Data Based on Binary Equivalence Relations: Neighborhood Search Heuristics for the Clique Partitioning Problem

ERIC Educational Resources Information Center

Brusco, Michael J.; Kohn, Hans-Friedrich

2009-01-01

The clique partitioning problem (CPP) requires the establishment of an equivalence relation for the vertices of a graph such that the sum of the edge costs associated with the relation is minimized. The CPP has important applications for the social sciences because it provides a framework for clustering objects measured on a collection of nominal…

Ad-Hoc Queries over Document Collections - A Case Study

NASA Astrophysics Data System (ADS)

Löser, Alexander; Lutter, Steffen; Düssel, Patrick; Markl, Volker

We discuss the novel problem of supporting analytical business intelligence queries over web-based textual content, e.g., BI-style reports based on 100.000's of documents from an ad-hoc web search result. Neither conventional search engines nor conventional Business Intelligence and ETL tools address this problem, which lies at the intersection of their capabilities. "Google Squared" or our system GOOLAP.info, are examples of these kinds of systems. They execute information extraction methods over one or several document collections at query time and integrate extracted records into a common view or tabular structure. Frequent extraction and object resolution failures cause incomplete records which could not be joined into a record answering the query. Our focus is the identification of join-reordering heuristics maximizing the size of complete records answering a structured query. With respect to given costs for document extraction we propose two novel join-operations: The multi-way CJ-operator joins records from multiple relationships extracted from a single document. The two-way join-operator DJ ensures data density by removing incomplete records from results. In a preliminary case study we observe that our join-reordering heuristics positively impact result size, record density and lower execution costs.

A hybrid, auto-adaptive and rule-based multi-agent approach using evolutionary algorithms for improved searching

NASA Astrophysics Data System (ADS)

Izquierdo, Joaquín; Montalvo, Idel; Campbell, Enrique; Pérez-García, Rafael

2016-08-01

Selecting the most appropriate heuristic for solving a specific problem is not easy, for many reasons. This article focuses on one of these reasons: traditionally, the solution search process has operated in a given manner regardless of the specific problem being solved, and the process has been the same regardless of the size, complexity and domain of the problem. To cope with this situation, search processes should mould the search into areas of the search space that are meaningful for the problem. This article builds on previous work in the development of a multi-agent paradigm using techniques derived from knowledge discovery (data-mining techniques) on databases of so-far visited solutions. The aim is to improve the search mechanisms, increase computational efficiency and use rules to enrich the formulation of optimization problems, while reducing the search space and catering to realistic problems.

PISA: Federated Search in P2P Networks with Uncooperative Peers

NASA Astrophysics Data System (ADS)

Ren, Zujie; Shou, Lidan; Chen, Gang; Chen, Chun; Bei, Yijun

Recently, federated search in P2P networks has received much attention. Most of the previous work assumed a cooperative environment where each peer can actively participate in information publishing and distributed document indexing. However, little work has addressed the problem of incorporating uncooperative peers, which do not publish their own corpus statistics, into a network. This paper presents a P2P-based federated search framework called PISA which incorporates uncooperative peers as well as the normal ones. In order to address the indexing needs for uncooperative peers, we propose a novel heuristic query-based sampling approach which can obtain high-quality resource descriptions from uncooperative peers at relatively low communication cost. We also propose an effective method called RISE to merge the results returned by uncooperative peers. Our experimental results indicate that PISA can provide quality search results, while utilizing the uncooperative peers at a low cost.

Memory-Based Simple Heuristics as Attribute Substitution: Competitive Tests of Binary Choice Inference Models.

PubMed

Honda, Hidehito; Matsuka, Toshihiko; Ueda, Kazuhiro

2017-05-01

Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in terms of attribute substitution in heuristic use (Kahneman & Frederick, 2005). In this framework, it is predicted that people will rely on heuristic or knowledge-based inference depending on the subjective difficulty of the inference task. We conducted competitive tests of binary choice inference models representing simple heuristics (fluency and familiarity heuristics) and knowledge-based inference models. We found that a simple heuristic model (especially a familiarity heuristic model) explained inference patterns for subjectively difficult inference tasks, and that a knowledge-based inference model explained subjectively easy inference tasks. These results were consistent with the predictions of the attribute substitution framework. Issues on usage of simple heuristics and psychological processes are discussed. Copyright © 2016 Cognitive Science Society, Inc.

A bi-objective model for robust yard allocation scheduling for outbound containers

NASA Astrophysics Data System (ADS)

Liu, Changchun; Zhang, Canrong; Zheng, Li

2017-01-01

This article examines the yard allocation problem for outbound containers, with consideration of uncertainty factors, mainly including the arrival and operation time of calling vessels. Based on the time buffer inserting method, a bi-objective model is constructed to minimize the total operational cost and to maximize the robustness of fighting against the uncertainty. Due to the NP-hardness of the constructed model, a two-stage heuristic is developed to solve the problem. In the first stage, initial solutions are obtained by a greedy algorithm that looks n-steps ahead with the uncertainty factors set as their respective expected values; in the second stage, based on the solutions obtained in the first stage and with consideration of uncertainty factors, a neighbourhood search heuristic is employed to generate robust solutions that can fight better against the fluctuation of uncertainty factors. Finally, extensive numerical experiments are conducted to test the performance of the proposed method.

Distribution Planning: An Integration of Constraint Satisfaction & Heuristic Search Techniques

DTIC Science & Technology

1990-01-01

Proceedings of the Symposium on Aritificial Intelligence in ~~litary Logistics, Arlington, VA: American Defense Preparedness Assoc. pp. 177-182...dynamic changes, too many variables, and lack pf planning time. The Human Engineeri n ~ Laboratory (HEL) is developing artificial intelligence (AI...first attempt. The field of artificial intelligence includes a variety of knowledge-based approaches. Most widely known are Expert Systems, that are

Optimization Techniques for Clustering,Connectivity, and Flow Problems in Complex Networks

DTIC Science & Technology

2012-10-01

discrete optimization and for analysis of performance of algorithm portfolios; introducing a metaheuristic framework of variable objective search that...The results of empirical evaluation of the proposed algorithm are also included. 1.3 Theoretical analysis of heuristics and designing new metaheuristic ...analysis of heuristics for inapproximable problems and designing new metaheuristic approaches for the problems of interest; (IV) Developing new models

Complexity, Heuristic, and Search Analysis for the Games of Crossings and Epaminondas

DTIC Science & Technology

2014-03-27

research in Artifical Intelligence (Section 2.1) and why games are studied (Section 2.2). Section 2.3 discusses how games are played and solved. An...5 2.1 Games in Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Game Study...Artificial Intelligence UCT Upper Confidence Bounds applied to Trees HUCT Heuristic Guided UCT LOA Lines of Action UCB Upper Confidence Bound RAVE Rapid

A Systolic Array-Based FPGA Parallel Architecture for the BLAST Algorithm

PubMed Central

Guo, Xinyu; Wang, Hong; Devabhaktuni, Vijay

2012-01-01

A design of systolic array-based Field Programmable Gate Array (FPGA) parallel architecture for Basic Local Alignment Search Tool (BLAST) Algorithm is proposed. BLAST is a heuristic biological sequence alignment algorithm which has been used by bioinformatics experts. In contrast to other designs that detect at most one hit in one-clock-cycle, our design applies a Multiple Hits Detection Module which is a pipelining systolic array to search multiple hits in a single-clock-cycle. Further, we designed a Hits Combination Block which combines overlapping hits from systolic array into one hit. These implementations completed the first and second step of BLAST architecture and achieved significant speedup comparing with previously published architectures. PMID:25969747

Ancient village fire escape path planning based on improved ant colony algorithm

NASA Astrophysics Data System (ADS)

Xia, Wei; Cao, Kang; Hu, QianChuan

2017-06-01

The roadways are narrow and perplexing in ancient villages, it brings challenges and difficulties for people to choose route to escape when a fire occurs. In this paper, a fire escape path planning method based on ant colony algorithm is presented according to the problem. The factors in the fire environment which influence the escape speed is introduced to improve the heuristic function of the algorithm, optimal transfer strategy, and adjustment pheromone volatile factor to improve pheromone update strategy adaptively, improve its dynamic search ability and search speed. Through simulation, the dynamic adjustment of the optimal escape path is obtained, and the method is proved to be feasible.

High-throughput search for caloric materials: the CaloriCool approach

NASA Astrophysics Data System (ADS)

Zarkevich, N. A.; Johnson, D. D.; Pecharsky, V. K.

2018-01-01

The high-throughput search paradigm adopted by the newly established caloric materials consortium—CaloriCool®—with the goal to substantially accelerate discovery and design of novel caloric materials is briefly discussed. We begin with describing material selection criteria based on known properties, which are then followed by heuristic fast estimates, ab initio calculations, all of which has been implemented in a set of automated computational tools and measurements. We also demonstrate how theoretical and computational methods serve as a guide for experimental efforts by considering a representative example from the field of magnetocaloric materials.

High-throughput search for caloric materials: the CaloriCool approach

DOE PAGES

Zarkevich, Nikolai A.; Johnson, Duane D.; Pecharsky, V. K.

2017-12-13

The high-throughput search paradigm adopted by the newly established caloric materials consortium—CaloriCool ®—with the goal to substantially accelerate discovery and design of novel caloric materials is briefly discussed. Here, we begin with describing material selection criteria based on known properties, which are then followed by heuristic fast estimates, ab initio calculations, all of which has been implemented in a set of automated computational tools and measurements. We also demonstrate how theoretical and computational methods serve as a guide for experimental efforts by considering a representative example from the field of magnetocaloric materials.

Discrete bacteria foraging optimization algorithm for graph based problems - a transition from continuous to discrete

NASA Astrophysics Data System (ADS)

Sur, Chiranjib; Shukla, Anupam

2018-03-01

Bacteria Foraging Optimisation Algorithm is a collective behaviour-based meta-heuristics searching depending on the social influence of the bacteria co-agents in the search space of the problem. The algorithm faces tremendous hindrance in terms of its application for discrete problems and graph-based problems due to biased mathematical modelling and dynamic structure of the algorithm. This had been the key factor to revive and introduce the discrete form called Discrete Bacteria Foraging Optimisation (DBFO) Algorithm for discrete problems which exceeds the number of continuous domain problems represented by mathematical and numerical equations in real life. In this work, we have mainly simulated a graph-based road multi-objective optimisation problem and have discussed the prospect of its utilisation in other similar optimisation problems and graph-based problems. The various solution representations that can be handled by this DBFO has also been discussed. The implications and dynamics of the various parameters used in the DBFO are illustrated from the point view of the problems and has been a combination of both exploration and exploitation. The result of DBFO has been compared with Ant Colony Optimisation and Intelligent Water Drops Algorithms. Important features of DBFO are that the bacteria agents do not depend on the local heuristic information but estimates new exploration schemes depending upon the previous experience and covered path analysis. This makes the algorithm better in combination generation for graph-based problems and combination generation for NP hard problems.

Non-uniform cosine modulated filter banks using meta-heuristic algorithms in CSD space.

PubMed

Kalathil, Shaeen; Elias, Elizabeth

2015-11-01

This paper presents an efficient design of non-uniform cosine modulated filter banks (CMFB) using canonic signed digit (CSD) coefficients. CMFB has got an easy and efficient design approach. Non-uniform decomposition can be easily obtained by merging the appropriate filters of a uniform filter bank. Only the prototype filter needs to be designed and optimized. In this paper, the prototype filter is designed using window method, weighted Chebyshev approximation and weighted constrained least square approximation. The coefficients are quantized into CSD, using a look-up-table. The finite precision CSD rounding, deteriorates the filter bank performances. The performances of the filter bank are improved using suitably modified meta-heuristic algorithms. The different meta-heuristic algorithms which are modified and used in this paper are Artificial Bee Colony algorithm, Gravitational Search algorithm, Harmony Search algorithm and Genetic algorithm and they result in filter banks with less implementation complexity, power consumption and area requirements when compared with those of the conventional continuous coefficient non-uniform CMFB.

Non-uniform cosine modulated filter banks using meta-heuristic algorithms in CSD space

PubMed Central

Kalathil, Shaeen; Elias, Elizabeth

2014-01-01

This paper presents an efficient design of non-uniform cosine modulated filter banks (CMFB) using canonic signed digit (CSD) coefficients. CMFB has got an easy and efficient design approach. Non-uniform decomposition can be easily obtained by merging the appropriate filters of a uniform filter bank. Only the prototype filter needs to be designed and optimized. In this paper, the prototype filter is designed using window method, weighted Chebyshev approximation and weighted constrained least square approximation. The coefficients are quantized into CSD, using a look-up-table. The finite precision CSD rounding, deteriorates the filter bank performances. The performances of the filter bank are improved using suitably modified meta-heuristic algorithms. The different meta-heuristic algorithms which are modified and used in this paper are Artificial Bee Colony algorithm, Gravitational Search algorithm, Harmony Search algorithm and Genetic algorithm and they result in filter banks with less implementation complexity, power consumption and area requirements when compared with those of the conventional continuous coefficient non-uniform CMFB. PMID:26644921

Guided Iterative Substructure Search (GI-SSS) - A New Trick for an Old Dog.

PubMed

Weskamp, Nils

2016-07-01

Substructure search (SSS) is a fundamental technique supported by various chemical information systems. Many users apply it in an iterative manner: they modify their queries to shape the composition of the retrieved hit sets according to their needs. We propose and evaluate two heuristic extensions of SSS aimed at simplifying these iterative query modifications by collecting additional information during query processing and visualizing this information in an intuitive way. This gives the user a convenient feedback on how certain changes to the query would affect the retrieved hit set and reduces the number of trial-and-error cycles needed to generate an optimal search result. The proposed heuristics are simple, yet surprisingly effective and can be easily added to existing SSS implementations. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A set-covering based heuristic algorithm for the periodic vehicle routing problem.

PubMed

Cacchiani, V; Hemmelmayr, V C; Tricoire, F

2014-01-30

We present a hybrid optimization algorithm for mixed-integer linear programming, embedding both heuristic and exact components. In order to validate it we use the periodic vehicle routing problem (PVRP) as a case study. This problem consists of determining a set of minimum cost routes for each day of a given planning horizon, with the constraints that each customer must be visited a required number of times (chosen among a set of valid day combinations), must receive every time the required quantity of product, and that the number of routes per day (each respecting the capacity of the vehicle) does not exceed the total number of available vehicles. This is a generalization of the well-known vehicle routing problem (VRP). Our algorithm is based on the linear programming (LP) relaxation of a set-covering-like integer linear programming formulation of the problem, with additional constraints. The LP-relaxation is solved by column generation, where columns are generated heuristically by an iterated local search algorithm. The whole solution method takes advantage of the LP-solution and applies techniques of fixing and releasing of the columns as a local search, making use of a tabu list to avoid cycling. We show the results of the proposed algorithm on benchmark instances from the literature and compare them to the state-of-the-art algorithms, showing the effectiveness of our approach in producing good quality solutions. In addition, we report the results on realistic instances of the PVRP introduced in Pacheco et al. (2011)  [24] and on benchmark instances of the periodic traveling salesman problem (PTSP), showing the efficacy of the proposed algorithm on these as well. Finally, we report the new best known solutions found for all the tested problems.

A set-covering based heuristic algorithm for the periodic vehicle routing problem

PubMed Central

Cacchiani, V.; Hemmelmayr, V.C.; Tricoire, F.

2014-01-01

We present a hybrid optimization algorithm for mixed-integer linear programming, embedding both heuristic and exact components. In order to validate it we use the periodic vehicle routing problem (PVRP) as a case study. This problem consists of determining a set of minimum cost routes for each day of a given planning horizon, with the constraints that each customer must be visited a required number of times (chosen among a set of valid day combinations), must receive every time the required quantity of product, and that the number of routes per day (each respecting the capacity of the vehicle) does not exceed the total number of available vehicles. This is a generalization of the well-known vehicle routing problem (VRP). Our algorithm is based on the linear programming (LP) relaxation of a set-covering-like integer linear programming formulation of the problem, with additional constraints. The LP-relaxation is solved by column generation, where columns are generated heuristically by an iterated local search algorithm. The whole solution method takes advantage of the LP-solution and applies techniques of fixing and releasing of the columns as a local search, making use of a tabu list to avoid cycling. We show the results of the proposed algorithm on benchmark instances from the literature and compare them to the state-of-the-art algorithms, showing the effectiveness of our approach in producing good quality solutions. In addition, we report the results on realistic instances of the PVRP introduced in Pacheco et al. (2011)  [24] and on benchmark instances of the periodic traveling salesman problem (PTSP), showing the efficacy of the proposed algorithm on these as well. Finally, we report the new best known solutions found for all the tested problems. PMID:24748696

Probabilistic representation in syllogistic reasoning: A theory to integrate mental models and heuristics.

PubMed

Hattori, Masasi

2016-12-01

This paper presents a new theory of syllogistic reasoning. The proposed model assumes there are probabilistic representations of given signature situations. Instead of conducting an exhaustive search, the model constructs an individual-based "logical" mental representation that expresses the most probable state of affairs, and derives a necessary conclusion that is not inconsistent with the model using heuristics based on informativeness. The model is a unification of previous influential models. Its descriptive validity has been evaluated against existing empirical data and two new experiments, and by qualitative analyses based on previous empirical findings, all of which supported the theory. The model's behavior is also consistent with findings in other areas, including working memory capacity. The results indicate that people assume the probabilities of all target events mentioned in a syllogism to be almost equal, which suggests links between syllogistic reasoning and other areas of cognition. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

FlavonoidSearch: A system for comprehensive flavonoid annotation by mass spectrometry.

PubMed

Akimoto, Nayumi; Ara, Takeshi; Nakajima, Daisuke; Suda, Kunihiro; Ikeda, Chiaki; Takahashi, Shingo; Muneto, Reiko; Yamada, Manabu; Suzuki, Hideyuki; Shibata, Daisuke; Sakurai, Nozomu

2017-04-28

Currently, in mass spectrometry-based metabolomics, limited reference mass spectra are available for flavonoid identification. In the present study, a database of probable mass fragments for 6,867 known flavonoids (FsDatabase) was manually constructed based on new structure- and fragmentation-related rules using new heuristics to overcome flavonoid complexity. We developed the FlavonoidSearch system for flavonoid annotation, which consists of the FsDatabase and a computational tool (FsTool) to automatically search the FsDatabase using the mass spectra of metabolite peaks as queries. This system showed the highest identification accuracy for the flavonoid aglycone when compared to existing tools and revealed accurate discrimination between the flavonoid aglycone and other compounds. Sixteen new flavonoids were found from parsley, and the diversity of the flavonoid aglycone among different fruits and vegetables was investigated.

Theoretical Analysis of Local Search and Simple Evolutionary Algorithms for the Generalized Travelling Salesperson Problem.

PubMed

Pourhassan, Mojgan; Neumann, Frank

2018-06-22

The generalized travelling salesperson problem is an important NP-hard combinatorial optimization problem for which meta-heuristics, such as local search and evolutionary algorithms, have been used very successfully. Two hierarchical approaches with different neighbourhood structures, namely a Cluster-Based approach and a Node-Based approach, have been proposed by Hu and Raidl (2008) for solving this problem. In this paper, local search algorithms and simple evolutionary algorithms based on these approaches are investigated from a theoretical perspective. For local search algorithms, we point out the complementary abilities of the two approaches by presenting instances where they mutually outperform each other. Afterwards, we introduce an instance which is hard for both approaches when initialized on a particular point of the search space, but where a variable neighbourhood search combining them finds the optimal solution in polynomial time. Then we turn our attention to analysing the behaviour of simple evolutionary algorithms that use these approaches. We show that the Node-Based approach solves the hard instance of the Cluster-Based approach presented in Corus et al. (2016) in polynomial time. Furthermore, we prove an exponential lower bound on the optimization time of the Node-Based approach for a class of Euclidean instances.

Improving e-book access via a library-developed full-text search tool.

PubMed

Foust, Jill E; Bergen, Phillip; Maxeiner, Gretchen L; Pawlowski, Peter N

2007-01-01

This paper reports on the development of a tool for searching the contents of licensed full-text electronic book (e-book) collections. The Health Sciences Library System (HSLS) provides services to the University of Pittsburgh's medical programs and large academic health system. The HSLS has developed an innovative tool for federated searching of its e-book collections. Built using the XML-based Vivísimo development environment, the tool enables a user to perform a full-text search of over 2,500 titles from the library's seven most highly used e-book collections. From a single "Google-style" query, results are returned as an integrated set of links pointing directly to relevant sections of the full text. Results are also grouped into categories that enable more precise retrieval without reformulation of the search. A heuristic evaluation demonstrated the usability of the tool and a web server log analysis indicated an acceptable level of usage. Based on its success, there are plans to increase the number of online book collections searched. This library's first foray into federated searching has produced an effective tool for searching across large collections of full-text e-books and has provided a good foundation for the development of other library-based federated searching products.

Improving e-book access via a library-developed full-text search tool*

PubMed Central

Foust, Jill E.; Bergen, Phillip; Maxeiner, Gretchen L.; Pawlowski, Peter N.

2007-01-01

Purpose: This paper reports on the development of a tool for searching the contents of licensed full-text electronic book (e-book) collections. Setting: The Health Sciences Library System (HSLS) provides services to the University of Pittsburgh's medical programs and large academic health system. Brief Description: The HSLS has developed an innovative tool for federated searching of its e-book collections. Built using the XML-based Vivísimo development environment, the tool enables a user to perform a full-text search of over 2,500 titles from the library's seven most highly used e-book collections. From a single “Google-style” query, results are returned as an integrated set of links pointing directly to relevant sections of the full text. Results are also grouped into categories that enable more precise retrieval without reformulation of the search. Results/Evaluation: A heuristic evaluation demonstrated the usability of the tool and a web server log analysis indicated an acceptable level of usage. Based on its success, there are plans to increase the number of online book collections searched. Conclusion: This library's first foray into federated searching has produced an effective tool for searching across large collections of full-text e-books and has provided a good foundation for the development of other library-based federated searching products. PMID:17252065

Deep Reading and Learning

DTIC Science & Technology

2017-10-01

works better to learn two different functions rather than sharing the same function for both guiding the search and selecting the final output. 4.2 Co...actions and the other to select the best among the remaining merge actions (Ma et al. 2014). Both of these heuristics are learned by imitating the...decisions that contribute zero loss are considered good for selection . Learning occurs by adjusting weights of the heuristic and the pruning functions

Performance Review of Harmony Search, Differential Evolution and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Mohan Pandey, Hari

2017-08-01

Metaheuristic algorithms are effective in the design of an intelligent system. These algorithms are widely applied to solve complex optimization problems, including image processing, big data analytics, language processing, pattern recognition and others. This paper presents a performance comparison of three meta-heuristic algorithms, namely Harmony Search, Differential Evolution, and Particle Swarm Optimization. These algorithms are originated altogether from different fields of meta-heuristics yet share a common objective. The standard benchmark functions are used for the simulation. Statistical tests are conducted to derive a conclusion on the performance. The key motivation to conduct this research is to categorize the computational capabilities, which might be useful to the researchers.

Familiarity and Recollection in Heuristic Decision Making

PubMed Central

Schwikert, Shane R.; Curran, Tim

2014-01-01

Heuristics involve the ability to utilize memory to make quick judgments by exploiting fundamental cognitive abilities. In the current study we investigated the memory processes that contribute to the recognition heuristic and the fluency heuristic, which are both presumed to capitalize on the by-products of memory to make quick decisions. In Experiment 1, we used a city-size comparison task while recording event-related potentials (ERPs) to investigate the potential contributions of familiarity and recollection to the two heuristics. ERPs were markedly different for recognition heuristic-based decisions and fluency heuristic-based decisions, suggesting a role for familiarity in the recognition heuristic and recollection in the fluency heuristic. In Experiment 2, we coupled the same city-size comparison task with measures of subjective pre-experimental memory for each stimulus in the task. Although previous literature suggests the fluency heuristic relies on recognition speed alone, our results suggest differential contributions of recognition speed and recollected knowledge to these decisions, whereas the recognition heuristic relies on familiarity. Based on these results, we created a new theoretical frame work that explains decisions attributed to both heuristics based on the underlying memory associated with the choice options. PMID:25347534

Familiarity and recollection in heuristic decision making.

PubMed

Schwikert, Shane R; Curran, Tim

2014-12-01

Heuristics involve the ability to utilize memory to make quick judgments by exploiting fundamental cognitive abilities. In the current study we investigated the memory processes that contribute to the recognition heuristic and the fluency heuristic, which are both presumed to capitalize on the byproducts of memory to make quick decisions. In Experiment 1, we used a city-size comparison task while recording event-related potentials (ERPs) to investigate the potential contributions of familiarity and recollection to the 2 heuristics. ERPs were markedly different for recognition heuristic-based decisions and fluency heuristic-based decisions, suggesting a role for familiarity in the recognition heuristic and recollection in the fluency heuristic. In Experiment 2, we coupled the same city-size comparison task with measures of subjective preexperimental memory for each stimulus in the task. Although previous literature suggests the fluency heuristic relies on recognition speed alone, our results suggest differential contributions of recognition speed and recollected knowledge to these decisions, whereas the recognition heuristic relies on familiarity. Based on these results, we created a new theoretical framework that explains decisions attributed to both heuristics based on the underlying memory associated with the choice options. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Approximation algorithms for a genetic diagnostics problem.

PubMed

Kosaraju, S R; Schäffer, A A; Biesecker, L G

1998-01-01

We define and study a combinatorial problem called WEIGHTED DIAGNOSTIC COVER (WDC) that models the use of a laboratory technique called genotyping in the diagnosis of an important class of chromosomal aberrations. An optimal solution to WDC would enable us to define a genetic assay that maximizes the diagnostic power for a specified cost of laboratory work. We develop approximation algorithms for WDC by making use of the well-known problem SET COVER for which the greedy heuristic has been extensively studied. We prove worst-case performance bounds on the greedy heuristic for WDC and for another heuristic we call directional greedy. We implemented both heuristics. We also implemented a local search heuristic that takes the solutions obtained by greedy and dir-greedy and applies swaps until they are locally optimal. We report their performance on a real data set that is representative of the options that a clinical geneticist faces for the real diagnostic problem. Many open problems related to WDC remain, both of theoretical interest and practical importance.

An effective PSO-based memetic algorithm for flow shop scheduling.

PubMed

Liu, Bo; Wang, Ling; Jin, Yi-Hui

2007-02-01

This paper proposes an effective particle swarm optimization (PSO)-based memetic algorithm (MA) for the permutation flow shop scheduling problem (PFSSP) with the objective to minimize the maximum completion time, which is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed PSO-based MA (PSOMA), both PSO-based searching operators and some special local searching operators are designed to balance the exploration and exploitation abilities. In particular, the PSOMA applies the evolutionary searching mechanism of PSO, which is characterized by individual improvement, population cooperation, and competition to effectively perform exploration. On the other hand, the PSOMA utilizes several adaptive local searches to perform exploitation. First, to make PSO suitable for solving PFSSP, a ranked-order value rule based on random key representation is presented to convert the continuous position values of particles to job permutations. Second, to generate an initial swarm with certain quality and diversity, the famous Nawaz-Enscore-Ham (NEH) heuristic is incorporated into the initialization of population. Third, to balance the exploration and exploitation abilities, after the standard PSO-based searching operation, a new local search technique named NEH_1 insertion is probabilistically applied to some good particles selected by using a roulette wheel mechanism with a specified probability. Fourth, to enrich the searching behaviors and to avoid premature convergence, a simulated annealing (SA)-based local search with multiple different neighborhoods is designed and incorporated into the PSOMA. Meanwhile, an effective adaptive meta-Lamarckian learning strategy is employed to decide which neighborhood to be used in SA-based local search. Finally, to further enhance the exploitation ability, a pairwise-based local search is applied after the SA-based search. Simulation results based on benchmarks demonstrate the effectiveness of the PSOMA. Additionally, the effects of some parameters on optimization performances are also discussed.

Detecting false positive sequence homology: a machine learning approach.

PubMed

Fujimoto, M Stanley; Suvorov, Anton; Jensen, Nicholas O; Clement, Mark J; Bybee, Seth M

2016-02-24

Accurate detection of homologous relationships of biological sequences (DNA or amino acid) amongst organisms is an important and often difficult task that is essential to various evolutionary studies, ranging from building phylogenies to predicting functional gene annotations. There are many existing heuristic tools, most commonly based on bidirectional BLAST searches that are used to identify homologous genes and combine them into two fundamentally distinct classes: orthologs and paralogs. Due to only using heuristic filtering based on significance score cutoffs and having no cluster post-processing tools available, these methods can often produce multiple clusters constituting unrelated (non-homologous) sequences. Therefore sequencing data extracted from incomplete genome/transcriptome assemblies originated from low coverage sequencing or produced by de novo processes without a reference genome are susceptible to high false positive rates of homology detection. In this paper we develop biologically informative features that can be extracted from multiple sequence alignments of putative homologous genes (orthologs and paralogs) and further utilized in context of guided experimentation to verify false positive outcomes. We demonstrate that our machine learning method trained on both known homology clusters obtained from OrthoDB and randomly generated sequence alignments (non-homologs), successfully determines apparent false positives inferred by heuristic algorithms especially among proteomes recovered from low-coverage RNA-seq data. Almost ~42 % and ~25 % of predicted putative homologies by InParanoid and HaMStR respectively were classified as false positives on experimental data set. Our process increases the quality of output from other clustering algorithms by providing a novel post-processing method that is both fast and efficient at removing low quality clusters of putative homologous genes recovered by heuristic-based approaches.

The identification of complete domains within protein sequences using accurate E-values for semi-global alignment

PubMed Central

Kann, Maricel G.; Sheetlin, Sergey L.; Park, Yonil; Bryant, Stephen H.; Spouge, John L.

2007-01-01

The sequencing of complete genomes has created a pressing need for automated annotation of gene function. Because domains are the basic units of protein function and evolution, a gene can be annotated from a domain database by aligning domains to the corresponding protein sequence. Ideally, complete domains are aligned to protein subsequences, in a ‘semi-global alignment’. Local alignment, which aligns pieces of domains to subsequences, is common in high-throughput annotation applications, however. It is a mature technique, with the heuristics and accurate E-values required for screening large databases and evaluating the screening results. Hidden Markov models (HMMs) provide an alternative theoretical framework for semi-global alignment, but their use is limited because they lack heuristic acceleration and accurate E-values. Our new tool, GLOBAL, overcomes some limitations of previous semi-global HMMs: it has accurate E-values and the possibility of the heuristic acceleration required for high-throughput applications. Moreover, according to a standard of truth based on protein structure, two semi-global HMM alignment tools (GLOBAL and HMMer) had comparable performance in identifying complete domains, but distinctly outperformed two tools based on local alignment. When searching for complete protein domains, therefore, GLOBAL avoids disadvantages commonly associated with HMMs, yet maintains their superior retrieval performance. PMID:17596268

Multiobjective hyper heuristic scheme for system design and optimization

NASA Astrophysics Data System (ADS)

Rafique, Amer Farhan

2012-11-01

As system design is becoming more and more multifaceted, integrated, and complex, the traditional single objective optimization trends of optimal design are becoming less and less efficient and effective. Single objective optimization methods present a unique optimal solution whereas multiobjective methods present pareto front. The foremost intent is to predict a reasonable distributed pareto-optimal solution set independent of the problem instance through multiobjective scheme. Other objective of application of intended approach is to improve the worthiness of outputs of the complex engineering system design process at the conceptual design phase. The process is automated in order to provide the system designer with the leverage of the possibility of studying and analyzing a large multiple of possible solutions in a short time. This article presents Multiobjective Hyper Heuristic Optimization Scheme based on low level meta-heuristics developed for the application in engineering system design. Herein, we present a stochastic function to manage meta-heuristics (low-level) to augment surety of global optimum solution. Generic Algorithm, Simulated Annealing and Swarm Intelligence are used as low-level meta-heuristics in this study. Performance of the proposed scheme is investigated through a comprehensive empirical analysis yielding acceptable results. One of the primary motives for performing multiobjective optimization is that the current engineering systems require simultaneous optimization of conflicting and multiple. Random decision making makes the implementation of this scheme attractive and easy. Injecting feasible solutions significantly alters the search direction and also adds diversity of population resulting in accomplishment of pre-defined goals set in the proposed scheme.

Location-based social networking media for restaurant promotion and food review using mobile application

NASA Astrophysics Data System (ADS)

Luhur, H. S.; Widjaja, N. D.

2014-03-01

This paper is focusing on the development of a mobile application for searching restaurants and promotions with location based and social networking features. The main function of the application is to search restaurant information. Other functions are also available in this application such as add restaurant, add promotion, add photo, add food review, and other features including social networking features. The restaurant and promotion searching application will be developed under Android platform. Upon completion of this paper, heuristic evaluation and usability testing have been conducted. The result of both testing shows that the application is highly usable. Even though there are some usability problems discovered, the problems can be eliminated immediately by implementing the recommendations from the expert evaluators and the users as the testers of the application. Further improvement made to the application will ensure that the application can really be beneficial for the users of the application.

Need for closure and heuristic information processing: the moderating role of the ability to achieve the need for closure.

PubMed

Kossowska, Małgorzata; Bar-Tal, Yoram

2013-11-01

In contrast to the ample research that shows a positive relationship between the need for closure (NFC) and heuristic information processing, this research examines the hypothesis that this relationship is moderated by the ability to achieve closure (AAC), that is, the ability to use information-processing strategies consistent with the level of NFC. Three different operationalizations of heuristic information processing were used: recall of information consistent with the impression (Study 1); pre-decisional information search (Study 2); and stereotypic impression formation (Study 3). The results of the studies showed that there were positive relationships between NFC and heuristic information processing when participants assessed themselves as being able to use cognitive strategies consistent with their level of NFC (high AAC). For individuals with low AAC, the relationships were negative. Our data show that motivation-cognition interactions influence the information-processing style. © 2012 The British Psychological Society.

When the lowest energy does not induce native structures: parallel minimization of multi-energy values by hybridizing searching intelligences.

PubMed

Lü, Qiang; Xia, Xiao-Yan; Chen, Rong; Miao, Da-Jun; Chen, Sha-Sha; Quan, Li-Jun; Li, Hai-Ou

2012-01-01

Protein structure prediction (PSP), which is usually modeled as a computational optimization problem, remains one of the biggest challenges in computational biology. PSP encounters two difficult obstacles: the inaccurate energy function problem and the searching problem. Even if the lowest energy has been luckily found by the searching procedure, the correct protein structures are not guaranteed to obtain. A general parallel metaheuristic approach is presented to tackle the above two problems. Multi-energy functions are employed to simultaneously guide the parallel searching threads. Searching trajectories are in fact controlled by the parameters of heuristic algorithms. The parallel approach allows the parameters to be perturbed during the searching threads are running in parallel, while each thread is searching the lowest energy value determined by an individual energy function. By hybridizing the intelligences of parallel ant colonies and Monte Carlo Metropolis search, this paper demonstrates an implementation of our parallel approach for PSP. 16 classical instances were tested to show that the parallel approach is competitive for solving PSP problem. This parallel approach combines various sources of both searching intelligences and energy functions, and thus predicts protein conformations with good quality jointly determined by all the parallel searching threads and energy functions. It provides a framework to combine different searching intelligence embedded in heuristic algorithms. It also constructs a container to hybridize different not-so-accurate objective functions which are usually derived from the domain expertise.

When the Lowest Energy Does Not Induce Native Structures: Parallel Minimization of Multi-Energy Values by Hybridizing Searching Intelligences

PubMed Central

Lü, Qiang; Xia, Xiao-Yan; Chen, Rong; Miao, Da-Jun; Chen, Sha-Sha; Quan, Li-Jun; Li, Hai-Ou

2012-01-01

Background Protein structure prediction (PSP), which is usually modeled as a computational optimization problem, remains one of the biggest challenges in computational biology. PSP encounters two difficult obstacles: the inaccurate energy function problem and the searching problem. Even if the lowest energy has been luckily found by the searching procedure, the correct protein structures are not guaranteed to obtain. Results A general parallel metaheuristic approach is presented to tackle the above two problems. Multi-energy functions are employed to simultaneously guide the parallel searching threads. Searching trajectories are in fact controlled by the parameters of heuristic algorithms. The parallel approach allows the parameters to be perturbed during the searching threads are running in parallel, while each thread is searching the lowest energy value determined by an individual energy function. By hybridizing the intelligences of parallel ant colonies and Monte Carlo Metropolis search, this paper demonstrates an implementation of our parallel approach for PSP. 16 classical instances were tested to show that the parallel approach is competitive for solving PSP problem. Conclusions This parallel approach combines various sources of both searching intelligences and energy functions, and thus predicts protein conformations with good quality jointly determined by all the parallel searching threads and energy functions. It provides a framework to combine different searching intelligence embedded in heuristic algorithms. It also constructs a container to hybridize different not-so-accurate objective functions which are usually derived from the domain expertise. PMID:23028708

A Framework for Debugging Geoscience Projects in a High Performance Computing Environment

NASA Astrophysics Data System (ADS)

Baxter, C.; Matott, L.

2012-12-01

High performance computing (HPC) infrastructure has become ubiquitous in today's world with the emergence of commercial cloud computing and academic supercomputing centers. Teams of geoscientists, hydrologists and engineers can take advantage of this infrastructure to undertake large research projects - for example, linking one or more site-specific environmental models with soft computing algorithms, such as heuristic global search procedures, to perform parameter estimation and predictive uncertainty analysis, and/or design least-cost remediation systems. However, the size, complexity and distributed nature of these projects can make identifying failures in the associated numerical experiments using conventional ad-hoc approaches both time- consuming and ineffective. To address these problems a multi-tiered debugging framework has been developed. The framework allows for quickly isolating and remedying a number of potential experimental failures, including: failures in the HPC scheduler; bugs in the soft computing code; bugs in the modeling code; and permissions and access control errors. The utility of the framework is demonstrated via application to a series of over 200,000 numerical experiments involving a suite of 5 heuristic global search algorithms and 15 mathematical test functions serving as cheap analogues for the simulation-based optimization of pump-and-treat subsurface remediation systems.

Social Milieu Oriented Routing: A New Dimension to Enhance Network Security in WSNs.

PubMed

Liu, Lianggui; Chen, Li; Jia, Huiling

2016-02-19

In large-scale wireless sensor networks (WSNs), in order to enhance network security, it is crucial for a trustor node to perform social milieu oriented routing to a target a trustee node to carry out trust evaluation. This challenging social milieu oriented routing with more than one end-to-end Quality of Trust (QoT) constraint has proved to be NP-complete. Heuristic algorithms with polynomial and pseudo-polynomial-time complexities are often used to deal with this challenging problem. However, existing solutions cannot guarantee the efficiency of searching; that is, they can hardly avoid obtaining partial optimal solutions during a searching process. Quantum annealing (QA) uses delocalization and tunneling to avoid falling into local minima without sacrificing execution time. This has been proven a promising way to many optimization problems in recently published literatures. In this paper, for the first time, with the help of a novel approach, that is, configuration path-integral Monte Carlo (CPIMC) simulations, a QA-based optimal social trust path (QA_OSTP) selection algorithm is applied to the extraction of the optimal social trust path in large-scale WSNs. Extensive experiments have been conducted, and the experiment results demonstrate that QA_OSTP outperforms its heuristic opponents.

Search-based model identification of smart-structure damage

NASA Technical Reports Server (NTRS)

Glass, B. J.; Macalou, A.

1991-01-01

This paper describes the use of a combined model and parameter identification approach, based on modal analysis and artificial intelligence (AI) techniques, for identifying damage or flaws in a rotating truss structure incorporating embedded piezoceramic sensors. This smart structure example is representative of a class of structures commonly found in aerospace systems and next generation space structures. Artificial intelligence techniques of classification, heuristic search, and an object-oriented knowledge base are used in an AI-based model identification approach. A finite model space is classified into a search tree, over which a variant of best-first search is used to identify the model whose stored response most closely matches that of the input. Newly-encountered models can be incorporated into the model space. This adaptativeness demonstrates the potential for learning control. Following this output-error model identification, numerical parameter identification is used to further refine the identified model. Given the rotating truss example in this paper, noisy data corresponding to various damage configurations are input to both this approach and a conventional parameter identification method. The combination of the AI-based model identification with parameter identification is shown to lead to smaller parameter corrections than required by the use of parameter identification alone.

A review of parameters and heuristics for guiding metabolic pathfinding.

PubMed

Kim, Sarah M; Peña, Matthew I; Moll, Mark; Bennett, George N; Kavraki, Lydia E

2017-09-15

Recent developments in metabolic engineering have led to the successful biosynthesis of valuable products, such as the precursor of the antimalarial compound, artemisinin, and opioid precursor, thebaine. Synthesizing these traditionally plant-derived compounds in genetically modified yeast cells introduces the possibility of significantly reducing the total time and resources required for their production, and in turn, allows these valuable compounds to become cheaper and more readily available. Most biosynthesis pathways used in metabolic engineering applications have been discovered manually, requiring a tedious search of existing literature and metabolic databases. However, the recent rapid development of available metabolic information has enabled the development of automated approaches for identifying novel pathways. Computer-assisted pathfinding has the potential to save biochemists time in the initial discovery steps of metabolic engineering. In this paper, we review the parameters and heuristics used to guide the search in recent pathfinding algorithms. These parameters and heuristics capture information on the metabolic network structure, compound structures, reaction features, and organism-specificity of pathways. No one metabolic pathfinding algorithm or search parameter stands out as the best to use broadly for solving the pathfinding problem, as each method and parameter has its own strengths and shortcomings. As assisted pathfinding approaches continue to become more sophisticated, the development of better methods for visualizing pathway results and integrating these results into existing metabolic engineering practices is also important for encouraging wider use of these pathfinding methods.

Implementation Of Haversine Formula And Best First Search Method In Searching Of Tsunami Evacuation Route

NASA Astrophysics Data System (ADS)

Anisya; Yoga Swara, Ganda

2017-12-01

Padang is one of the cities prone to earthquake disaster with tsunami due to its position at the meeting of two active plates, this is, a source of potentially powerful earthquake and tsunami. Central government and most offices are located in the red zone (vulnerable areas), it will also affect the evacuation of the population during the earthquake and tsunami disaster. In this study, researchers produced a system of search nearest shelter using best-first-search method. This method uses the heuristic function, the amount of cost taken and the estimated value or travel time, path length and population density. To calculate the length of the path, researchers used method of haversine formula. The value obtained from the calculation process is implemented on a web-based system. Some alternative paths and some of the closest shelters will be displayed in the system.

Planning chemical syntheses with deep neural networks and symbolic AI

NASA Astrophysics Data System (ADS)

Segler, Marwin H. S.; Preuss, Mike; Waller, Mark P.

2018-03-01

To plan the syntheses of small organic molecules, chemists use retrosynthesis, a problem-solving technique in which target molecules are recursively transformed into increasingly simpler precursors. Computer-aided retrosynthesis would be a valuable tool but at present it is slow and provides results of unsatisfactory quality. Here we use Monte Carlo tree search and symbolic artificial intelligence (AI) to discover retrosynthetic routes. We combined Monte Carlo tree search with an expansion policy network that guides the search, and a filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on essentially all reactions ever published in organic chemistry. Our system solves for almost twice as many molecules, thirty times faster than the traditional computer-aided search method, which is based on extracted rules and hand-designed heuristics. In a double-blind AB test, chemists on average considered our computer-generated routes to be equivalent to reported literature routes.

Assessing the use of cognitive heuristic representativeness in clinical reasoning.

PubMed

Payne, Velma L; Crowley, Rebecca S; Crowley, Rebecca

2008-11-06

We performed a pilot study to investigate use of the cognitive heuristic Representativeness in clinical reasoning. We tested a set of tasks and assessments to determine whether subjects used the heuristics in reasoning, to obtain initial frequencies of heuristic use and related cognitive errors, and to collect cognitive process data using think-aloud techniques. The study investigates two aspects of the Representativeness heuristic - judging by perceived frequency and representativeness as causal beliefs. Results show that subjects apply both aspects of the heuristic during reasoning, and make errors related to misapplication of these heuristics. Subjects in this study rarely used base rates, showed significant variability in their recall of base rates, demonstrated limited ability to use provided base rates, and favored causal data in diagnosis. We conclude that the tasks and assessments we have developed provide a suitable test-bed to study the cognitive processes underlying heuristic errors.

Assessing Use of Cognitive Heuristic Representativeness in Clinical Reasoning

PubMed Central

Payne, Velma L.; Crowley, Rebecca S.

2008-01-01

We performed a pilot study to investigate use of the cognitive heuristic Representativeness in clinical reasoning. We tested a set of tasks and assessments to determine whether subjects used the heuristics in reasoning, to obtain initial frequencies of heuristic use and related cognitive errors, and to collect cognitive process data using think-aloud techniques. The study investigates two aspects of the Representativeness heuristic - judging by perceived frequency and representativeness as causal beliefs. Results show that subjects apply both aspects of the heuristic during reasoning, and make errors related to misapplication of these heuristics. Subjects in this study rarely used base rates, showed significant variability in their recall of base rates, demonstrated limited ability to use provided base rates, and favored causal data in diagnosis. We conclude that the tasks and assessments we have developed provide a suitable test-bed to study the cognitive processes underlying heuristic errors. PMID:18999140

An analysis of iterated local search for job-shop scheduling.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whitley, L. Darrell; Howe, Adele E.; Watson, Jean-Paul

2003-08-01

Iterated local search, or ILS, is among the most straightforward meta-heuristics for local search. ILS employs both small-step and large-step move operators. Search proceeds via iterative modifications to a single solution, in distinct alternating phases. In the first phase, local neighborhood search (typically greedy descent) is used in conjunction with the small-step operator to transform solutions into local optima. In the second phase, the large-step operator is applied to generate perturbations to the local optima obtained in the first phase. Ideally, when local neighborhood search is applied to the resulting solution, search will terminate at a different local optimum, i.e.,more » the large-step perturbations should be sufficiently large to enable escape from the attractor basins of local optima. ILS has proven capable of delivering excellent performance on numerous N P-Hard optimization problems. [LMS03]. However, despite its implicity, very little is known about why ILS can be so effective, and under what conditions. The goal of this paper is to advance the state-of-the-art in the analysis of meta-heuristics, by providing answers to this research question. They focus on characterizing both the relationship between the structure of the underlying search space and ILS performance, and the dynamic behavior of ILS. The analysis proceeds in the context of the job-shop scheduling problem (JSP) [Tai94]. They begin by demonstrating that the attractor basins of local optima in the JSP are surprisingly weak, and can be escaped with high probaiblity by accepting a short random sequence of less-fit neighbors. this result is used to develop a new ILS algorithms for the JSP, I-JAR, whose performance is competitive with tabu search on difficult benchmark instances. They conclude by developing a very accurate behavioral model of I-JAR, which yields significant insights into the dynamics of search. The analysis is based on a set of 100 random 10 x 10 problem instances, in addition to some widely used benchmark instances. Both I-JAR and the tabu search algorithm they consider are based on the N1 move operator introduced by van Laarhoven et al. [vLAL92]. The N1 operator induces a connected search space, such that it is always possible to move from an arbitrary solution to an optimal solution; this property is integral to the development of a behavioral model of I-JAR. However, much of the analysis generalizes to other move operators, including that of Nowicki and Smutnick [NS96]. Finally the models are based on the distance between two solutions, which they take as the well-known disjunctive graph distance [MBK99].« less

A System for Automatically Generating Scheduling Heuristics

NASA Technical Reports Server (NTRS)

Morris, Robert

1996-01-01

The goal of this research is to improve the performance of automated schedulers by designing and implementing an algorithm by automatically generating heuristics by selecting a schedule. The particular application selected by applying this method solves the problem of scheduling telescope observations, and is called the Associate Principal Astronomer. The input to the APA scheduler is a set of observation requests submitted by one or more astronomers. Each observation request specifies an observation program as well as scheduling constraints and preferences associated with the program. The scheduler employs greedy heuristic search to synthesize a schedule that satisfies all hard constraints of the domain and achieves a good score with respect to soft constraints expressed as an objective function established by an astronomer-user.

Integrated scheduling of a container handling system with simultaneous loading and discharging operations

NASA Astrophysics Data System (ADS)

Li, Chen; Lu, Zhiqiang; Han, Xiaole; Zhang, Yuejun; Wang, Li

2016-03-01

The integrated scheduling of container handling systems aims to optimize the coordination and overall utilization of all handling equipment, so as to minimize the makespan of a given set of container tasks. A modified disjunctive graph is proposed and a mixed 0-1 programming model is formulated. A heuristic algorithm is presented, in which the original problem is divided into two subproblems. In the first subproblem, contiguous bay crane operations are applied to obtain a good quay crane schedule. In the second subproblem, proper internal truck and yard crane schedules are generated to match the given quay crane schedule. Furthermore, a genetic algorithm based on the heuristic algorithm is developed to search for better solutions. The computational results show that the proposed algorithm can efficiently find high-quality solutions. They also indicate the effectiveness of simultaneous loading and discharging operations compared with separate ones.

Analysis of the type II robotic mixed-model assembly line balancing problem

NASA Astrophysics Data System (ADS)

Çil, Zeynel Abidin; Mete, Süleyman; Ağpak, Kürşad

2017-06-01

In recent years, there has been an increasing trend towards using robots in production systems. Robots are used in different areas such as packaging, transportation, loading/unloading and especially assembly lines. One important step in taking advantage of robots on the assembly line is considering them while balancing the line. On the other hand, market conditions have increased the importance of mixed-model assembly lines. Therefore, in this article, the robotic mixed-model assembly line balancing problem is studied. The aim of this study is to develop a new efficient heuristic algorithm based on beam search in order to minimize the sum of cycle times over all models. In addition, mathematical models of the problem are presented for comparison. The proposed heuristic is tested on benchmark problems and compared with the optimal solutions. The results show that the algorithm is very competitive and is a promising tool for further research.

Optimal path planning for a mobile robot using cuckoo search algorithm

NASA Astrophysics Data System (ADS)

Mohanty, Prases K.; Parhi, Dayal R.

2016-03-01

The shortest/optimal path planning is essential for efficient operation of autonomous vehicles. In this article, a new nature-inspired meta-heuristic algorithm has been applied for mobile robot path planning in an unknown or partially known environment populated by a variety of static obstacles. This meta-heuristic algorithm is based on the levy flight behaviour and brood parasitic behaviour of cuckoos. A new objective function has been formulated between the robots and the target and obstacles, which satisfied the conditions of obstacle avoidance and target-seeking behaviour of robots present in the terrain. Depending upon the objective function value of each nest (cuckoo) in the swarm, the robot avoids obstacles and proceeds towards the target. The smooth optimal trajectory is framed with this algorithm when the robot reaches its goal. Some simulation and experimental results are presented at the end of the paper to show the effectiveness of the proposed navigational controller.

A linguistic geometry for 3D strategic planning

NASA Technical Reports Server (NTRS)

Stilman, Boris

1995-01-01

This paper is a new step in the development and application of the Linguistic Geometry. This formal theory is intended to discover the inner properties of human expert heuristics, which have been successful in a certain class of complex control systems, and apply them to different systems. In this paper we investigate heuristics extracted in the form of hierarchical networks of planning paths of autonomous agents. Employing Linguistic Geometry tools the dynamic hierarchy of networks is represented as a hierarchy of formal attribute languages. The main ideas of this methodology are shown in this paper on the new pilot example of the solution of the extremely complex 3D optimization problem of strategic planning for the space combat of autonomous vehicles. This example demonstrates deep and highly selective search in comparison with conventional search algorithms.

Planning and Scheduling for Fleets of Earth Observing Satellites

NASA Technical Reports Server (NTRS)

Frank, Jeremy; Jonsson, Ari; Morris, Robert; Smith, David E.; Norvig, Peter (Technical Monitor)

2001-01-01

We address the problem of scheduling observations for a collection of earth observing satellites. This scheduling task is a difficult optimization problem, potentially involving many satellites, hundreds of requests, constraints on when and how to service each request, and resources such as instruments, recording devices, transmitters, and ground stations. High-fidelity models are required to ensure the validity of schedules; at the same time, the size and complexity of the problem makes it unlikely that systematic optimization search methods will be able to solve them in a reasonable time. This paper presents a constraint-based approach to solving the Earth Observing Satellites (EOS) scheduling problem, and proposes a stochastic heuristic search method for solving it.

Variable neighborhood search for reverse engineering of gene regulatory networks.

PubMed

Nicholson, Charles; Goodwin, Leslie; Clark, Corey

2017-01-01

A new search heuristic, Divided Neighborhood Exploration Search, designed to be used with inference algorithms such as Bayesian networks to improve on the reverse engineering of gene regulatory networks is presented. The approach systematically moves through the search space to find topologies representative of gene regulatory networks that are more likely to explain microarray data. In empirical testing it is demonstrated that the novel method is superior to the widely employed greedy search techniques in both the quality of the inferred networks and computational time. Copyright © 2016 Elsevier Inc. All rights reserved.

An adaptive large neighborhood search heuristic for Two-Echelon Vehicle Routing Problems arising in city logistics

PubMed Central

Hemmelmayr, Vera C.; Cordeau, Jean-François; Crainic, Teodor Gabriel

2012-01-01

In this paper, we propose an adaptive large neighborhood search heuristic for the Two-Echelon Vehicle Routing Problem (2E-VRP) and the Location Routing Problem (LRP). The 2E-VRP arises in two-level transportation systems such as those encountered in the context of city logistics. In such systems, freight arrives at a major terminal and is shipped through intermediate satellite facilities to the final customers. The LRP can be seen as a special case of the 2E-VRP in which vehicle routing is performed only at the second level. We have developed new neighborhood search operators by exploiting the structure of the two problem classes considered and have also adapted existing operators from the literature. The operators are used in a hierarchical scheme reflecting the multi-level nature of the problem. Computational experiments conducted on several sets of instances from the literature show that our algorithm outperforms existing solution methods for the 2E-VRP and achieves excellent results on the LRP. PMID:23483764

An adaptive large neighborhood search heuristic for Two-Echelon Vehicle Routing Problems arising in city logistics.

PubMed

Hemmelmayr, Vera C; Cordeau, Jean-François; Crainic, Teodor Gabriel

2012-12-01

In this paper, we propose an adaptive large neighborhood search heuristic for the Two-Echelon Vehicle Routing Problem (2E-VRP) and the Location Routing Problem (LRP). The 2E-VRP arises in two-level transportation systems such as those encountered in the context of city logistics. In such systems, freight arrives at a major terminal and is shipped through intermediate satellite facilities to the final customers. The LRP can be seen as a special case of the 2E-VRP in which vehicle routing is performed only at the second level. We have developed new neighborhood search operators by exploiting the structure of the two problem classes considered and have also adapted existing operators from the literature. The operators are used in a hierarchical scheme reflecting the multi-level nature of the problem. Computational experiments conducted on several sets of instances from the literature show that our algorithm outperforms existing solution methods for the 2E-VRP and achieves excellent results on the LRP.

Hybrid General Pattern Search and Simulated Annealing for Industrail Production Planning Problems

NASA Astrophysics Data System (ADS)

Vasant, P.; Barsoum, N.

2010-06-01

In this paper, the hybridization of GPS (General Pattern Search) method and SA (Simulated Annealing) incorporated in the optimization process in order to look for the global optimal solution for the fitness function and decision variables as well as minimum computational CPU time. The real strength of SA approach been tested in this case study problem of industrial production planning. This is due to the great advantage of SA for being easily escaping from trapped in local minima by accepting up-hill move through a probabilistic procedure in the final stages of optimization process. Vasant [1] in his Ph. D thesis has provided 16 different techniques of heuristic and meta-heuristic in solving industrial production problems with non-linear cubic objective functions, eight decision variables and 29 constraints. In this paper, fuzzy technological problems have been solved using hybrid techniques of general pattern search and simulated annealing. The simulated and computational results are compared to other various evolutionary techniques.

Asking better questions: How presentation formats influence information search.

PubMed

Wu, Charley M; Meder, Björn; Filimon, Flavia; Nelson, Jonathan D

2017-08-01

While the influence of presentation formats have been widely studied in Bayesian reasoning tasks, we present the first systematic investigation of how presentation formats influence information search decisions. Four experiments were conducted across different probabilistic environments, where subjects (N = 2,858) chose between 2 possible search queries, each with binary probabilistic outcomes, with the goal of maximizing classification accuracy. We studied 14 different numerical and visual formats for presenting information about the search environment, constructed across 6 design features that have been prominently related to improvements in Bayesian reasoning accuracy (natural frequencies, posteriors, complement, spatial extent, countability, and part-to-whole information). The posterior variants of the icon array and bar graph formats led to the highest proportion of correct responses, and were substantially better than the standard probability format. Results suggest that presenting information in terms of posterior probabilities and visualizing natural frequencies using spatial extent (a perceptual feature) were especially helpful in guiding search decisions, although environments with a mixture of probabilistic and certain outcomes were challenging across all formats. Subjects who made more accurate probability judgments did not perform better on the search task, suggesting that simple decision heuristics may be used to make search decisions without explicitly applying Bayesian inference to compute probabilities. We propose a new take-the-difference (TTD) heuristic that identifies the accuracy-maximizing query without explicit computation of posterior probabilities. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Feature selection with harmony search.

PubMed

Diao, Ren; Shen, Qiang

2012-12-01

Many search strategies have been exploited for the task of feature selection (FS), in an effort to identify more compact and better quality subsets. Such work typically involves the use of greedy hill climbing (HC), or nature-inspired heuristics, in order to discover the optimal solution without going through exhaustive search. In this paper, a novel FS approach based on harmony search (HS) is presented. It is a general approach that can be used in conjunction with many subset evaluation techniques. The simplicity of HS is exploited to reduce the overall complexity of the search process. The proposed approach is able to escape from local solutions and identify multiple solutions owing to the stochastic nature of HS. Additional parameter control schemes are introduced to reduce the effort and impact of parameter configuration. These can be further combined with the iterative refinement strategy, tailored to enforce the discovery of quality subsets. The resulting approach is compared with those that rely on HC, genetic algorithms, and particle swarm optimization, accompanied by in-depth studies of the suggested improvements.

Automated Detection of Heuristics and Biases among Pathologists in a Computer-Based System

ERIC Educational Resources Information Center

Crowley, Rebecca S.; Legowski, Elizabeth; Medvedeva, Olga; Reitmeyer, Kayse; Tseytlin, Eugene; Castine, Melissa; Jukic, Drazen; Mello-Thoms, Claudia

2013-01-01

The purpose of this study is threefold: (1) to develop an automated, computer-based method to detect heuristics and biases as pathologists examine virtual slide cases, (2) to measure the frequency and distribution of heuristics and errors across three levels of training, and (3) to examine relationships of heuristics to biases, and biases to…

Automated Classification of Phonological Errors in Aphasic Language

PubMed Central

Ahuja, Sanjeev B.; Reggia, James A.; Berndt, Rita S.

1984-01-01

Using heuristically-guided state space search, a prototype program has been developed to simulate and classify phonemic errors occurring in the speech of neurologically-impaired patients. Simulations are based on an interchangeable rule/operator set of elementary errors which represent a theory of phonemic processing faults. This work introduces and evaluates a novel approach to error simulation and classification, it provides a prototype simulation tool for neurolinguistic research, and it forms the initial phase of a larger research effort involving computer modelling of neurolinguistic processes.

The Stratway Program for Strategic Conflict Resolution: User's Guide

NASA Technical Reports Server (NTRS)

Hagen, George E.; Butler, Ricky W.; Maddalon, Jeffrey M.

2016-01-01

Stratway is a strategic conflict detection and resolution program. It provides both intent-based conflict detection and conflict resolution for a single ownship in the presence of multiple traffic aircraft and weather cells defined by moving polygons. It relies on a set of heuristic search strategies to solve conflicts. These strategies are user configurable through multiple parameters. The program can be called from other programs through an application program interface (API) and can also be executed from a command line.

Adaptive photoacoustic imaging quality optimization with EMD and reconstruction

NASA Astrophysics Data System (ADS)

Guo, Chengwen; Ding, Yao; Yuan, Jie; Xu, Guan; Wang, Xueding; Carson, Paul L.

2016-10-01

Biomedical photoacoustic (PA) signal is characterized with extremely low signal to noise ratio which will yield significant artifacts in photoacoustic tomography (PAT) images. Since PA signals acquired by ultrasound transducers are non-linear and non-stationary, traditional data analysis methods such as Fourier and wavelet method cannot give useful information for further research. In this paper, we introduce an adaptive method to improve the quality of PA imaging based on empirical mode decomposition (EMD) and reconstruction. Data acquired by ultrasound transducers are adaptively decomposed into several intrinsic mode functions (IMFs) after a sifting pre-process. Since noise is randomly distributed in different IMFs, depressing IMFs with more noise while enhancing IMFs with less noise can effectively enhance the quality of reconstructed PAT images. However, searching optimal parameters by means of brute force searching algorithms will cost too much time, which prevent this method from practical use. To find parameters within reasonable time, heuristic algorithms, which are designed for finding good solutions more efficiently when traditional methods are too slow, are adopted in our method. Two of the heuristic algorithms, Simulated Annealing Algorithm, a probabilistic method to approximate the global optimal solution, and Artificial Bee Colony Algorithm, an optimization method inspired by the foraging behavior of bee swarm, are selected to search optimal parameters of IMFs in this paper. The effectiveness of our proposed method is proved both on simulated data and PA signals from real biomedical tissue, which might bear the potential for future clinical PA imaging de-noising.

Mathematical programming formulations for satellite synthesis

NASA Technical Reports Server (NTRS)

Bhasin, Puneet; Reilly, Charles H.

1987-01-01

The problem of satellite synthesis can be described as optimally allotting locations and sometimes frequencies and polarizations, to communication satellites so that interference from unwanted satellite signals does not exceed a specified threshold. In this report, mathematical programming models and optimization methods are used to solve satellite synthesis problems. A nonlinear programming formulation which is solved using Zoutendijk's method and a gradient search method is described. Nine mixed integer programming models are considered. Results of computer runs with these nine models and five geographically compatible scenarios are presented and evaluated. A heuristic solution procedure is also used to solve two of the models studied. Heuristic solutions to three large synthesis problems are presented. The results of our analysis show that the heuristic performs very well, both in terms of solution quality and solution time, on the two models to which it was applied. It is concluded that the heuristic procedure is the best of the methods considered for solving satellite synthesis problems.

Meta-RaPS Algorithm for the Aerial Refueling Scheduling Problem

NASA Technical Reports Server (NTRS)

Kaplan, Sezgin; Arin, Arif; Rabadi, Ghaith

2011-01-01

The Aerial Refueling Scheduling Problem (ARSP) can be defined as determining the refueling completion times for each fighter aircraft (job) on multiple tankers (machines). ARSP assumes that jobs have different release times and due dates, The total weighted tardiness is used to evaluate schedule's quality. Therefore, ARSP can be modeled as a parallel machine scheduling with release limes and due dates to minimize the total weighted tardiness. Since ARSP is NP-hard, it will be more appropriate to develop a pproimate or heuristic algorithm to obtain solutions in reasonable computation limes. In this paper, Meta-Raps-ATC algorithm is implemented to create high quality solutions. Meta-RaPS (Meta-heuristic for Randomized Priority Search) is a recent and promising meta heuristic that is applied by introducing randomness to a construction heuristic. The Apparent Tardiness Rule (ATC), which is a good rule for scheduling problems with tardiness objective, is used to construct initial solutions which are improved by an exchanging operation. Results are presented for generated instances.

The use of a genetic algorithm-based search strategy in geostatistics: application to a set of anisotropic piezometric head data

NASA Astrophysics Data System (ADS)

Abedini, M. J.; Nasseri, M.; Burn, D. H.

2012-04-01

In any geostatistical study, an important consideration is the choice of an appropriate, repeatable, and objective search strategy that controls the nearby samples to be included in the location-specific estimation procedure. Almost all geostatistical software available in the market puts the onus on the user to supply search strategy parameters in a heuristic manner. These parameters are solely controlled by geographical coordinates that are defined for the entire area under study, and the user has no guidance as to how to choose these parameters. The main thesis of the current study is that the selection of search strategy parameters has to be driven by data—both the spatial coordinates and the sample values—and cannot be chosen beforehand. For this purpose, a genetic-algorithm-based ordinary kriging with moving neighborhood technique is proposed. The search capability of a genetic algorithm is exploited to search the feature space for appropriate, either local or global, search strategy parameters. Radius of circle/sphere and/or radii of standard or rotated ellipse/ellipsoid are considered as the decision variables to be optimized by GA. The superiority of GA-based ordinary kriging is demonstrated through application to the Wolfcamp Aquifer piezometric head data. Assessment of numerical results showed that definition of search strategy parameters based on both geographical coordinates and sample values improves cross-validation statistics when compared with that based on geographical coordinates alone. In the case of a variable search neighborhood for each estimation point, optimization of local search strategy parameters for an elliptical support domain—the orientation of which is dictated by anisotropic axes—via GA was able to capture the dynamics of piezometric head in west Texas/New Mexico in an efficient way.

Heuristic reusable dynamic programming: efficient updates of local sequence alignment.

PubMed

Hong, Changjin; Tewfik, Ahmed H

2009-01-01

Recomputation of the previously evaluated similarity results between biological sequences becomes inevitable when researchers realize errors in their sequenced data or when the researchers have to compare nearly similar sequences, e.g., in a family of proteins. We present an efficient scheme for updating local sequence alignments with an affine gap model. In principle, using the previous matching result between two amino acid sequences, we perform a forward-backward alignment to generate heuristic searching bands which are bounded by a set of suboptimal paths. Given a correctly updated sequence, we initially predict a new score of the alignment path for each contour to select the best candidates among them. Then, we run the Smith-Waterman algorithm in this confined space. Furthermore, our heuristic alignment for an updated sequence shows that it can be further accelerated by using reusable dynamic programming (rDP), our prior work. In this study, we successfully validate "relative node tolerance bound" (RNTB) in the pruned searching space. Furthermore, we improve the computational performance by quantifying the successful RNTB tolerance probability and switch to rDP on perturbation-resilient columns only. In our searching space derived by a threshold value of 90 percent of the optimal alignment score, we find that 98.3 percent of contours contain correctly updated paths. We also find that our method consumes only 25.36 percent of the runtime cost of sparse dynamic programming (sDP) method, and to only 2.55 percent of that of a normal dynamic programming with the Smith-Waterman algorithm.

On simulated annealing phase transitions in phylogeny reconstruction.

PubMed

Strobl, Maximilian A R; Barker, Daniel

2016-08-01

Phylogeny reconstruction with global criteria is NP-complete or NP-hard, hence in general requires a heuristic search. We investigate the powerful, physically inspired, general-purpose heuristic simulated annealing, applied to phylogeny reconstruction. Simulated annealing mimics the physical process of annealing, where a liquid is gently cooled to form a crystal. During the search, periods of elevated specific heat occur, analogous to physical phase transitions. These simulated annealing phase transitions play a crucial role in the outcome of the search. Nevertheless, they have received comparably little attention, for phylogeny or other optimisation problems. We analyse simulated annealing phase transitions during searches for the optimal phylogenetic tree for 34 real-world multiple alignments. In the same way in which melting temperatures differ between materials, we observe distinct specific heat profiles for each input file. We propose this reflects differences in the search landscape and can serve as a measure for problem difficulty and for suitability of the algorithm's parameters. We discuss application in algorithmic optimisation and as a diagnostic to assess parameterisation before computationally costly, large phylogeny reconstructions are launched. Whilst the focus here lies on phylogeny reconstruction under maximum parsimony, it is plausible that our results are more widely applicable to optimisation procedures in science and industry. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Deriving a Set of Privacy Specific Heuristics for the Assessment of PHRs (Personal Health Records).

PubMed

Furano, Riccardo F; Kushniruk, Andre; Barnett, Jeff

2017-01-01

With the emergence of personal health record (PHR) platforms becoming more widely available, this research focused on the development of privacy heuristics to assess PHRs regarding privacy. Existing sets of heuristics are typically not application specific and do not address patient-centric privacy as a main concern prior to undergoing PHR procurement. A set of privacy specific heuristics were developed based on a scoping review of the literature. An internet-based commercially available, vendor specific PHR application was evaluated using the derived set of privacy specific heuristics. The proposed set of privacy specific derived heuristics is explored in detail in relation to ISO 29100. The assessment of the internet-based commercially available, vendor specific PHR application indicated numerous violations. These violations were noted within the study. It is argued that the new derived privacy heuristics should be used in addition to Nielsen's well-established set of heuristics. Privacy specific heuristics could be used to assess PHR portal system-level privacy mechanisms in the procurement process of a PHR application and may prove to be a beneficial form of assessment to prevent the selection of a PHR platform with a poor privacy specific interface design.

Restart Operator Meta-heuristics for a Problem-Oriented Evolutionary Strategies Algorithm in Inverse Mathematical MISO Modelling Problem Solving

NASA Astrophysics Data System (ADS)

Ryzhikov, I. S.; Semenkin, E. S.

2017-02-01

This study is focused on solving an inverse mathematical modelling problem for dynamical systems based on observation data and control inputs. The mathematical model is being searched in the form of a linear differential equation, which determines the system with multiple inputs and a single output, and a vector of the initial point coordinates. The described problem is complex and multimodal and for this reason the proposed evolutionary-based optimization technique, which is oriented on a dynamical system identification problem, was applied. To improve its performance an algorithm restart operator was implemented.

Generalized Likelihood Uncertainty Estimation (GLUE) Using Multi-Optimization Algorithm as Sampling Method

NASA Astrophysics Data System (ADS)

Wang, Z.

2015-12-01

For decades, distributed and lumped hydrological models have furthered our understanding of hydrological system. The development of hydrological simulation in large scale and high precision elaborated the spatial descriptions and hydrological behaviors. Meanwhile, the new trend is also followed by the increment of model complexity and number of parameters, which brings new challenges of uncertainty quantification. Generalized Likelihood Uncertainty Estimation (GLUE) has been widely used in uncertainty analysis for hydrological models referring to Monte Carlo method coupled with Bayesian estimation. However, the stochastic sampling method of prior parameters adopted by GLUE appears inefficient, especially in high dimensional parameter space. The heuristic optimization algorithms utilizing iterative evolution show better convergence speed and optimality-searching performance. In light of the features of heuristic optimization algorithms, this study adopted genetic algorithm, differential evolution, shuffled complex evolving algorithm to search the parameter space and obtain the parameter sets of large likelihoods. Based on the multi-algorithm sampling, hydrological model uncertainty analysis is conducted by the typical GLUE framework. To demonstrate the superiority of the new method, two hydrological models of different complexity are examined. The results shows the adaptive method tends to be efficient in sampling and effective in uncertainty analysis, providing an alternative path for uncertainty quantilization.

A Hybrid Tabu Search Heuristic for a Bilevel Competitive Facility Location Model

NASA Astrophysics Data System (ADS)

Küçükaydın, Hande; Aras, Necati; Altınel, I. Kuban

We consider a problem in which a firm or franchise enters a market by locating new facilities where there are existing facilities belonging to a competitor. The firm aims at finding the location and attractiveness of each facility to be opened so as to maximize its profit. The competitor, on the other hand, can react by adjusting the attractiveness of its existing facilities, opening new facilities and/or closing existing ones with the objective of maximizing its own profit. The demand is assumed to be aggregated at certain points in the plane and the facilities of the firm can be located at prespecified candidate sites. We employ Huff's gravity-based rule in modeling the behavior of the customers where the fraction of customers at a demand point that visit a certain facility is proportional to the facility attractiveness and inversely proportional to the distance between the facility site and demand point. We formulate a bilevel mixed-integer nonlinear programming model where the firm entering the market is the leader and the competitor is the follower. In order to find a feasible solution of this model, we develop a hybrid tabu search heuristic which makes use of two exact methods as subroutines: a gradient ascent method and a branch-and-bound algorithm with nonlinear programming relaxation.

An improved Pearson's correlation proximity-based hierarchical clustering for mining biological association between genes.

PubMed

Booma, P M; Prabhakaran, S; Dhanalakshmi, R

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality.

An Improved Pearson's Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes

PubMed Central

Booma, P. M.; Prabhakaran, S.; Dhanalakshmi, R.

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality. PMID:25136661

Two-agent cooperative search using game models with endurance-time constraints

NASA Astrophysics Data System (ADS)

Sujit, P. B.; Ghose, Debasish

2010-07-01

In this article, the problem of two Unmanned Aerial Vehicles (UAVs) cooperatively searching an unknown region is addressed. The search region is discretized into hexagonal cells and each cell is assumed to possess an uncertainty value. The UAVs have to cooperatively search these cells taking limited endurance, sensor and communication range constraints into account. Due to limited endurance, the UAVs need to return to the base station for refuelling and also need to select a base station when multiple base stations are present. This article proposes a route planning algorithm that takes endurance time constraints into account and uses game theoretical strategies to reduce the uncertainty. The route planning algorithm selects only those cells that ensure the agent will return to any one of the available bases. A set of paths are formed using these cells which the game theoretical strategies use to select a path that yields maximum uncertainty reduction. We explore non-cooperative Nash, cooperative and security strategies from game theory to enhance the search effectiveness. Monte-Carlo simulations are carried out which show the superiority of the game theoretical strategies over greedy strategy for different look ahead step length paths. Within the game theoretical strategies, non-cooperative Nash and cooperative strategy perform similarly in an ideal case, but Nash strategy performs better than the cooperative strategy when the perceived information is different. We also propose a heuristic based on partitioning of the search space into sectors to reduce computational overhead without performance degradation.

Moving multiple sinks through wireless sensor networks for lifetime maximization.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Petrioli, Chiara; Carosi, Alessio; Basagni, Stefano

2008-01-01

Unattended sensor networks typically watch for some phenomena such as volcanic events, forest fires, pollution, or movements in animal populations. Sensors report to a collection point periodically or when they observe reportable events. When sensors are too far from the collection point to communicate directly, other sensors relay messages for them. If the collection point location is static, sensor nodes that are closer to the collection point relay far more messages than those on the periphery. Assuming all sensor nodes have roughly the same capabilities, those with high relay burden experience battery failure much faster than the rest of themore » network. However, since their death disconnects the live nodes from the collection point, the whole network is then dead. We consider the problem of moving a set of collectors (sinks) through a wireless sensor network to balance the energy used for relaying messages, maximizing the lifetime of the network. We show how to compute an upper bound on the lifetime for any instance using linear and integer programming. We present a centralized heuristic that produces sink movement schedules that produce network lifetimes within 1.4% of the upper bound for realistic settings. We also present a distributed heuristic that produces lifetimes at most 25:3% below the upper bound. More specifically, we formulate a linear program (LP) that is a relaxation of the scheduling problem. The variables are naturally continuous, but the LP relaxes some constraints. The LP has an exponential number of constraints, but we can satisfy them all by enforcing only a polynomial number using a separation algorithm. This separation algorithm is a p-median facility location problem, which we can solve efficiently in practice for huge instances using integer programming technology. This LP selects a set of good sensor configurations. Given the solution to the LP, we can find a feasible schedule by selecting a subset of these configurations, ordering them via a traveling salesman heuristic, and computing feasible transitions using matching algorithms. This algorithm assumes sinks can get a schedule from a central server or a leader sink. If the network owner prefers the sinks make independent decisions, they can use our distributed heuristic. In this heuristic, sinks maintain estimates of the energy distribution in the network and move greedily (with some coordination) based on local search. This application uses the new SUCASA (Solver Utility for Customization with Automatic Symbol Access) facility within the PICO (Parallel Integer and Combinatorial Optimizer) integer programming solver system. SUCASA allows rapid development of customized math programming (search-based) solvers using a problem's natural multidimensional representation. In this case, SUCASA also significantly improves runtime compared to implementations in the ampl math programming language or in perl.« less

Combination of graph heuristics in producing initial solution of curriculum based course timetabling problem

NASA Astrophysics Data System (ADS)

Wahid, Juliana; Hussin, Naimah Mohd

2016-08-01

The construction of population of initial solution is a crucial task in population-based metaheuristic approach for solving curriculum-based university course timetabling problem because it can affect the convergence speed and also the quality of the final solution. This paper presents an exploration on combination of graph heuristics in construction approach in curriculum based course timetabling problem to produce a population of initial solutions. The graph heuristics were set as single and combination of two heuristics. In addition, several ways of assigning courses into room and timeslot are implemented. All settings of heuristics are then tested on the same curriculum based course timetabling problem instances and are compared with each other in terms of number of population produced. The result shows that combination of saturation degree followed by largest degree heuristic produce the highest number of population of initial solutions. The results from this study can be used in the improvement phase of algorithm that uses population of initial solutions.

Computationally efficient stochastic optimization using multiple realizations

NASA Astrophysics Data System (ADS)

Bayer, P.; Bürger, C. M.; Finkel, M.

2008-02-01

The presented study is concerned with computationally efficient methods for solving stochastic optimization problems involving multiple equally probable realizations of uncertain parameters. A new and straightforward technique is introduced that is based on dynamically ordering the stack of realizations during the search procedure. The rationale is that a small number of critical realizations govern the output of a reliability-based objective function. By utilizing a problem, which is typical to designing a water supply well field, several variants of this "stack ordering" approach are tested. The results are statistically assessed, in terms of optimality and nominal reliability. This study demonstrates that the simple ordering of a given number of 500 realizations while applying an evolutionary search algorithm can save about half of the model runs without compromising the optimization procedure. More advanced variants of stack ordering can, if properly configured, save up to more than 97% of the computational effort that would be required if the entire number of realizations were considered. The findings herein are promising for similar problems of water management and reliability-based design in general, and particularly for non-convex problems that require heuristic search techniques.

An effective hybrid immune algorithm for solving the distributed permutation flow-shop scheduling problem

NASA Astrophysics Data System (ADS)

Xu, Ye; Wang, Ling; Wang, Shengyao; Liu, Min

2014-09-01

In this article, an effective hybrid immune algorithm (HIA) is presented to solve the distributed permutation flow-shop scheduling problem (DPFSP). First, a decoding method is proposed to transfer a job permutation sequence to a feasible schedule considering both factory dispatching and job sequencing. Secondly, a local search with four search operators is presented based on the characteristics of the problem. Thirdly, a special crossover operator is designed for the DPFSP, and mutation and vaccination operators are also applied within the framework of the HIA to perform an immune search. The influence of parameter setting on the HIA is investigated based on the Taguchi method of design of experiment. Extensive numerical testing results based on 420 small-sized instances and 720 large-sized instances are provided. The effectiveness of the HIA is demonstrated by comparison with some existing heuristic algorithms and the variable neighbourhood descent methods. New best known solutions are obtained by the HIA for 17 out of 420 small-sized instances and 585 out of 720 large-sized instances.

An Experience Oriented-Convergence Improved Gravitational Search Algorithm for Minimum Variance Distortionless Response Beamforming Optimum.

PubMed

Darzi, Soodabeh; Tiong, Sieh Kiong; Tariqul Islam, Mohammad; Rezai Soleymanpour, Hassan; Kibria, Salehin

2016-01-01

An experience oriented-convergence improved gravitational search algorithm (ECGSA) based on two new modifications, searching through the best experiments and using of a dynamic gravitational damping coefficient (α), is introduced in this paper. ECGSA saves its best fitness function evaluations and uses those as the agents' positions in searching process. In this way, the optimal found trajectories are retained and the search starts from these trajectories, which allow the algorithm to avoid the local optimums. Also, the agents can move faster in search space to obtain better exploration during the first stage of the searching process and they can converge rapidly to the optimal solution at the final stage of the search process by means of the proposed dynamic gravitational damping coefficient. The performance of ECGSA has been evaluated by applying it to eight standard benchmark functions along with six complicated composite test functions. It is also applied to adaptive beamforming problem as a practical issue to improve the weight vectors computed by minimum variance distortionless response (MVDR) beamforming technique. The results of implementation of the proposed algorithm are compared with some well-known heuristic methods and verified the proposed method in both reaching to optimal solutions and robustness.

Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight.

PubMed

Kumar, Manjeet; Rawat, Tarun Kumar; Aggarwal, Apoorva

2017-03-01

In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Learning to improve iterative repair scheduling

NASA Technical Reports Server (NTRS)

Zweben, Monte; Davis, Eugene

1992-01-01

This paper presents a general learning method for dynamically selecting between repair heuristics in an iterative repair scheduling system. The system employs a version of explanation-based learning called Plausible Explanation-Based Learning (PEBL) that uses multiple examples to confirm conjectured explanations. The basic approach is to conjecture contradictions between a heuristic and statistics that measure the quality of the heuristic. When these contradictions are confirmed, a different heuristic is selected. To motivate the utility of this approach we present an empirical evaluation of the performance of a scheduling system with respect to two different repair strategies. We show that the scheduler that learns to choose between the heuristics outperforms the same scheduler with any one of two heuristics alone.

Scheduling and control strategies for the departure problem in air traffic control

NASA Astrophysics Data System (ADS)

Bolender, Michael Alan

Two problems relating to the departure problem in air traffic control automation are examined. The first problem that is addressed is the scheduling of aircraft for departure. The departure operations at a major US hub airport are analyzed, and a discrete event simulation of the departure operations is constructed. Specifically, the case where there is a single departure runway is considered. The runway is fed by two queues of aircraft. Each queue, in turn, is fed by a single taxiway. Two salient areas regarding scheduling are addressed. The first is the construction of optimal departure sequences for the aircraft that are queued. Several greedy search algorithms are designed to minimize the total time to depart a set of queued aircraft. Each algorithm has a different set of heuristic rules to resolve situations within the search space whenever two branches of the search tree with equal edge costs are encountered. These algorithms are then compared and contrasted with a genetic search algorithm in order to assess the performance of the heuristics. This is done in the context of a static departure problem where the length of the departure queue is fixed. A greedy algorithm which deepens the search whenever two branches of the search tree with non-unique costs are encountered is shown to outperform the other heuristic algorithms. This search strategy is then implemented in the discrete event simulation. A baseline performance level is established, and a sensitivity analysis is performed by implementing changes in traffic mix, routing, and miles-in-trail restrictions for comparison. It is concluded that to minimize the average time spent in the queue for different traffic conditions, a queue assignment algorithm is needed to maintain an even balance of aircraft in the queues. A necessary consideration is to base queue assignment upon traffic management restrictions such as miles-in-trail constraints. The second problem addresses the technical challenges associated with merging departure aircraft onto their filed routes in a congested airspace environment. Conflicts between departures and en route aircraft within the Center airspace are analyzed. Speed control, holding the aircraft; at an intermediate altitude, re-routing, and vectoring are posed as possible deconfliction maneuvers. A cost assessment of these merge strategies, which are based upon 4D fight management and conflict detection and resolution principles, is given. Several merge conflicts are studied and a cost for each resolution is computed. It is shown that vectoring tends to be the most expensive resolution technique. Altitude hold is simple, costs less than vectoring, but may require a long time for the aircraft to achieve separation. Re-routing is the simplest, and provides the most cost benefit since the aircraft flies a shorter distance than if it had followed its filed route. Speed control is shown to be ineffective as a means of increasing separation, but is effective for maintaining separation between aircraft. In addition, the affects of uncertainties on the cost are assessed. The analysis shows that cost is invariant with the decision time.

Prefiltering Model for Homology Detection Algorithms on GPU.

PubMed

Retamosa, Germán; de Pedro, Luis; González, Ivan; Tamames, Javier

2016-01-01

Homology detection has evolved over the time from heavy algorithms based on dynamic programming approaches to lightweight alternatives based on different heuristic models. However, the main problem with these algorithms is that they use complex statistical models, which makes it difficult to achieve a relevant speedup and find exact matches with the original results. Thus, their acceleration is essential. The aim of this article was to prefilter a sequence database. To make this work, we have implemented a groundbreaking heuristic model based on NVIDIA's graphics processing units (GPUs) and multicore processors. Depending on the sensitivity settings, this makes it possible to quickly reduce the sequence database by factors between 50% and 95%, while rejecting no significant sequences. Furthermore, this prefiltering application can be used together with multiple homology detection algorithms as a part of a next-generation sequencing system. Extensive performance and accuracy tests have been carried out in the Spanish National Centre for Biotechnology (NCB). The results show that GPU hardware can accelerate the execution times of former homology detection applications, such as National Centre for Biotechnology Information (NCBI), Basic Local Alignment Search Tool for Proteins (BLASTP), up to a factor of 4.

Memory-Based Simple Heuristics as Attribute Substitution: Competitive Tests of Binary Choice Inference Models

ERIC Educational Resources Information Center

Honda, Hidehito; Matsuka, Toshihiko; Ueda, Kazuhiro

2017-01-01

Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in…

Creating targeted initial populations for genetic product searches in heterogeneous markets

NASA Astrophysics Data System (ADS)

Foster, Garrett; Turner, Callaway; Ferguson, Scott; Donndelinger, Joseph

2014-12-01

Genetic searches often use randomly generated initial populations to maximize diversity and enable a thorough sampling of the design space. While many of these initial configurations perform poorly, the trade-off between population diversity and solution quality is typically acceptable for small-scale problems. Navigating complex design spaces, however, often requires computationally intelligent approaches that improve solution quality. This article draws on research advances in market-based product design and heuristic optimization to strategically construct 'targeted' initial populations. Targeted initial designs are created using respondent-level part-worths estimated from discrete choice models. These designs are then integrated into a traditional genetic search. Two case study problems of differing complexity are presented to illustrate the benefits of this approach. In both problems, targeted populations lead to computational savings and product configurations with improved market share of preferences. Future research efforts to tailor this approach and extend it towards multiple objectives are also discussed.

Advances in feature selection methods for hyperspectral image processing in food industry applications: a review.

PubMed

Dai, Qiong; Cheng, Jun-Hu; Sun, Da-Wen; Zeng, Xin-An

2015-01-01

There is an increased interest in the applications of hyperspectral imaging (HSI) for assessing food quality, safety, and authenticity. HSI provides abundance of spatial and spectral information from foods by combining both spectroscopy and imaging, resulting in hundreds of contiguous wavebands for each spatial position of food samples, also known as the curse of dimensionality. It is desirable to employ feature selection algorithms for decreasing computation burden and increasing predicting accuracy, which are especially relevant in the development of online applications. Recently, a variety of feature selection algorithms have been proposed that can be categorized into three groups based on the searching strategy namely complete search, heuristic search and random search. This review mainly introduced the fundamental of each algorithm, illustrated its applications in hyperspectral data analysis in the food field, and discussed the advantages and disadvantages of these algorithms. It is hoped that this review should provide a guideline for feature selections and data processing in the future development of hyperspectral imaging technique in foods.

Planning collision free paths for two cooperating robots using a divide-and-conquer C-space traversal heuristic

NASA Technical Reports Server (NTRS)

Weaver, Johnathan M.

1993-01-01

A method was developed to plan feasible and obstacle-avoiding paths for two spatial robots working cooperatively in a known static environment. Cooperating spatial robots as referred to herein are robots which work in 6D task space while simultaneously grasping and manipulating a common, rigid payload. The approach is configuration space (c-space) based and performs selective rather than exhaustive c-space mapping. No expensive precomputations are required. A novel, divide-and-conquer type of heuristic is used to guide the selective mapping process. The heuristic does not involve any robot, environment, or task specific assumptions. A technique was also developed which enables solution of the cooperating redundant robot path planning problem without requiring the use of inverse kinematics for a redundant robot. The path planning strategy involves first attempting to traverse along the configuration space vector from the start point towards the goal point. If an unsafe region is encountered, an intermediate via point is identified by conducting a systematic search in the hyperplane orthogonal to and bisecting the unsafe region of the vector. This process is repeatedly applied until a solution to the global path planning problem is obtained. The basic concept behind this strategy is that better local decisions at the beginning of the trouble region may be made if a possible way around the 'center' of the trouble region is known. Thus, rather than attempting paths which look promising locally (at the beginning of a trouble region) but which may not yield overall results, the heuristic attempts local strategies that appear promising for circumventing the unsafe region.

Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification

PubMed Central

2012-01-01

Background Previous studies on tumor classification based on gene expression profiles suggest that gene selection plays a key role in improving the classification performance. Moreover, finding important tumor-related genes with the highest accuracy is a very important task because these genes might serve as tumor biomarkers, which is of great benefit to not only tumor molecular diagnosis but also drug development. Results This paper proposes a novel gene selection method with rich biomedical meaning based on Heuristic Breadth-first Search Algorithm (HBSA) to find as many optimal gene subsets as possible. Due to the curse of dimensionality, this type of method could suffer from over-fitting and selection bias problems. To address these potential problems, a HBSA-based ensemble classifier is constructed using majority voting strategy from individual classifiers constructed by the selected gene subsets, and a novel HBSA-based gene ranking method is designed to find important tumor-related genes by measuring the significance of genes using their occurrence frequencies in the selected gene subsets. The experimental results on nine tumor datasets including three pairs of cross-platform datasets indicate that the proposed method can not only obtain better generalization performance but also find many important tumor-related genes. Conclusions It is found that the frequencies of the selected genes follow a power-law distribution, indicating that only a few top-ranked genes can be used as potential diagnosis biomarkers. Moreover, the top-ranked genes leading to very high prediction accuracy are closely related to specific tumor subtype and even hub genes. Compared with other related methods, the proposed method can achieve higher prediction accuracy with fewer genes. Moreover, they are further justified by analyzing the top-ranked genes in the context of individual gene function, biological pathway, and protein-protein interaction network. PMID:22830977

Influence maximization based on partial network structure information: A comparative analysis on seed selection heuristics

NASA Astrophysics Data System (ADS)

Erkol, Şirag; Yücel, Gönenç

In this study, the problem of seed selection is investigated. This problem is mainly treated as an optimization problem, which is proved to be NP-hard. There are several heuristic approaches in the literature which mostly use algorithmic heuristics. These approaches mainly focus on the trade-off between computational complexity and accuracy. Although the accuracy of algorithmic heuristics are high, they also have high computational complexity. Furthermore, in the literature, it is generally assumed that complete information on the structure and features of a network is available, which is not the case in most of the times. For the study, a simulation model is constructed, which is capable of creating networks, performing seed selection heuristics, and simulating diffusion models. Novel metric-based seed selection heuristics that rely only on partial information are proposed and tested using the simulation model. These heuristics use local information available from nodes in the synthetically created networks. The performances of heuristics are comparatively analyzed on three different network types. The results clearly show that the performance of a heuristic depends on the structure of a network. A heuristic to be used should be selected after investigating the properties of the network at hand. More importantly, the approach of partial information provided promising results. In certain cases, selection heuristics that rely only on partial network information perform very close to similar heuristics that require complete network data.

Solving Capacitated Closed Vehicle Routing Problem with Time Windows (CCVRPTW) using BRKGA with local search

NASA Astrophysics Data System (ADS)

Prasetyo, H.; Alfatsani, M. A.; Fauza, G.

2018-05-01

The main issue in vehicle routing problem (VRP) is finding the shortest route of product distribution from the depot to outlets to minimize total cost of distribution. Capacitated Closed Vehicle Routing Problem with Time Windows (CCVRPTW) is one of the variants of VRP that accommodates vehicle capacity and distribution period. Since the main problem of CCVRPTW is considered a non-polynomial hard (NP-hard) problem, it requires an efficient and effective algorithm to solve the problem. This study was aimed to develop Biased Random Key Genetic Algorithm (BRKGA) that is combined with local search to solve the problem of CCVRPTW. The algorithm design was then coded by MATLAB. Using numerical test, optimum algorithm parameters were set and compared with the heuristic method and Standard BRKGA to solve a case study on soft drink distribution. Results showed that BRKGA combined with local search resulted in lower total distribution cost compared with the heuristic method. Moreover, the developed algorithm was found to be successful in increasing the performance of Standard BRKGA.

Better Decomposition Heuristics for the Maximum-Weight Connected Graph Problem Using Betweenness Centrality

NASA Astrophysics Data System (ADS)

Yamamoto, Takanori; Bannai, Hideo; Nagasaki, Masao; Miyano, Satoru

We present new decomposition heuristics for finding the optimal solution for the maximum-weight connected graph problem, which is known to be NP-hard. Previous optimal algorithms for solving the problem decompose the input graph into subgraphs using heuristics based on node degree. We propose new heuristics based on betweenness centrality measures, and show through computational experiments that our new heuristics tend to reduce the number of subgraphs in the decomposition, and therefore could lead to the reduction in computational time for finding the optimal solution. The method is further applied to analysis of biological pathway data.

Usability Study and Heuristic Evaluation of the Applied Robotics for Installations and Base Operations (ARIBO) Driverless Vehicle Reservation Application ARIBO Mobile

DTIC Science & Technology

2017-03-01

ARL-TN-0814 ● MAR 2017 US Army Research Laboratory Usability Study and Heuristic Evaluation of the Applied Robotics for...ARL-TN-0814 ● MAR 2017 US Army Research Laboratory Usability Study and Heuristic Evaluation of the Applied Robotics for...Heuristic Evaluation of the Applied Robotics for Installations and Base Operations (ARIBO) Driverless Vehicle Reservation Application ARIBO Mobile 5a

A new hybrid meta-heuristic algorithm for optimal design of large-scale dome structures

NASA Astrophysics Data System (ADS)

Kaveh, A.; Ilchi Ghazaan, M.

2018-02-01

In this article a hybrid algorithm based on a vibrating particles system (VPS) algorithm, multi-design variable configuration (Multi-DVC) cascade optimization, and an upper bound strategy (UBS) is presented for global optimization of large-scale dome truss structures. The new algorithm is called MDVC-UVPS in which the VPS algorithm acts as the main engine of the algorithm. The VPS algorithm is one of the most recent multi-agent meta-heuristic algorithms mimicking the mechanisms of damped free vibration of single degree of freedom systems. In order to handle a large number of variables, cascade sizing optimization utilizing a series of DVCs is used. Moreover, the UBS is utilized to reduce the computational time. Various dome truss examples are studied to demonstrate the effectiveness and robustness of the proposed method, as compared to some existing structural optimization techniques. The results indicate that the MDVC-UVPS technique is a powerful search and optimization method for optimizing structural engineering problems.

Proteomics Versus Clinical Data and Stochastic Local Search Based Feature Selection for Acute Myeloid Leukemia Patients' Classification.

PubMed

Chebouba, Lokmane; Boughaci, Dalila; Guziolowski, Carito

2018-06-04

The use of data issued from high throughput technologies in drug target problems is widely widespread during the last decades. This study proposes a meta-heuristic framework using stochastic local search (SLS) combined with random forest (RF) where the aim is to specify the most important genes and proteins leading to the best classification of Acute Myeloid Leukemia (AML) patients. First we use a stochastic local search meta-heuristic as a feature selection technique to select the most significant proteins to be used in the classification task step. Then we apply RF to classify new patients into their corresponding classes. The evaluation technique is to run the RF classifier on the training data to get a model. Then, we apply this model on the test data to find the appropriate class. We use as metrics the balanced accuracy (BAC) and the area under the receiver operating characteristic curve (AUROC) to measure the performance of our model. The proposed method is evaluated on the dataset issued from DREAM 9 challenge. The comparison is done with a pure random forest (without feature selection), and with the two best ranked results of the DREAM 9 challenge. We used three types of data: only clinical data, only proteomics data, and finally clinical and proteomics data combined. The numerical results show that the highest scores are obtained when using clinical data alone, and the lowest is obtained when using proteomics data alone. Further, our method succeeds in finding promising results compared to the methods presented in the DREAM challenge.

A Variable-Selection Heuristic for K-Means Clustering.

ERIC Educational Resources Information Center

Brusco, Michael J.; Cradit, J. Dennis

2001-01-01

Presents a variable selection heuristic for nonhierarchical (K-means) cluster analysis based on the adjusted Rand index for measuring cluster recovery. Subjected the heuristic to Monte Carlo testing across more than 2,200 datasets. Results indicate that the heuristic is extremely effective at eliminating masking variables. (SLD)

An engineering approach to automatic programming

NASA Technical Reports Server (NTRS)

Rubin, Stuart H.

1990-01-01

An exploratory study of the automatic generation and optimization of symbolic programs using DECOM - a prototypical requirement specification model implemented in pure LISP was undertaken. It was concluded, on the basis of this study, that symbolic processing languages such as LISP can support a style of programming based upon formal transformation and dependent upon the expression of constraints in an object-oriented environment. Such languages can represent all aspects of the software generation process (including heuristic algorithms for effecting parallel search) as dynamic processes since data and program are represented in a uniform format.

PDDL4J: a planning domain description library for java

NASA Astrophysics Data System (ADS)

Pellier, D.; Fiorino, H.

2018-01-01

PDDL4J (Planning Domain Description Library for Java) is an open source toolkit for Java cross-platform developers meant (1) to provide state-of-the-art planners based on the Pddl language, and (2) to facilitate research works on new planners. In this article, we present an overview of the Automated Planning concepts and languages. We present some planning systems and their most significant applications. Then, we detail the Pddl4j toolkit with an emphasis on the available informative structures, heuristics and search algorithms.

Smart internet search engine through 6W

NASA Astrophysics Data System (ADS)

Goehler, Stephen; Cader, Masud; Szu, Harold

2006-04-01

Current Internet search engine technology is limited in its ability to display necessary relevant information to the user. Yahoo, Google and Microsoft use lookup tables or indexes which limits the ability of users to find their desired information. While these companies have improved their results over the years by enhancing their existing technology and algorithms with specialized heuristics such as PageRank, there is a need for a next generation smart search engine that can effectively interpret the relevance of user searches and provide the actual information requested. This paper explores whether a smarter Internet search engine can effectively fulfill a user's needs through the use of 6W representations.

Trends Concerning Four Misconceptions in Students' Intuitively-Based Probabilistic Reasoning Sourced in the Heuristic of Representativeness

ERIC Educational Resources Information Center

Kustos, Paul Nicholas

2010-01-01

Student difficulty in the study of probability arises in intuitively-based misconceptions derived from heuristics. One such heuristic, the one of note for this research study, is that of representativeness, in which an individual informally assesses the probability of an event based on the degree to which the event is similar to the sample from…

Chinese-English Automation and Computer Technology Dictionary, Volume 2.

DTIC Science & Technology

1980-08-01

The purpose of the series is to provide rapid reference tools for translators, abstractors, and research analysts concerned with scientific and...tansuo search; searching; 25 exploration; explore: research ; hunting; trace; seek tansuo dianxian tracer wire 26 884 tansuxd . ., heuristic 01 tansuofa...xunwen -v Ij ;I system interrogation 22 xitong yanjiu A f IC system research 23 xitong yinqyong chengxiud -A, i M 1 R N- #1 , system utility program 24

Managing search complexity in linguistic geometry.

PubMed

Stilman, B

1997-01-01

This paper is a new step in the development of linguistic geometry. This formal theory is intended to discover and generalize the inner properties of human expert heuristics, which have been successful in a certain class of complex control systems, and apply them to different systems. In this paper, we investigate heuristics extracted in the form of hierarchical networks of planning paths of autonomous agents. Employing linguistic geometry tools the dynamic hierarchy of networks is represented as a hierarchy of formal attribute languages. The main ideas of this methodology are shown in the paper on two pilot examples of the solution of complex optimization problems. The first example is a problem of strategic planning for the air combat, in which concurrent actions of four vehicles are simulated as serial interleaving moves. The second example is a problem of strategic planning for the space comb of eight autonomous vehicles (with interleaving moves) that requires generation of the search tree of the depth 25 with the branching factor 30. This is beyond the capabilities of modern and conceivable future computers (employing conventional approaches). In both examples the linguistic geometry tools showed deep and highly selective searches in comparison with conventional search algorithms. For the first example a sketch of the proof of optimality of the solution is considered.

An extended abstract: A heuristic repair method for constraint-satisfaction and scheduling problems

NASA Technical Reports Server (NTRS)

Minton, Steven; Johnston, Mark D.; Philips, Andrew B.; Laird, Philip

1992-01-01

The work described in this paper was inspired by a surprisingly effective neural network developed for scheduling astronomical observations on the Hubble Space Telescope. Our heuristic constraint satisfaction problem (CSP) method was distilled from an analysis of the network. In the process of carrying out the analysis, we discovered that the effectiveness of the network has little to do with its connectionist implementation. Furthermore, the ideas employed in the network can be implemented very efficiently within a symbolic CSP framework. The symbolic implementation is extremely simple. It also has the advantage that several different search strategies can be employed, although we have found that hill-climbing methods are particularly well-suited for the applications that we have investigated. We begin the paper with a brief review of the neural network. Following this, we describe our symbolic method for heuristic repair.

Using heuristic evaluations to assess the safety of health information systems.

PubMed

Carvalho, Christopher J; Borycki, Elizabeth M; Kushniruk, Andre W

2009-01-01

Health information systems (HISs) are typically seen as a mechanism for reducing medical errors. There is, however, evidence to prove that technology may actually be the cause of errors. As a result, it is crucial to fully test any system prior to its implementation. At present, evidence-based evaluation heuristics do not exist for assessing aspects of interface design that lead to medical errors. A three phase study was conducted to develop evidence-based heuristics for evaluating interfaces. Phase 1 consisted of a systematic review of the literature. In Phase 2 a comprehensive list of 33 evaluation heuristics was developed based on the review that could be used to test for potential technology induced errors. Phase 3 involved applying these healthcare specific heuristics to evaluate a HIS.

Tabu Search enhances network robustness under targeted attacks

NASA Astrophysics Data System (ADS)

Sun, Shi-wen; Ma, Yi-lin; Li, Rui-qi; Wang, Li; Xia, Cheng-yi

2016-03-01

We focus on the optimization of network robustness with respect to intentional attacks on high-degree nodes. Given an existing network, this problem can be considered as a typical single-objective combinatorial optimization problem. Based on the heuristic Tabu Search optimization algorithm, a link-rewiring method is applied to reconstruct the network while keeping the degree of every node unchanged. Through numerical simulations, BA scale-free network and two real-world networks are investigated to verify the effectiveness of the proposed optimization method. Meanwhile, we analyze how the optimization affects other topological properties of the networks, including natural connectivity, clustering coefficient and degree-degree correlation. The current results can help to improve the robustness of existing complex real-world systems, as well as to provide some insights into the design of robust networks.

Using Heuristic Task Analysis to Create Web-Based Instructional Design Theory

ERIC Educational Resources Information Center

Fiester, Herbert R.

2010-01-01

The first purpose of this study was to identify procedural and heuristic knowledge used when creating web-based instruction. The second purpose of this study was to develop suggestions for improving the Heuristic Task Analysis process, a technique for eliciting, analyzing, and representing expertise in cognitively complex tasks. Three expert…

Job shop scheduling problem with late work criterion

NASA Astrophysics Data System (ADS)

Piroozfard, Hamed; Wong, Kuan Yew

2015-05-01

Scheduling is considered as a key task in many industries, such as project based scheduling, crew scheduling, flight scheduling, machine scheduling, etc. In the machine scheduling area, the job shop scheduling problems are considered to be important and highly complex, in which they are characterized as NP-hard. The job shop scheduling problems with late work criterion and non-preemptive jobs are addressed in this paper. Late work criterion is a fairly new objective function. It is a qualitative measure and concerns with late parts of the jobs, unlike classical objective functions that are quantitative measures. In this work, simulated annealing was presented to solve the scheduling problem. In addition, operation based representation was used to encode the solution, and a neighbourhood search structure was employed to search for the new solutions. The case studies are Lawrence instances that were taken from the Operations Research Library. Computational results of this probabilistic meta-heuristic algorithm were compared with a conventional genetic algorithm, and a conclusion was made based on the algorithm and problem.

An Experience Oriented-Convergence Improved Gravitational Search Algorithm for Minimum Variance Distortionless Response Beamforming Optimum

PubMed Central

Darzi, Soodabeh; Tiong, Sieh Kiong; Tariqul Islam, Mohammad; Rezai Soleymanpour, Hassan; Kibria, Salehin

2016-01-01

An experience oriented-convergence improved gravitational search algorithm (ECGSA) based on two new modifications, searching through the best experiments and using of a dynamic gravitational damping coefficient (α), is introduced in this paper. ECGSA saves its best fitness function evaluations and uses those as the agents’ positions in searching process. In this way, the optimal found trajectories are retained and the search starts from these trajectories, which allow the algorithm to avoid the local optimums. Also, the agents can move faster in search space to obtain better exploration during the first stage of the searching process and they can converge rapidly to the optimal solution at the final stage of the search process by means of the proposed dynamic gravitational damping coefficient. The performance of ECGSA has been evaluated by applying it to eight standard benchmark functions along with six complicated composite test functions. It is also applied to adaptive beamforming problem as a practical issue to improve the weight vectors computed by minimum variance distortionless response (MVDR) beamforming technique. The results of implementation of the proposed algorithm are compared with some well-known heuristic methods and verified the proposed method in both reaching to optimal solutions and robustness. PMID:27399904

Heuristic Identification of Biological Architectures for Simulating Complex Hierarchical Genetic Interactions

PubMed Central

Moore, Jason H; Amos, Ryan; Kiralis, Jeff; Andrews, Peter C

2015-01-01

Simulation plays an essential role in the development of new computational and statistical methods for the genetic analysis of complex traits. Most simulations start with a statistical model using methods such as linear or logistic regression that specify the relationship between genotype and phenotype. This is appealing due to its simplicity and because these statistical methods are commonly used in genetic analysis. It is our working hypothesis that simulations need to move beyond simple statistical models to more realistically represent the biological complexity of genetic architecture. The goal of the present study was to develop a prototype genotype–phenotype simulation method and software that are capable of simulating complex genetic effects within the context of a hierarchical biology-based framework. Specifically, our goal is to simulate multilocus epistasis or gene–gene interaction where the genetic variants are organized within the framework of one or more genes, their regulatory regions and other regulatory loci. We introduce here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating data in this manner. This approach combines a biological hierarchy, a flexible mathematical framework, a liability threshold model for defining disease endpoints, and a heuristic search strategy for identifying high-order epistatic models of disease susceptibility. We provide several simulation examples using genetic models exhibiting independent main effects and three-way epistatic effects. PMID:25395175

Simulation-based planning for theater air warfare

NASA Astrophysics Data System (ADS)

Popken, Douglas A.; Cox, Louis A., Jr.

2004-08-01

Planning for Theatre Air Warfare can be represented as a hierarchy of decisions. At the top level, surviving airframes must be assigned to roles (e.g., Air Defense, Counter Air, Close Air Support, and AAF Suppression) in each time period in response to changing enemy air defense capabilities, remaining targets, and roles of opposing aircraft. At the middle level, aircraft are allocated to specific targets to support their assigned roles. At the lowest level, routing and engagement decisions are made for individual missions. The decisions at each level form a set of time-sequenced Courses of Action taken by opposing forces. This paper introduces a set of simulation-based optimization heuristics operating within this planning hierarchy to optimize allocations of aircraft. The algorithms estimate distributions for stochastic outcomes of the pairs of Red/Blue decisions. Rather than using traditional stochastic dynamic programming to determine optimal strategies, we use an innovative combination of heuristics, simulation-optimization, and mathematical programming. Blue decisions are guided by a stochastic hill-climbing search algorithm while Red decisions are found by optimizing over a continuous representation of the decision space. Stochastic outcomes are then provided by fast, Lanchester-type attrition simulations. This paper summarizes preliminary results from top and middle level models.

Performance Engineering as an Expert System.

ERIC Educational Resources Information Center

Harmon, Paul

1984-01-01

Considers three powerful techniques--heuristics, context trees, and search via backward chaining--that a knowledge engineer might employ to develop an expert system to automate performance engineering, i.e., the branch of instructional technology that focuses on the problems of business and industry. (MBR)

Conceptual Information Retrieval.

DTIC Science & Technology

1980-12-01

A240 R I SS BYL U V CZC AM-PAL SKED 8 -18 -------- BY FERNANDO DEL MUNDO MANILA, PHILIPPINES (UPI)-A bomb exploded aboard a Philippine Airlines jetliner...understandinq Natural Lanquaqe, auto - matically organizing and reorqanizinq their memories, and usinq intelliqent heuristics for searching their memories

Tachyon search speeds up retrieval of similar sequences by several orders of magnitude.

PubMed

Tan, Joshua; Kuchibhatla, Durga; Sirota, Fernanda L; Sherman, Westley A; Gattermayer, Tobias; Kwoh, Chia Yee; Eisenhaber, Frank; Schneider, Georg; Maurer-Stroh, Sebastian

2012-06-15

The usage of current sequence search tools becomes increasingly slower as databases of protein sequences continue to grow exponentially. Tachyon, a new algorithm that identifies closely related protein sequences ~200 times faster than standard BLAST, circumvents this limitation with a reduced database and oligopeptide matching heuristic. The tool is publicly accessible as a webserver at http://tachyon.bii.a-star.edu.sg and can also be accessed programmatically through SOAP.

Ranking Information in Networks

NASA Astrophysics Data System (ADS)

Eliassi-Rad, Tina; Henderson, Keith

Given a network, we are interested in ranking sets of nodes that score highest on user-specified criteria. For instance in graphs from bibliographic data (e.g. PubMed), we would like to discover sets of authors with expertise in a wide range of disciplines. We present this ranking task as a Top-K problem; utilize fixed-memory heuristic search; and present performance of both the serial and distributed search algorithms on synthetic and real-world data sets.

An Evolutionary Optimization of the Refueling Simulation for a CANDU Reactor

NASA Astrophysics Data System (ADS)

Do, Q. B.; Choi, H.; Roh, G. H.

2006-10-01

This paper presents a multi-cycle and multi-objective optimization method for the refueling simulation of a 713 MWe Canada deuterium uranium (CANDU-6) reactor based on a genetic algorithm, an elitism strategy and a heuristic rule. The proposed algorithm searches for the optimal refueling patterns for a single cycle that maximizes the average discharge burnup, minimizes the maximum channel power and minimizes the change in the zone controller unit water fills while satisfying the most important safety-related neutronic parameters of the reactor core. The heuristic rule generates an initial population of individuals very close to a feasible solution and it reduces the computing time of the optimization process. The multi-cycle optimization is carried out based on a single cycle refueling simulation. The proposed approach was verified by a refueling simulation of a natural uranium CANDU-6 reactor for an operation period of 6 months at an equilibrium state and compared with the experience-based automatic refueling simulation and the generalized perturbation theory. The comparison has shown that the simulation results are consistent from each other and the proposed approach is a reasonable optimization method of the refueling simulation that controls all the safety-related parameters of the reactor core during the simulation

Heuristics for multiobjective multiple sequence alignment.

PubMed

Abbasi, Maryam; Paquete, Luís; Pereira, Francisco B

2016-07-15

Aligning multiple sequences arises in many tasks in Bioinformatics. However, the alignments produced by the current software packages are highly dependent on the parameters setting, such as the relative importance of opening gaps with respect to the increase of similarity. Choosing only one parameter setting may provide an undesirable bias in further steps of the analysis and give too simplistic interpretations. In this work, we reformulate multiple sequence alignment from a multiobjective point of view. The goal is to generate several sequence alignments that represent a trade-off between maximizing the substitution score and minimizing the number of indels/gaps in the sum-of-pairs score function. This trade-off gives to the practitioner further information about the similarity of the sequences, from which she could analyse and choose the most plausible alignment. We introduce several heuristic approaches, based on local search procedures, that compute a set of sequence alignments, which are representative of the trade-off between the two objectives (substitution score and indels). Several algorithm design options are discussed and analysed, with particular emphasis on the influence of the starting alignment and neighborhood search definitions on the overall performance. A perturbation technique is proposed to improve the local search, which provides a wide range of high-quality alignments. The proposed approach is tested experimentally on a wide range of instances. We performed several experiments with sequences obtained from the benchmark database BAliBASE 3.0. To evaluate the quality of the results, we calculate the hypervolume indicator of the set of score vectors returned by the algorithms. The results obtained allow us to identify reasonably good choices of parameters for our approach. Further, we compared our method in terms of correctly aligned pairs ratio and columns correctly aligned ratio with respect to reference alignments. Experimental results show that our approaches can obtain better results than TCoffee and Clustal Omega in terms of the first ratio.

A multiobjective optimization model and an orthogonal design-based hybrid heuristic algorithm for regional urban mining management problems.

PubMed

Wu, Hao; Wan, Zhong

2018-02-01

In this paper, a multiobjective mixed-integer piecewise nonlinear programming model (MOMIPNLP) is built to formulate the management problem of urban mining system, where the decision variables are associated with buy-back pricing, choices of sites, transportation planning, and adjustment of production capacity. Different from the existing approaches, the social negative effect, generated from structural optimization of the recycling system, is minimized in our model, as well as the total recycling profit and utility from environmental improvement are jointly maximized. For solving the problem, the MOMIPNLP model is first transformed into an ordinary mixed-integer nonlinear programming model by variable substitution such that the piecewise feature of the model is removed. Then, based on technique of orthogonal design, a hybrid heuristic algorithm is developed to find an approximate Pareto-optimal solution, where genetic algorithm is used to optimize the structure of search neighborhood, and both local branching algorithm and relaxation-induced neighborhood search algorithm are employed to cut the searching branches and reduce the number of variables in each branch. Numerical experiments indicate that this algorithm spends less CPU (central processing unit) time in solving large-scale regional urban mining management problems, especially in comparison with the similar ones available in literature. By case study and sensitivity analysis, a number of practical managerial implications are revealed from the model. Since the metal stocks in society are reliable overground mineral sources, urban mining has been paid great attention as emerging strategic resources in an era of resource shortage. By mathematical modeling and development of efficient algorithms, this paper provides decision makers with useful suggestions on the optimal design of recycling system in urban mining. For example, this paper can answer how to encourage enterprises to join the recycling activities by government's support and subsidies, whether the existing recycling system can meet the developmental requirements or not, and what is a reasonable adjustment of production capacity.

Scheduling in the Face of Uncertain Resource Consumption and Utility

NASA Technical Reports Server (NTRS)

Koga, Dennis (Technical Monitor); Frank, Jeremy; Dearden, Richard

2003-01-01

We discuss the problem of scheduling tasks that consume a resource with known capacity and where the tasks have varying utility. We consider problems in which the resource consumption and utility of each activity is described by probability distributions. In these circumstances, we would like to find schedules that exceed a lower bound on the expected utility when executed. We first show that while some of these problems are NP-complete, others are only NP-Hard. We then describe various heuristic search algorithms to solve these problems and their drawbacks. Finally, we present empirical results that characterize the behavior of these heuristics over a variety of problem classes.

Improvements on the minimax algorithm for the Laplace transformation of orbital energy denominators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Helmich-Paris, Benjamin, E-mail: b.helmichparis@vu.nl; Visscher, Lucas, E-mail: l.visscher@vu.nl

2016-09-15

We present a robust and non-heuristic algorithm that finds all extremum points of the error distribution function of numerically Laplace-transformed orbital energy denominators. The extremum point search is one of the two key steps for finding the minimax approximation. If pre-tabulation of initial guesses is supposed to be avoided, strategies for a sufficiently robust algorithm have not been discussed so far. We compare our non-heuristic approach with a bracketing and bisection algorithm and demonstrate that 3 times less function evaluations are required altogether when applying it to typical non-relativistic and relativistic quantum chemical systems.

Two criteria for the selection of assembly plans - Maximizing the flexibility of sequencing the assembly tasks and minimizing the assembly time through parallel execution of assembly tasks

NASA Technical Reports Server (NTRS)

Homem De Mello, Luiz S.; Sanderson, Arthur C.

1991-01-01

The authors introduce two criteria for the evaluation and selection of assembly plans. The first criterion is to maximize the number of different sequences in which the assembly tasks can be executed. The second criterion is to minimize the total assembly time through simultaneous execution of assembly tasks. An algorithm that performs a heuristic search for the best assembly plan over the AND/OR graph representation of assembly plans is discussed. Admissible heuristics for each of the two criteria introduced are presented. Some implementation issues that affect the computational efficiency are addressed.

Use of Heuristics to Facilitate Scientific Discovery Learning in a Simulation Learning Environment in a Physics Domain

ERIC Educational Resources Information Center

Veermans, Koen; van Joolingen, Wouter; de Jong, Ton

2006-01-01

This article describes a study into the role of heuristic support in facilitating discovery learning through simulation-based learning. The study compares the use of two such learning environments in the physics domain of collisions. In one learning environment (implicit heuristics) heuristics are only used to provide the learner with guidance…

Evolutionary squeaky wheel optimization: a new framework for analysis.

PubMed

Li, Jingpeng; Parkes, Andrew J; Burke, Edmund K

2011-01-01

Squeaky wheel optimization (SWO) is a relatively new metaheuristic that has been shown to be effective for many real-world problems. At each iteration SWO does a complete construction of a solution starting from the empty assignment. Although the construction uses information from previous iterations, the complete rebuilding does mean that SWO is generally effective at diversification but can suffer from a relatively weak intensification. Evolutionary SWO (ESWO) is a recent extension to SWO that is designed to improve the intensification by keeping the good components of solutions and only using SWO to reconstruct other poorer components of the solution. In such algorithms a standard challenge is to understand how the various parameters affect the search process. In order to support the future study of such issues, we propose a formal framework for the analysis of ESWO. The framework is based on Markov chains, and the main novelty arises because ESWO moves through the space of partial assignments. This makes it significantly different from the analyses used in local search (such as simulated annealing) which only move through complete assignments. Generally, the exact details of ESWO will depend on various heuristics; so we focus our approach on a case of ESWO that we call ESWO-II and that has probabilistic as opposed to heuristic selection and construction operators. For ESWO-II, we study a simple problem instance and explicitly compute the stationary distribution probability over the states of the search space. We find interesting properties of the distribution. In particular, we find that the probabilities of states generally, but not always, increase with their fitness. This nonmonotonocity is quite different from the monotonicity expected in algorithms such as simulated annealing.

mTM-align: a server for fast protein structure database search and multiple protein structure alignment.

PubMed

Dong, Runze; Pan, Shuo; Peng, Zhenling; Zhang, Yang; Yang, Jianyi

2018-05-21

With the rapid increase of the number of protein structures in the Protein Data Bank, it becomes urgent to develop algorithms for efficient protein structure comparisons. In this article, we present the mTM-align server, which consists of two closely related modules: one for structure database search and the other for multiple structure alignment. The database search is speeded up based on a heuristic algorithm and a hierarchical organization of the structures in the database. The multiple structure alignment is performed using the recently developed algorithm mTM-align. Benchmark tests demonstrate that our algorithms outperform other peering methods for both modules, in terms of speed and accuracy. One of the unique features for the server is the interplay between database search and multiple structure alignment. The server provides service not only for performing fast database search, but also for making accurate multiple structure alignment with the structures found by the search. For the database search, it takes about 2-5 min for a structure of a medium size (∼300 residues). For the multiple structure alignment, it takes a few seconds for ∼10 structures of medium sizes. The server is freely available at: http://yanglab.nankai.edu.cn/mTM-align/.

Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

NASA Technical Reports Server (NTRS)

Cheung, Kar-Ming; Lee, Charles H.

2012-01-01

We developed framework and the mathematical formulation for optimizing communication network using mixed integer programming. The design yields a system that is much smaller, in search space size, when compared to the earlier approach. Our constrained network optimization takes into account the dynamics of link performance within the network along with mission and operation requirements. A unique penalty function is introduced to transform the mixed integer programming into the more manageable problem of searching in a continuous space. The constrained optimization problem was proposed to solve in two stages: first using the heuristic Particle Swarming Optimization algorithm to get a good initial starting point, and then feeding the result into the Sequential Quadratic Programming algorithm to achieve the final optimal schedule. We demonstrate the above planning and scheduling methodology with a scenario of 20 spacecraft and 3 ground stations of a Deep Space Network site. Our approach and framework have been simple and flexible so that problems with larger number of constraints and network can be easily adapted and solved.

An Information-Theoretic-based Evolutionary Approach for the Dynamic Search Path Planning Problem

DTIC Science & Technology

2014-05-01

0 12 ,min TT TT EEE EE (18) where 0E and iTE are the initial system entropy and the expected entropy...xlsp 10,| (8) c z f z zc z f z z p p p p 1 1; 0,0, zz EE (10) Posterior zone z target occupancy belief for an s-success l...heuristics (1 and 2) shown at the end of time horizon T is defined as follows: 21 0 2 0 1 0 21 21 ,min,max TT TT TT TT EEE EEEE gg gg TRIG 21

A Modified Artificial Bee Colony Algorithm for p-Center Problems

PubMed Central

Yurtkuran, Alkın

2014-01-01

The objective of the p-center problem is to locate p-centers on a network such that the maximum of the distances from each node to its nearest center is minimized. The artificial bee colony algorithm is a swarm-based meta-heuristic algorithm that mimics the foraging behavior of honey bee colonies. This study proposes a modified ABC algorithm that benefits from a variety of search strategies to balance exploration and exploitation. Moreover, random key-based coding schemes are used to solve the p-center problem effectively. The proposed algorithm is compared to state-of-the-art techniques using different benchmark problems, and computational results reveal that the proposed approach is very efficient. PMID:24616648

Automatic discovery of optimal classes

NASA Technical Reports Server (NTRS)

Cheeseman, Peter; Stutz, John; Freeman, Don; Self, Matthew

1986-01-01

A criterion, based on Bayes' theorem, is described that defines the optimal set of classes (a classification) for a given set of examples. This criterion is transformed into an equivalent minimum message length criterion with an intuitive information interpretation. This criterion does not require that the number of classes be specified in advance, this is determined by the data. The minimum message length criterion includes the message length required to describe the classes, so there is a built in bias against adding new classes unless they lead to a reduction in the message length required to describe the data. Unfortunately, the search space of possible classifications is too large to search exhaustively, so heuristic search methods, such as simulated annealing, are applied. Tutored learning and probabilistic prediction in particular cases are an important indirect result of optimal class discovery. Extensions to the basic class induction program include the ability to combine category and real value data, hierarchical classes, independent classifications and deciding for each class which attributes are relevant.

Search-based optimization

NASA Technical Reports Server (NTRS)

Wheeler, Ward C.

2003-01-01

The problem of determining the minimum cost hypothetical ancestral sequences for a given cladogram is known to be NP-complete (Wang and Jiang, 1994). Traditionally, point estimations of hypothetical ancestral sequences have been used to gain heuristic, upper bounds on cladogram cost. These include procedures with such diverse approaches as non-additive optimization of multiple sequence alignment, direct optimization (Wheeler, 1996), and fixed-state character optimization (Wheeler, 1999). A method is proposed here which, by extending fixed-state character optimization, replaces the estimation process with a search. This form of optimization examines a diversity of potential state solutions for cost-efficient hypothetical ancestral sequences and can result in greatly more parsimonious cladograms. Additionally, such an approach can be applied to other NP-complete phylogenetic optimization problems such as genomic break-point analysis. c2003 The Willi Hennig Society. Published by Elsevier Science (USA). All rights reserved.

Learning to Search. From Weak Methods to Domain-Specific Heuristics.

DTIC Science & Technology

1984-09-01

move as undesirable. The remaining productions interact with MARKED-BAD, providing the labeling of states it requires for application. One of these, NOTE...to previously visited states, it did not attempt to learn from this knowledge, and simply abandoned dese undesirable pads. From the two remaining...the search strategy that SAGE employs. Many problems (such as winning a chess game ) are so complex that they can only be solved by breaking the task up

Frequency Assignments for HFDF Receivers in a Search and Rescue Network

DTIC Science & Technology

1990-03-01

SAR problem where whether or not a signal is detected by RS or HFDF at the various stations is described by probabilities. Daskin assumes the...allows the problem to be formulated with a linear objective function (6:52-53). Daskin also developed a heuristic solution algorithm to solve this...en CM in o CM CM < I Q < - -.~- -^ * . . . ■ . ,■ . :ST.-.r . 5 Frequency Assignments for HFDF Receivers in a Search and

The Effectiveness of Local Culture-Based Mathematical Heuristic-KR Learning towards Enhancing Student's Creative Thinking Skill

ERIC Educational Resources Information Center

Tandiseru, Selvi Rajuaty

2015-01-01

The problem in this research is the lack of creative thinking skills of students. One of the learning models that is expected to enhance student's creative thinking skill is the local culture-based mathematical heuristic-KR learning model (LC-BMHLM). Heuristic-KR is a learning model which was introduced by Krulik and Rudnick (1995) that is the…

Heuristic decision making in medicine

PubMed Central

Marewski, Julian N.; Gigerenzer, Gerd

2012-01-01

Can less information be more helpful when it comes to making medical decisions? Contrary to the common intuition that more information is always better, the use of heuristics can help both physicians and patients to make sound decisions. Heuristics are simple decision strategies that ignore part of the available information, basing decisions on only a few relevant predictors. We discuss: (i) how doctors and patients use heuristics; and (ii) when heuristics outperform information-greedy methods, such as regressions in medical diagnosis. Furthermore, we outline those features of heuristics that make them useful in health care settings. These features include their surprising accuracy, transparency, and wide accessibility, as well as the low costs and little time required to employ them. We close by explaining one of the statistical reasons why heuristics are accurate, and by pointing to psychiatry as one area for future research on heuristics in health care. PMID:22577307

Heuristic decision making in medicine.

PubMed

Marewski, Julian N; Gigerenzer, Gerd

2012-03-01

Can less information be more helpful when it comes to making medical decisions? Contrary to the common intuition that more information is always better, the use of heuristics can help both physicians and patients to make sound decisions. Heuristics are simple decision strategies that ignore part of the available information, basing decisions on only a few relevant predictors. We discuss: (i) how doctors and patients use heuristics; and (ii) when heuristics outperform information-greedy methods, such as regressions in medical diagnosis. Furthermore, we outline those features of heuristics that make them useful in health care settings. These features include their surprising accuracy, transparency, and wide accessibility, as well as the low costs and little time required to employ them. We close by explaining one of the statistical reasons why heuristics are accurate, and by pointing to psychiatry as one area for future research on heuristics in health care.

Information technology aided exploration of system design spaces

NASA Technical Reports Server (NTRS)

Feather, Martin S.; Kiper, James D.; Kalafat, Selcuk

2004-01-01

We report on a practical application of information technology techniques to aid system engineers effectively explore large design spaces. We make use of heuristic search, visualization and data mining, the combination of which we have implemented wtihin a risk management tool in use at JPL and NASA.

Discovery Systems.

DTIC Science & Technology

1986-04-01

independent regularities - called "concept germs" by Minsky [Min86] and mA 9 "cognitive cliches" by Chapman [ChaB3] - to which are attached batteries of...Dougls B. Lenat. Theory Formation by Heuristic Search. Artificial Intelligence, 21, 1983. [Mns6 Marvin insky. The Society of Mind. Simon and Schuster

Proportional reasoning as a heuristic-based process: time constraint and dual task considerations.

PubMed

Gillard, Ellen; Van Dooren, Wim; Schaeken, Walter; Verschaffel, Lieven

2009-01-01

The present study interprets the overuse of proportional solution methods from a dual process framework. Dual process theories claim that analytic operations involve time-consuming executive processing, whereas heuristic operations are fast and automatic. In two experiments to test whether proportional reasoning is heuristic-based, the participants solved "proportional" problems, for which proportional solution methods provide correct answers, and "nonproportional" problems known to elicit incorrect answers based on the assumption of proportionality. In Experiment 1, the available solution time was restricted. In Experiment 2, the executive resources were burdened with a secondary task. Both manipulations induced an increase in proportional answers and a decrease in correct answers to nonproportional problems. These results support the hypothesis that the choice for proportional methods is heuristic-based.

Accelerating Information Retrieval from Profile Hidden Markov Model Databases.

PubMed

Tamimi, Ahmad; Ashhab, Yaqoub; Tamimi, Hashem

2016-01-01

Profile Hidden Markov Model (Profile-HMM) is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases.

Social biases determine spatiotemporal sparseness of ciliate mating heuristics.

PubMed

Clark, Kevin B

2012-01-01

Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate's initial subjective bias, responsiveness, or preparedness, as defined by Stevens' Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The present findings indicate sparseness performs a similar function in single aneural cells by tuning the size and density of encoded computational architectures useful for decision making in social contexts.

Social biases determine spatiotemporal sparseness of ciliate mating heuristics

PubMed Central

2012-01-01

Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate’s initial subjective bias, responsiveness, or preparedness, as defined by Stevens’ Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The present findings indicate sparseness performs a similar function in single aneural cells by tuning the size and density of encoded computational architectures useful for decision making in social contexts. PMID:22482001

A novel symbiotic organisms search algorithm for congestion management in deregulated environment

NASA Astrophysics Data System (ADS)

Verma, Sumit; Saha, Subhodip; Mukherjee, V.

2017-01-01

In today's competitive electricity market, managing transmission congestion in deregulated power system has created challenges for independent system operators to operate the transmission lines reliably within the limits. This paper proposes a new meta-heuristic algorithm, called as symbiotic organisms search (SOS) algorithm, for congestion management (CM) problem in pool based electricity market by real power rescheduling of generators. Inspired by interactions among organisms in ecosystem, SOS algorithm is a recent population based algorithm which does not require any algorithm specific control parameters unlike other algorithms. Various security constraints such as load bus voltage and line loading are taken into account while dealing with the CM problem. In this paper, the proposed SOS algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results, thus, obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the proposed SOS algorithm for obtaining the higher quality solution is also established.

A novel symbiotic organisms search algorithm for congestion management in deregulated environment

NASA Astrophysics Data System (ADS)

Verma, Sumit; Saha, Subhodip; Mukherjee, V.

2017-01-01

In today's competitive electricity market, managing transmission congestion in deregulated power system has created challenges for independent system operators to operate the transmission lines reliably within the limits. This paper proposes a new meta-heuristic algorithm, called as symbiotic organisms search (SOS) algorithm, for congestion management (CM) problem in pool-based electricity market by real power rescheduling of generators. Inspired by interactions among organisms in ecosystem, SOS algorithm is a recent population-based algorithm which does not require any algorithm specific control parameters unlike other algorithms. Various security constraints such as load bus voltage and line loading are taken into account while dealing with the CM problem. In this paper, the proposed SOS algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results, thus, obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the proposed SOS algorithm for obtaining the higher quality solution is also established.

Estimation of distribution algorithm with path relinking for the blocking flow-shop scheduling problem

NASA Astrophysics Data System (ADS)

Shao, Zhongshi; Pi, Dechang; Shao, Weishi

2018-05-01

This article presents an effective estimation of distribution algorithm, named P-EDA, to solve the blocking flow-shop scheduling problem (BFSP) with the makespan criterion. In the P-EDA, a Nawaz-Enscore-Ham (NEH)-based heuristic and the random method are combined to generate the initial population. Based on several superior individuals provided by a modified linear rank selection, a probabilistic model is constructed to describe the probabilistic distribution of the promising solution space. The path relinking technique is incorporated into EDA to avoid blindness of the search and improve the convergence property. A modified referenced local search is designed to enhance the local exploitation. Moreover, a diversity-maintaining scheme is introduced into EDA to avoid deterioration of the population. Finally, the parameters of the proposed P-EDA are calibrated using a design of experiments approach. Simulation results and comparisons with some well-performing algorithms demonstrate the effectiveness of the P-EDA for solving BFSP.

Autonomous Agents for Dynamic Process Planning in the Flexible Manufacturing System

NASA Astrophysics Data System (ADS)

Nik Nejad, Hossein Tehrani; Sugimura, Nobuhiro; Iwamura, Koji; Tanimizu, Yoshitaka

Rapid changes of market demands and pressures of competition require manufacturers to maintain highly flexible manufacturing systems to cope with a complex manufacturing environment. This paper deals with development of an agent-based architecture of dynamic systems for incremental process planning in the manufacturing systems. In consideration of alternative manufacturing processes and machine tools, the process plans and the schedules of the manufacturing resources are generated incrementally and dynamically. A negotiation protocol is discussed, in this paper, to generate suitable process plans for the target products real-timely and dynamically, based on the alternative manufacturing processes. The alternative manufacturing processes are presented by the process plan networks discussed in the previous paper, and the suitable process plans are searched and generated to cope with both the dynamic changes of the product specifications and the disturbances of the manufacturing resources. We initiatively combine the heuristic search algorithms of the process plan networks with the negotiation protocols, in order to generate suitable process plans in the dynamic manufacturing environment.

Decision-making and problem-solving methods in automation technology

NASA Technical Reports Server (NTRS)

Hankins, W. W.; Pennington, J. E.; Barker, L. K.

1983-01-01

The state of the art in the automation of decision making and problem solving is reviewed. The information upon which the report is based was derived from literature searches, visits to university and government laboratories performing basic research in the area, and a 1980 Langley Research Center sponsored conferences on the subject. It is the contention of the authors that the technology in this area is being generated by research primarily in the three disciplines of Artificial Intelligence, Control Theory, and Operations Research. Under the assumption that the state of the art in decision making and problem solving is reflected in the problems being solved, specific problems and methods of their solution are often discussed to elucidate particular aspects of the subject. Synopses of the following major topic areas comprise most of the report: (1) detection and recognition; (2) planning; and scheduling; (3) learning; (4) theorem proving; (5) distributed systems; (6) knowledge bases; (7) search; (8) heuristics; and (9) evolutionary programming.

Discovery and problem solving: Triangulation as a weak heuristic

NASA Technical Reports Server (NTRS)

Rochowiak, Daniel

1987-01-01

Recently the artificial intelligence community has turned its attention to the process of discovery and found that the history of science is a fertile source for what Darden has called compiled hindsight. Such hindsight generates weak heuristics for discovery that do not guarantee that discoveries will be made but do have proven worth in leading to discoveries. Triangulation is one such heuristic that is grounded in historical hindsight. This heuristic is explored within the general framework of the BACON, GLAUBER, STAHL, DALTON, and SUTTON programs. In triangulation different bases of information are compared in an effort to identify gaps between the bases. Thus, assuming that the bases of information are relevantly related, the gaps that are identified should be good locations for discovery and robust analysis.

Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations.

PubMed

Branke, Jürgen; Hildebrandt, Torsten; Scholz-Reiter, Bernd

2015-01-01

Dispatching rules are frequently used for real-time, online scheduling in complex manufacturing systems. Design of such rules is usually done by experts in a time consuming trial-and-error process. Recently, evolutionary algorithms have been proposed to automate the design process. There are several possibilities to represent rules for this hyper-heuristic search. Because the representation determines the search neighborhood and the complexity of the rules that can be evolved, a suitable choice of representation is key for a successful evolutionary algorithm. In this paper we empirically compare three different representations, both numeric and symbolic, for automated rule design: A linear combination of attributes, a representation based on artificial neural networks, and a tree representation. Using appropriate evolutionary algorithms (CMA-ES for the neural network and linear representations, genetic programming for the tree representation), we empirically investigate the suitability of each representation in a dynamic stochastic job shop scenario. We also examine the robustness of the evolved dispatching rules against variations in the underlying job shop scenario, and visualize what the rules do, in order to get an intuitive understanding of their inner workings. Results indicate that the tree representation using an improved version of genetic programming gives the best results if many candidate rules can be evaluated, closely followed by the neural network representation that already leads to good results for small to moderate computational budgets. The linear representation is found to be competitive only for extremely small computational budgets.

On the predictability of protein database search complexity and its relevance to optimization of distributed searches.

PubMed

Deciu, Cosmin; Sun, Jun; Wall, Mark A

2007-09-01

We discuss several aspects related to load balancing of database search jobs in a distributed computing environment, such as Linux cluster. Load balancing is a technique for making the most of multiple computational resources, which is particularly relevant in environments in which the usage of such resources is very high. The particular case of the Sequest program is considered here, but the general methodology should apply to any similar database search program. We show how the runtimes for Sequest searches of tandem mass spectral data can be predicted from profiles of previous representative searches, and how this information can be used for better load balancing of novel data. A well-known heuristic load balancing method is shown to be applicable to this problem, and its performance is analyzed for a variety of search parameters.

QoE collaborative evaluation method based on fuzzy clustering heuristic algorithm.

PubMed

Bao, Ying; Lei, Weimin; Zhang, Wei; Zhan, Yuzhuo

2016-01-01

At present, to realize or improve the quality of experience (QoE) is a major goal for network media transmission service, and QoE evaluation is the basis for adjusting the transmission control mechanism. Therefore, a kind of QoE collaborative evaluation method based on fuzzy clustering heuristic algorithm is proposed in this paper, which is concentrated on service score calculation at the server side. The server side collects network transmission quality of service (QoS) parameter, node location data, and user expectation value from client feedback information. Then it manages the historical data in database through the "big data" process mode, and predicts user score according to heuristic rules. On this basis, it completes fuzzy clustering analysis, and generates service QoE score and management message, which will be finally fed back to clients. Besides, this paper mainly discussed service evaluation generative rules, heuristic evaluation rules and fuzzy clustering analysis methods, and presents service-based QoE evaluation processes. The simulation experiments have verified the effectiveness of QoE collaborative evaluation method based on fuzzy clustering heuristic rules.

Analytic and heuristic processes in the detection and resolution of conflict.

PubMed

Ferreira, Mário B; Mata, André; Donkin, Christopher; Sherman, Steven J; Ihmels, Max

2016-10-01

Previous research with the ratio-bias task found larger response latencies for conflict trials where the heuristic- and analytic-based responses are assumed to be in opposition (e.g., choosing between 1/10 and 9/100 ratios of success) when compared to no-conflict trials where both processes converge on the same response (e.g., choosing between 1/10 and 11/100). This pattern is consistent with parallel dual-process models, which assume that there is effective, rather than lax, monitoring of the output of heuristic processing. It is, however, unclear why conflict resolution sometimes fails. Ratio-biased choices may increase because of a decline in analytical reasoning (leaving heuristic-based responses unopposed) or to a rise in heuristic processing (making it more difficult for analytic processes to override the heuristic preferences). Using the process-dissociation procedure, we found that instructions to respond logically and response speed affected analytic (controlled) processing (C), leaving heuristic processing (H) unchanged, whereas the intuitive preference for large nominators (as assessed by responses to equal ratio trials) affected H but not C. These findings create new challenges to the debate between dual-process and single-process accounts, which are discussed.

A positional estimation technique for an autonomous land vehicle in an unstructured environment

NASA Technical Reports Server (NTRS)

Talluri, Raj; Aggarwal, J. K.

1990-01-01

This paper presents a solution to the positional estimation problem of an autonomous land vehicle navigating in an unstructured mountainous terrain. A Digital Elevation Map (DEM) of the area in which the robot is to navigate is assumed to be given. It is also assumed that the robot is equipped with a camera that can be panned and tilted, and a device to measure the elevation of the robot above the ground surface. No recognizable landmarks are assumed to be present in the environment in which the robot is to navigate. The solution presented makes use of the DEM information, and structures the problem as a heuristic search in the DEM for the possible robot location. The shape and position of the horizon line in the image plane and the known camera geometry of the perspective projection are used as parameters to search the DEM. Various heuristics drawn from the geometric constraints are used to prune the search space significantly. The algorithm is made robust to errors in the imaging process by accounting for the worst care errors. The approach is tested using DEM data of areas in Colorado and Texas. The method is suitable for use in outdoor mobile robots and planetary rovers.

A Discrete Fruit Fly Optimization Algorithm for the Traveling Salesman Problem.

PubMed

Jiang, Zi-Bin; Yang, Qiong

2016-01-01

The fruit fly optimization algorithm (FOA) is a newly developed bio-inspired algorithm. The continuous variant version of FOA has been proven to be a powerful evolutionary approach to determining the optima of a numerical function on a continuous definition domain. In this study, a discrete FOA (DFOA) is developed and applied to the traveling salesman problem (TSP), a common combinatorial problem. In the DFOA, the TSP tour is represented by an ordering of city indices, and the bio-inspired meta-heuristic search processes are executed with two elaborately designed main procedures: the smelling and tasting processes. In the smelling process, an effective crossover operator is used by the fruit fly group to search for the neighbors of the best-known swarm location. During the tasting process, an edge intersection elimination (EXE) operator is designed to improve the neighbors of the non-optimum food location in order to enhance the exploration performance of the DFOA. In addition, benchmark instances from the TSPLIB are classified in order to test the searching ability of the proposed algorithm. Furthermore, the effectiveness of the proposed DFOA is compared to that of other meta-heuristic algorithms. The results indicate that the proposed DFOA can be effectively used to solve TSPs, especially large-scale problems.

A Discrete Fruit Fly Optimization Algorithm for the Traveling Salesman Problem

PubMed Central

Jiang, Zi-bin; Yang, Qiong

2016-01-01

The fruit fly optimization algorithm (FOA) is a newly developed bio-inspired algorithm. The continuous variant version of FOA has been proven to be a powerful evolutionary approach to determining the optima of a numerical function on a continuous definition domain. In this study, a discrete FOA (DFOA) is developed and applied to the traveling salesman problem (TSP), a common combinatorial problem. In the DFOA, the TSP tour is represented by an ordering of city indices, and the bio-inspired meta-heuristic search processes are executed with two elaborately designed main procedures: the smelling and tasting processes. In the smelling process, an effective crossover operator is used by the fruit fly group to search for the neighbors of the best-known swarm location. During the tasting process, an edge intersection elimination (EXE) operator is designed to improve the neighbors of the non-optimum food location in order to enhance the exploration performance of the DFOA. In addition, benchmark instances from the TSPLIB are classified in order to test the searching ability of the proposed algorithm. Furthermore, the effectiveness of the proposed DFOA is compared to that of other meta-heuristic algorithms. The results indicate that the proposed DFOA can be effectively used to solve TSPs, especially large-scale problems. PMID:27812175

Recursive heuristic classification

NASA Technical Reports Server (NTRS)

Wilkins, David C.

1994-01-01

The author will describe a new problem-solving approach called recursive heuristic classification, whereby a subproblem of heuristic classification is itself formulated and solved by heuristic classification. This allows the construction of more knowledge-intensive classification programs in a way that yields a clean organization. Further, standard knowledge acquisition and learning techniques for heuristic classification can be used to create, refine, and maintain the knowledge base associated with the recursively called classification expert system. The method of recursive heuristic classification was used in the Minerva blackboard shell for heuristic classification. Minerva recursively calls itself every problem-solving cycle to solve the important blackboard scheduler task, which involves assigning a desirability rating to alternative problem-solving actions. Knowing these ratings is critical to the use of an expert system as a component of a critiquing or apprenticeship tutoring system. One innovation of this research is a method called dynamic heuristic classification, which allows selection among dynamically generated classification categories instead of requiring them to be prenumerated.

Restarting and recentering genetic algorithm variations for DNA fragment assembly: The necessity of a multi-strategy approach.

PubMed

Hughes, James Alexander; Houghten, Sheridan; Ashlock, Daniel

2016-12-01

DNA Fragment assembly - an NP-Hard problem - is one of the major steps in of DNA sequencing. Multiple strategies have been used for this problem, including greedy graph-based algorithms, deBruijn graphs, and the overlap-layout-consensus approach. This study focuses on the overlap-layout-consensus approach. Heuristics and computational intelligence methods are combined to exploit their respective benefits. These algorithm combinations were able to produce high quality results surpassing the best results obtained by a number of competitive algorithms specially designed and tuned for this problem on thirteen of sixteen popular benchmarks. This work also reinforces the necessity of using multiple search strategies as it is clearly observed that algorithm performance is dependent on problem instance; without a deeper look into many searches, top solutions could be missed entirely. Copyright © 2016. Published by Elsevier Ireland Ltd.

Solving optimization problems by the public goods game

NASA Astrophysics Data System (ADS)

Javarone, Marco Alberto

2017-09-01

We introduce a method based on the Public Goods Game for solving optimization tasks. In particular, we focus on the Traveling Salesman Problem, i.e. a NP-hard problem whose search space exponentially grows increasing the number of cities. The proposed method considers a population whose agents are provided with a random solution to the given problem. In doing so, agents interact by playing the Public Goods Game using the fitness of their solution as currency of the game. Notably, agents with better solutions provide higher contributions, while those with lower ones tend to imitate the solution of richer agents for increasing their fitness. Numerical simulations show that the proposed method allows to compute exact solutions, and suboptimal ones, in the considered search spaces. As result, beyond to propose a new heuristic for combinatorial optimization problems, our work aims to highlight the potentiality of evolutionary game theory beyond its current horizons.

Clinical decision-making by midwives: managing case complexity.

PubMed

Cioffi, J; Markham, R

1997-02-01

In making clinical judgements, it is argued that midwives use 'shortcuts' or heuristics based on estimated probabilities to simplify the decision-making task. Midwives (n = 30) were given simulated patient assessment situations of high and low complexity and were required to think aloud. Analysis of verbal protocols showed that subjective probability judgements (heuristics) were used more frequently in the high than low complexity case and predominated in the last quarter of the assessment period for the high complexity case. 'Representativeness' was identified more frequently in the high than in the low case, but was the dominant heuristic in both. Reports completed after each simulation suggest that heuristics based on memory for particular conditions affect decisions. It is concluded that midwives use heuristics, derived mainly from their clinical experiences, in an attempt to save cognitive effort and to facilitate reasonably accurate decisions in the decision-making process.

Heuristic Processes in Ratings of Leader Behavior: Assessing Item-Induced Availability Biases.

ERIC Educational Resources Information Center

Binning, John F.; Fernandez, Guadalupe

Since observers' memory-based ratings of organizational phenomena provide data in research and decision-making contexts, bias in observers' judgments must be examined. A study was conducted to explore the extent to which leader behavior ratings are more generally biased by the availability heuristic. The availability heuristic is operative when a…

The Generation and Resemblance Heuristics in Face Recognition: Cooperation and Competition

ERIC Educational Resources Information Center

Kleider, Heather M.; Goldinger, Stephen D.

2006-01-01

Like all probabilistic decisions, recognition memory judgments are based on inferences about the strength and quality of stimulus familiarity. In recent articles, B. W. A. Whittlesea and J. Leboe (2000; J. Leboe & B. W. A. Whittlesea, 2002) proposed that such memory decisions entail various heuristics, similar to well-known heuristics in overt…

Automated analysis of complex data

NASA Technical Reports Server (NTRS)

Saintamant, Robert; Cohen, Paul R.

1994-01-01

We have examined some of the issues involved in automating exploratory data analysis, in particular the tradeoff between control and opportunism. We have proposed an opportunistic planning solution for this tradeoff, and we have implemented a prototype, Igor, to test the approach. Our experience in developing Igor was surprisingly smooth. In contrast to earlier versions that relied on rule representation, it was straightforward to increment Igor's knowledge base without causing the search space to explode. The planning representation appears to be both general and powerful, with high level strategic knowledge provided by goals and plans, and the hooks for domain-specific knowledge are provided by monitors and focusing heuristics.

Discrete Bat Algorithm for Optimal Problem of Permutation Flow Shop Scheduling

PubMed Central

Luo, Qifang; Zhou, Yongquan; Xie, Jian; Ma, Mingzhi; Li, Liangliang

2014-01-01

A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem. PMID:25243220

Discrete bat algorithm for optimal problem of permutation flow shop scheduling.

PubMed

Luo, Qifang; Zhou, Yongquan; Xie, Jian; Ma, Mingzhi; Li, Liangliang

2014-01-01

A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem.

Path Planning Method in Multi-obstacle Marine Environment

NASA Astrophysics Data System (ADS)

Zhang, Jinpeng; Sun, Hanxv

2017-12-01

In this paper, an improved algorithm for particle swarm optimization is proposed for the application of underwater robot in the complex marine environment. Not only did consider to avoid obstacles when path planning, but also considered the current direction and the size effect on the performance of the robot dynamics. The algorithm uses the trunk binary tree structure to construct the path search space and A * heuristic search method is used in the search space to find a evaluation standard path. Then the particle swarm algorithm to optimize the path by adjusting evaluation function, which makes the underwater robot in the current navigation easier to control, and consume less energy.

Comparing the performance of expert user heuristics and an integer linear program in aircraft carrier deck operations.

PubMed

Ryan, Jason C; Banerjee, Ashis Gopal; Cummings, Mary L; Roy, Nicholas

2014-06-01

Planning operations across a number of domains can be considered as resource allocation problems with timing constraints. An unexplored instance of such a problem domain is the aircraft carrier flight deck, where, in current operations, replanning is done without the aid of any computerized decision support. Rather, veteran operators employ a set of experience-based heuristics to quickly generate new operating schedules. These expert user heuristics are neither codified nor evaluated by the United States Navy; they have grown solely from the convergent experiences of supervisory staff. As unmanned aerial vehicles (UAVs) are introduced in the aircraft carrier domain, these heuristics may require alterations due to differing capabilities. The inclusion of UAVs also allows for new opportunities for on-line planning and control, providing an alternative to the current heuristic-based replanning methodology. To investigate these issues formally, we have developed a decision support system for flight deck operations that utilizes a conventional integer linear program-based planning algorithm. In this system, a human operator sets both the goals and constraints for the algorithm, which then returns a proposed schedule for operator approval. As a part of validating this system, the performance of this collaborative human-automation planner was compared with that of the expert user heuristics over a set of test scenarios. The resulting analysis shows that human heuristics often outperform the plans produced by an optimization algorithm, but are also often more conservative.

Ambush frequency should increase over time during optimal predator search for prey

PubMed Central

Alpern, Steve; Fokkink, Robbert; Timmer, Marco; Casas, Jérôme

2011-01-01

We advance and apply the mathematical theory of search games to model the problem faced by a predator searching for prey. Two search modes are available: ambush and cruising search. Some species can adopt either mode, with their choice at a given time traditionally explained in terms of varying habitat and physiological conditions. We present an additional explanation of the observed predator alternation between these search modes, which is based on the dynamical nature of the search game they are playing: the possibility of ambush decreases the propensity of the prey to frequently change locations and thereby renders it more susceptible to the systematic cruising search portion of the strategy. This heuristic explanation is supported by showing that in a new idealized search game where the predator is allowed to ambush or search at any time, and the prey can change locations at intermittent times, optimal predator play requires an alternation (or mixture) over time of ambush and cruise search. Thus, our game is an extension of the well-studied ‘Princess and Monster’ search game. Search games are zero sum games, where the pay-off is the capture time and neither the Searcher nor the Hider knows the location of the other. We are able to determine the optimal mixture of the search modes when the predator uses a mixture which is constant over time, and also to determine how the mode mixture changes over time when dynamic strategies are allowed (the ambush probability increases over time). In particular, we establish the ‘square root law of search predation’: the optimal proportion of active search equals the square root of the fraction of the region that has not yet been explored. PMID:21571944

Ambush frequency should increase over time during optimal predator search for prey.

PubMed

Alpern, Steve; Fokkink, Robbert; Timmer, Marco; Casas, Jérôme

2011-11-07

We advance and apply the mathematical theory of search games to model the problem faced by a predator searching for prey. Two search modes are available: ambush and cruising search. Some species can adopt either mode, with their choice at a given time traditionally explained in terms of varying habitat and physiological conditions. We present an additional explanation of the observed predator alternation between these search modes, which is based on the dynamical nature of the search game they are playing: the possibility of ambush decreases the propensity of the prey to frequently change locations and thereby renders it more susceptible to the systematic cruising search portion of the strategy. This heuristic explanation is supported by showing that in a new idealized search game where the predator is allowed to ambush or search at any time, and the prey can change locations at intermittent times, optimal predator play requires an alternation (or mixture) over time of ambush and cruise search. Thus, our game is an extension of the well-studied 'Princess and Monster' search game. Search games are zero sum games, where the pay-off is the capture time and neither the Searcher nor the Hider knows the location of the other. We are able to determine the optimal mixture of the search modes when the predator uses a mixture which is constant over time, and also to determine how the mode mixture changes over time when dynamic strategies are allowed (the ambush probability increases over time). In particular, we establish the 'square root law of search predation': the optimal proportion of active search equals the square root of the fraction of the region that has not yet been explored.

Optimization Techniques for Analysis of Biological and Social Networks

DTIC Science & Technology

2012-03-28

analyzing a new metaheuristic technique, variable objective search. 3. Experimentation and application: Implement the proposed algorithms , test and fine...alternative mathematical programming formulations, their theoretical analysis, the development of exact algorithms , and heuristics. Originally, clusters...systematic fashion under a unifying theoretical and algorithmic framework. Optimization, Complex Networks, Social Network Analysis, Computational

Ant system: optimization by a colony of cooperating agents.

PubMed

Dorigo, M; Maniezzo, V; Colorni, A

1996-01-01

An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.

New insights into diversification of hyper-heuristics.

PubMed

Ren, Zhilei; Jiang, He; Xuan, Jifeng; Hu, Yan; Luo, Zhongxuan

2014-10-01

There has been a growing research trend of applying hyper-heuristics for problem solving, due to their ability of balancing the intensification and the diversification with low level heuristics. Traditionally, the diversification mechanism is mostly realized by perturbing the incumbent solutions to escape from local optima. In this paper, we report our attempt toward providing a new diversification mechanism, which is based on the concept of instance perturbation. In contrast to existing approaches, the proposed mechanism achieves the diversification by perturbing the instance under solving, rather than the solutions. To tackle the challenge of incorporating instance perturbation into hyper-heuristics, we also design a new hyper-heuristic framework HIP-HOP (recursive acronym of HIP-HOP is an instance perturbation-based hyper-heuristic optimization procedure), which employs a grammar guided high level strategy to manipulate the low level heuristics. With the expressive power of the grammar, the constraints, such as the feasibility of the output solution could be easily satisfied. Numerical results and statistical tests over both the Ising spin glass problem and the p -median problem instances show that HIP-HOP is able to achieve promising performances. Furthermore, runtime distribution analysis reveals that, although being relatively slow at the beginning, HIP-HOP is able to achieve competitive solutions once given sufficient time.

An Elitist Multiobjective Tabu Search for Optimal Design of Groundwater Remediation Systems.

PubMed

Yang, Yun; Wu, Jianfeng; Wang, Jinguo; Zhou, Zhifang

2017-11-01

This study presents a new multiobjective evolutionary algorithm (MOEA), the elitist multiobjective tabu search (EMOTS), and incorporates it with MODFLOW/MT3DMS to develop a groundwater simulation-optimization (SO) framework based on modular design for optimal design of groundwater remediation systems using pump-and-treat (PAT) technique. The most notable improvement of EMOTS over the original multiple objective tabu search (MOTS) lies in the elitist strategy, selection strategy, and neighborhood move rule. The elitist strategy is to maintain all nondominated solutions within later search process for better converging to the true Pareto front. The elitism-based selection operator is modified to choose two most remote solutions from current candidate list as seed solutions to increase the diversity of searching space. Moreover, neighborhood solutions are uniformly generated using the Latin hypercube sampling (LHS) in the bounded neighborhood space around each seed solution. To demonstrate the performance of the EMOTS, we consider a synthetic groundwater remediation example. Problem formulations consist of two objective functions with continuous decision variables of pumping rates while meeting water quality requirements. Especially, sensitivity analysis is evaluated through the synthetic case for determination of optimal combination of the heuristic parameters. Furthermore, the EMOTS is successfully applied to evaluate remediation options at the field site of the Massachusetts Military Reservation (MMR) in Cape Cod, Massachusetts. With both the hypothetical and the large-scale field remediation sites, the EMOTS-based SO framework is demonstrated to outperform the original MOTS in achieving the performance metrics of optimality and diversity of nondominated frontiers with desirable stability and robustness. © 2017, National Ground Water Association.

Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets.

PubMed

Li, Jinyan; Fong, Simon; Wong, Raymond K; Millham, Richard; Wong, Kelvin K L

2017-06-28

Due to the high-dimensional characteristics of dataset, we propose a new method based on the Wolf Search Algorithm (WSA) for optimising the feature selection problem. The proposed approach uses the natural strategy established by Charles Darwin; that is, 'It is not the strongest of the species that survives, but the most adaptable'. This means that in the evolution of a swarm, the elitists are motivated to quickly obtain more and better resources. The memory function helps the proposed method to avoid repeat searches for the worst position in order to enhance the effectiveness of the search, while the binary strategy simplifies the feature selection problem into a similar problem of function optimisation. Furthermore, the wrapper strategy gathers these strengthened wolves with the classifier of extreme learning machine to find a sub-dataset with a reasonable number of features that offers the maximum correctness of global classification models. The experimental results from the six public high-dimensional bioinformatics datasets tested demonstrate that the proposed method can best some of the conventional feature selection methods up to 29% in classification accuracy, and outperform previous WSAs by up to 99.81% in computational time.

Indoor Trajectory Tracking Scheme Based on Delaunay Triangulation and Heuristic Information in Wireless Sensor Networks.

PubMed

Qin, Junping; Sun, Shiwen; Deng, Qingxu; Liu, Limin; Tian, Yonghong

2017-06-02

Object tracking and detection is one of the most significant research areas for wireless sensor networks. Existing indoor trajectory tracking schemes in wireless sensor networks are based on continuous localization and moving object data mining. Indoor trajectory tracking based on the received signal strength indicator ( RSSI ) has received increased attention because it has low cost and requires no special infrastructure. However, RSSI tracking introduces uncertainty because of the inaccuracies of measurement instruments and the irregularities (unstable, multipath, diffraction) of wireless signal transmissions in indoor environments. Heuristic information includes some key factors for trajectory tracking procedures. This paper proposes a novel trajectory tracking scheme based on Delaunay triangulation and heuristic information (TTDH). In this scheme, the entire field is divided into a series of triangular regions. The common side of adjacent triangular regions is regarded as a regional boundary. Our scheme detects heuristic information related to a moving object's trajectory, including boundaries and triangular regions. Then, the trajectory is formed by means of a dynamic time-warping position-fingerprint-matching algorithm with heuristic information constraints. Field experiments show that the average error distance of our scheme is less than 1.5 m, and that error does not accumulate among the regions.

Utility functions and resource management in an oversubscribed heterogeneous computing environment

DOE PAGES

Khemka, Bhavesh; Friese, Ryan; Briceno, Luis Diego; ...

2014-09-26

We model an oversubscribed heterogeneous computing system where tasks arrive dynamically and a scheduler maps the tasks to machines for execution. The environment and workloads are based on those being investigated by the Extreme Scale Systems Center at Oak Ridge National Laboratory. Utility functions that are designed based on specifications from the system owner and users are used to create a metric for the performance of resource allocation heuristics. Each task has a time-varying utility (importance) that the enterprise will earn based on when the task successfully completes execution. We design multiple heuristics, which include a technique to drop lowmore » utility-earning tasks, to maximize the total utility that can be earned by completing tasks. The heuristics are evaluated using simulation experiments with two levels of oversubscription. The results show the benefit of having fast heuristics that account for the importance of a task and the heterogeneity of the environment when making allocation decisions in an oversubscribed environment. Furthermore, the ability to drop low utility-earning tasks allow the heuristics to tolerate the high oversubscription as well as earn significant utility.« less

Optimizing Controlling-Value-Based Power Gating with Gate Count and Switching Activity

NASA Astrophysics Data System (ADS)

Chen, Lei; Kimura, Shinji

In this paper, a new heuristic algorithm is proposed to optimize the power domain clustering in controlling-value-based (CV-based) power gating technology. In this algorithm, both the switching activity of sleep signals (p) and the overall numbers of sleep gates (gate count, N) are considered, and the sum of the product of p and N is optimized. The algorithm effectively exerts the total power reduction obtained from the CV-based power gating. Even when the maximum depth is kept to be the same, the proposed algorithm can still achieve power reduction approximately 10% more than that of the prior algorithms. Furthermore, detailed comparison between the proposed heuristic algorithm and other possible heuristic algorithms are also presented. HSPICE simulation results show that over 26% of total power reduction can be obtained by using the new heuristic algorithm. In addition, the effect of dynamic power reduction through the CV-based power gating method and the delay overhead caused by the switching of sleep transistors are also shown in this paper.

An impatient evolutionary algorithm with probabilistic tabu search for unified solution of some NP-hard problems in graph and set theory via clique finding.

PubMed

Guturu, Parthasarathy; Dantu, Ram

2008-06-01

Many graph- and set-theoretic problems, because of their tremendous application potential and theoretical appeal, have been well investigated by the researchers in complexity theory and were found to be NP-hard. Since the combinatorial complexity of these problems does not permit exhaustive searches for optimal solutions, only near-optimal solutions can be explored using either various problem-specific heuristic strategies or metaheuristic global-optimization methods, such as simulated annealing, genetic algorithms, etc. In this paper, we propose a unified evolutionary algorithm (EA) to the problems of maximum clique finding, maximum independent set, minimum vertex cover, subgraph and double subgraph isomorphism, set packing, set partitioning, and set cover. In the proposed approach, we first map these problems onto the maximum clique-finding problem (MCP), which is later solved using an evolutionary strategy. The proposed impatient EA with probabilistic tabu search (IEA-PTS) for the MCP integrates the best features of earlier successful approaches with a number of new heuristics that we developed to yield a performance that advances the state of the art in EAs for the exploration of the maximum cliques in a graph. Results of experimentation with the 37 DIMACS benchmark graphs and comparative analyses with six state-of-the-art algorithms, including two from the smaller EA community and four from the larger metaheuristics community, indicate that the IEA-PTS outperforms the EAs with respect to a Pareto-lexicographic ranking criterion and offers competitive performance on some graph instances when individually compared to the other heuristic algorithms. It has also successfully set a new benchmark on one graph instance. On another benchmark suite called Benchmarks with Hidden Optimal Solutions, IEA-PTS ranks second, after a very recent algorithm called COVER, among its peers that have experimented with this suite.

Managing Heuristics as a Method of Inquiry in Autobiographical Graphic Design Theses

ERIC Educational Resources Information Center

Ings, Welby

2011-01-01

This article draws on case studies undertaken in postgraduate research at AUT University, Auckland. It seeks to address a number of issues related to heuristic inquiries employed by graphic design students who use autobiographical approaches when developing research-based theses. For this type of thesis, heuristics as a system of inquiry may…

Multiagent optimization system for solving the traveling salesman problem (TSP).

PubMed

Xie, Xiao-Feng; Liu, Jiming

2009-04-01

The multiagent optimization system (MAOS) is a nature-inspired method, which supports cooperative search by the self-organization of a group of compact agents situated in an environment with certain sharing public knowledge. Moreover, each agent in MAOS is an autonomous entity with personal declarative memory and behavioral components. In this paper, MAOS is refined for solving the traveling salesman problem (TSP), which is a classic hard computational problem. Based on a simplified MAOS version, in which each agent manipulates on extremely limited declarative knowledge, some simple and efficient components for solving TSP, including two improving heuristics based on a generalized edge assembly recombination, are implemented. Compared with metaheuristics in adaptive memory programming, MAOS is particularly suitable for supporting cooperative search. The experimental results on two TSP benchmark data sets show that MAOS is competitive as compared with some state-of-the-art algorithms, including the Lin-Kernighan-Helsgaun, IBGLK, PHGA, etc., although MAOS does not use any explicit local search during the runtime. The contributions of MAOS components are investigated. It indicates that certain clues can be positive for making suitable selections before time-consuming computation. More importantly, it shows that the cooperative search of agents can achieve an overall good performance with a macro rule in the switch mode, which deploys certain alternate search rules with the offline performance in negative correlations. Using simple alternate rules may prevent the high difficulty of seeking an omnipotent rule that is efficient for a large data set.

Requirements analysis, domain knowledge, and design

NASA Technical Reports Server (NTRS)

Potts, Colin

1988-01-01

Two improvements to current requirements analysis practices are suggested: domain modeling, and the systematic application of analysis heuristics. Domain modeling is the representation of relevant application knowledge prior to requirements specification. Artificial intelligence techniques may eventually be applicable for domain modeling. In the short term, however, restricted domain modeling techniques, such as that in JSD, will still be of practical benefit. Analysis heuristics are standard patterns of reasoning about the requirements. They usually generate questions of clarification or issues relating to completeness. Analysis heuristics can be represented and therefore systematically applied in an issue-based framework. This is illustrated by an issue-based analysis of JSD's domain modeling and functional specification heuristics. They are discussed in the context of the preliminary design of simple embedded systems.

Evaluating a Web-Based Interface for Internet Telemedicine

NASA Technical Reports Server (NTRS)

Lathan, Corinna E.; Newman, Dava J.; Sebrechts, Marc M.; Doarn, Charles R.

1997-01-01

The objective is to introduce the usability engineering methodology, heuristic evaluation, to the design and development of a web-based telemedicine system. Using a set of usability criteria, or heuristics, one evaluator examined the Spacebridge to Russia web-site for usability problems. Thirty-four usability problems were found in this preliminary study and all were assigned a severity rating. The value of heuristic analysis in the iterative design of a system is shown because the problems can be fixed before deployment of a system and the problems are of a different nature than those found by actual users of the system. It was therefore determined that there is potential value of heuristic evaluation paired with user testing as a strategy for optimal system performance design.

New optimization model for routing and spectrum assignment with nodes insecurity

NASA Astrophysics Data System (ADS)

Xuan, Hejun; Wang, Yuping; Xu, Zhanqi; Hao, Shanshan; Wang, Xiaoli

2017-04-01

By adopting the orthogonal frequency division multiplexing technology, elastic optical networks can provide the flexible and variable bandwidth allocation to each connection request and get higher spectrum utilization. The routing and spectrum assignment problem in elastic optical network is a well-known NP-hard problem. In addition, information security has received worldwide attention. We combine these two problems to investigate the routing and spectrum assignment problem with the guaranteed security in elastic optical network, and establish a new optimization model to minimize the maximum index of the used frequency slots, which is used to determine an optimal routing and spectrum assignment schemes. To solve the model effectively, a hybrid genetic algorithm framework integrating a heuristic algorithm into a genetic algorithm is proposed. The heuristic algorithm is first used to sort the connection requests and then the genetic algorithm is designed to look for an optimal routing and spectrum assignment scheme. In the genetic algorithm, tailor-made crossover, mutation and local search operators are designed. Moreover, simulation experiments are conducted with three heuristic strategies, and the experimental results indicate that the effectiveness of the proposed model and algorithm framework.

Inhibitory mechanism of the matching heuristic in syllogistic reasoning.

PubMed

Tse, Ping Ping; Moreno Ríos, Sergio; García-Madruga, Juan Antonio; Bajo Molina, María Teresa

2014-11-01

A number of heuristic-based hypotheses have been proposed to explain how people solve syllogisms with automatic processes. In particular, the matching heuristic employs the congruency of the quantifiers in a syllogism—by matching the quantifier of the conclusion with those of the two premises. When the heuristic leads to an invalid conclusion, successful solving of these conflict problems requires the inhibition of automatic heuristic processing. Accordingly, if the automatic processing were based on processing the set of quantifiers, no semantic contents would be inhibited. The mental model theory, however, suggests that people reason using mental models, which always involves semantic processing. Therefore, whatever inhibition occurs in the processing implies the inhibition of the semantic contents. We manipulated the validity of the syllogism and the congruency of the quantifier of its conclusion with those of the two premises according to the matching heuristic. A subsequent lexical decision task (LDT) with related words in the conclusion was used to test any inhibition of the semantic contents after each syllogistic evaluation trial. In the LDT, the facilitation effect of semantic priming diminished after correctly solved conflict syllogisms (match-invalid or mismatch-valid), but was intact after no-conflict syllogisms. The results suggest the involvement of an inhibitory mechanism of semantic contents in syllogistic reasoning when there is a conflict between the output of the syntactic heuristic and actual validity. Our results do not support a uniquely syntactic process of syllogistic reasoning but fit with the predictions based on mental model theory. Copyright © 2014 Elsevier B.V. All rights reserved.

Dynamic Resource Management for Parallel Tasks in an Oversubscribed Energy-Constrained Heterogeneous Environment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Imam, Neena; Koenig, Gregory A; Machovec, Dylan

2016-01-01

Abstract: The worth of completing parallel tasks is modeled using utility functions, which monotonically-decrease with time and represent the importance and urgency of a task. These functions define the utility earned by a task at the time of its completion. The performance of such a system is measured as the total utility earned by all completed tasks over some interval of time (e.g., 24 hours). To maximize system performance when scheduling dynamically arriving parallel tasks onto a high performance computing (HPC) system that is oversubscribed and energy-constrained, we have designed, analyzed, and compared different heuristic techniques. Four utility-aware heuristics (i.e.,more » Max Utility, Max Utility-per-Time, Max Utility-per-Resource, and Max Utility-per-Energy), three FCFS-based heuristics (Conservative Backfilling, EASY Backfilling, and FCFS with Multiple Queues), and a Random heuristic were examined in this study. A technique that is often used with the FCFS-based heuristics is the concept of a permanent reservation. We compare the performance of permanent reservations with temporary place-holders to demonstrate the advantages that place-holders can provide. We also present a novel energy filtering technique that constrains the maximum energy-per-resource used by each task. We conducted a simulation study to evaluate the performance of these heuristics and techniques in an energy-constrained oversubscribed HPC environment. With place-holders, energy filtering, and dropping tasks with low potential utility, our utility-aware heuristics are able to significantly outperform the existing FCFS-based techniques.« less

Overcoming Species Boundaries in Peptide Identification with Bayesian Information Criterion-driven Error-tolerant Peptide Search (BICEPS)*

PubMed Central

Renard, Bernhard Y.; Xu, Buote; Kirchner, Marc; Zickmann, Franziska; Winter, Dominic; Korten, Simone; Brattig, Norbert W.; Tzur, Amit; Hamprecht, Fred A.; Steen, Hanno

2012-01-01

Currently, the reliable identification of peptides and proteins is only feasible when thoroughly annotated sequence databases are available. Although sequencing capacities continue to grow, many organisms remain without reliable, fully annotated reference genomes required for proteomic analyses. Standard database search algorithms fail to identify peptides that are not exactly contained in a protein database. De novo searches are generally hindered by their restricted reliability, and current error-tolerant search strategies are limited by global, heuristic tradeoffs between database and spectral information. We propose a Bayesian information criterion-driven error-tolerant peptide search (BICEPS) and offer an open source implementation based on this statistical criterion to automatically balance the information of each single spectrum and the database, while limiting the run time. We show that BICEPS performs as well as current database search algorithms when such algorithms are applied to sequenced organisms, whereas BICEPS only uses a remotely related organism database. For instance, we use a chicken instead of a human database corresponding to an evolutionary distance of more than 300 million years (International Chicken Genome Sequencing Consortium (2004) Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature 432, 695–716). We demonstrate the successful application to cross-species proteomics with a 33% increase in the number of identified proteins for a filarial nematode sample of Litomosoides sigmodontis. PMID:22493179

Search Problems in Mission Planning and Navigation of Autonomous Aircraft. M.S. Thesis

NASA Technical Reports Server (NTRS)

Krozel, James A.

1988-01-01

An architecture for the control of an autonomous aircraft is presented. The architecture is a hierarchical system representing an anthropomorphic breakdown of the control problem into planner, navigator, and pilot systems. The planner system determines high level global plans from overall mission objectives. This abstract mission planning is investigated by focusing on the Traveling Salesman Problem with variations on local and global constraints. Tree search techniques are applied including the breadth first, depth first, and best first algorithms. The minimum-column and row entries for the Traveling Salesman Problem cost matrix provides a powerful heuristic to guide these search techniques. Mission planning subgoals are directed from the planner to the navigator for planning routes in mountainous terrain with threats. Terrain/threat information is abstracted into a graph of possible paths for which graph searches are performed. It is shown that paths can be well represented by a search graph based on the Voronoi diagram of points representing the vertices of mountain boundaries. A comparison of Dijkstra's dynamic programming algorithm and the A* graph search algorithm from artificial intelligence/operations research is performed for several navigation path planning examples. These examples illustrate paths that minimize a combination of distance and exposure to threats. Finally, the pilot system synthesizes the flight trajectory by creating the control commands to fly the aircraft.

Plan-graph Based Heuristics for Conformant Probabilistic Planning

NASA Technical Reports Server (NTRS)

Ramakrishnan, Salesh; Pollack, Martha E.; Smith, David E.

2004-01-01

In this paper, we introduce plan-graph based heuristics to solve a variation of the conformant probabilistic planning (CPP) problem. In many real-world problems, it is the case that the sensors are unreliable or take too many resources to provide knowledge about the environment. These domains are better modeled as conformant planning problems. POMDP based techniques are currently the most successful approach for solving CPP but have the limitation of state- space explosion. Recent advances in deterministic and conformant planning have shown that plan-graphs can be used to enhance the performance significantly. We show that this enhancement can also be translated to CPP. We describe our process for developing the plan-graph heuristics and estimating the probability of a partial plan. We compare the performance of our planner PVHPOP when used with different heuristics. We also perform a comparison with a POMDP solver to show over a order of magnitude improvement in performance.

Does the inherence heuristic take us to psychological essentialism?

PubMed

Marmodoro, Anna; Murphy, Robin A; Baker, A G

2014-10-01

We argue that the claim that essence-based causal explanations emerge, hydra-like, from an inherence heuristic is incomplete. No plausible mechanism for the transition from concrete properties, or cues, to essences is provided. Moreover, the fundamental shotgun and storytelling mechanisms of the inherence heuristic are not clearly enough specified to distinguish them, developmentally, from associative or causal networks.

The Memory State Heuristic: A Formal Model Based on Repeated Recognition Judgments

ERIC Educational Resources Information Center

Castela, Marta; Erdfelder, Edgar

2017-01-01

The recognition heuristic (RH) theory predicts that, in comparative judgment tasks, if one object is recognized and the other is not, the recognized one is chosen. The memory-state heuristic (MSH) extends the RH by assuming that choices are not affected by recognition judgments per se, but by the memory states underlying these judgments (i.e.,…

Heuristic for Critical Machine Based a Lot Streaming for Two-Stage Hybrid Production Environment

NASA Astrophysics Data System (ADS)

Vivek, P.; Saravanan, R.; Chandrasekaran, M.; Pugazhenthi, R.

2017-03-01

Lot streaming in Hybrid flowshop [HFS] is encountered in many real world problems. This paper deals with a heuristic approach for Lot streaming based on critical machine consideration for a two stage Hybrid Flowshop. The first stage has two identical parallel machines and the second stage has only one machine. In the second stage machine is considered as a critical by valid reasons these kind of problems is known as NP hard. A mathematical model developed for the selected problem. The simulation modelling and analysis were carried out in Extend V6 software. The heuristic developed for obtaining optimal lot streaming schedule. The eleven cases of lot streaming were considered. The proposed heuristic was verified and validated by real time simulation experiments. All possible lot streaming strategies and possible sequence under each lot streaming strategy were simulated and examined. The heuristic consistently yielded optimal schedule consistently in all eleven cases. The identification procedure for select best lot streaming strategy was suggested.

Impacts of Learning Inventive Problem-Solving Principles: Students' Transition from Systematic Searching to Heuristic Problem Solving

ERIC Educational Resources Information Center

Barak, Moshe

2013-01-01

This paper presents the outcomes of teaching an inventive problem-solving course in junior high schools in an attempt to deal with the current relative neglect of fostering students' creativity and problem-solving capabilities in traditional schooling. The method involves carrying out systematic manipulation with attributes, functions and…

Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis

ERIC Educational Resources Information Center

Brusco, Michael J.; Singh, Renu; Steinley, Douglas

2009-01-01

The selection of a subset of variables from a pool of candidates is an important problem in several areas of multivariate statistics. Within the context of principal component analysis (PCA), a number of authors have argued that subset selection is crucial for identifying those variables that are required for correct interpretation of the…

Model for the design of distributed data bases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ram, S.

This research focuses on developing a model to solve the File Allocation Problem (FAP). The model integrates two major design issues, namely Concurrently Control and Data Distribution. The central node locking mechanism is incorporated in developing a nonlinear integer programming model. Two solution algorithms are proposed, one of which was implemented in FORTRAN.V. The allocation of data bases and programs are examined using this heuristic. Several decision rules were also formulated based on the results of the heuristic. A second more comprehensive heuristic was proposed, based on the knapsack problem. The development and implementation of this algorithm has been leftmore » as a topic for future research.« less

Knowledge-based segmentation and feature analysis of hand and wrist radiographs

NASA Astrophysics Data System (ADS)

Efford, Nicholas D.

1993-07-01

The segmentation of hand and wrist radiographs for applications such as skeletal maturity assessment is best achieved by model-driven approaches incorporating anatomical knowledge. The reasons for this are discussed, and a particular frame-based or 'blackboard' strategy for the simultaneous segmentation of the hand and estimation of bone age via the TW2 method is described. The new approach is structured for optimum robustness and computational efficiency: features of interest are detected and analyzes in order of their size and prominence in the image, the largest and most distinctive being dealt with first, and the evidence generated by feature analysis is used to update a model of hand anatomy and hence guide later stages of the segmentation. Closed bone boundaries are formed by a hybrid technique combining knowledge-based, one-dimensional edge detection with model-assisted heuristic tree searching.

Amoeba-Inspired Heuristic Search Dynamics for Exploring Chemical Reaction Paths.

PubMed

Aono, Masashi; Wakabayashi, Masamitsu

2015-09-01

We propose a nature-inspired model for simulating chemical reactions in a computationally resource-saving manner. The model was developed by extending our previously proposed heuristic search algorithm, called "AmoebaSAT [Aono et al. 2013]," which was inspired by the spatiotemporal dynamics of a single-celled amoeboid organism that exhibits sophisticated computing capabilities in adapting to its environment efficiently [Zhu et al. 2013]. AmoebaSAT is used for solving an NP-complete combinatorial optimization problem [Garey and Johnson 1979], "the satisfiability problem," and finds a constraint-satisfying solution at a speed that is dramatically faster than one of the conventionally known fastest stochastic local search methods [Iwama and Tamaki 2004] for a class of randomly generated problem instances [ http://www.cs.ubc.ca/~hoos/5/benchm.html ]. In cases where the problem has more than one solution, AmoebaSAT exhibits dynamic transition behavior among a variety of the solutions. Inheriting these features of AmoebaSAT, we formulate "AmoebaChem," which explores a variety of metastable molecules in which several constraints determined by input atoms are satisfied and generates dynamic transition processes among the metastable molecules. AmoebaChem and its developed forms will be applied to the study of the origins of life, to discover reaction paths for which expected or unexpected organic compounds may be formed via unknown unstable intermediates and to estimate the likelihood of each of the discovered paths.

Multi-objective Decision Based Available Transfer Capability in Deregulated Power System Using Heuristic Approaches

NASA Astrophysics Data System (ADS)

Pasam, Gopi Krishna; Manohar, T. Gowri

2016-09-01

Determination of available transfer capability (ATC) requires the use of experience, intuition and exact judgment in order to meet several significant aspects in the deregulated environment. Based on these points, this paper proposes two heuristic approaches to compute ATC. The first proposed heuristic algorithm integrates the five methods known as continuation repeated power flow, repeated optimal power flow, radial basis function neural network, back propagation neural network and adaptive neuro fuzzy inference system to obtain ATC. The second proposed heuristic model is used to obtain multiple ATC values. Out of these, a specific ATC value will be selected based on a number of social, economic, deregulated environmental constraints and related to specific applications like optimization, on-line monitoring, and ATC forecasting known as multi-objective decision based optimal ATC. The validity of results obtained through these proposed methods are scrupulously verified on various buses of the IEEE 24-bus reliable test system. The results presented and derived conclusions in this paper are very useful for planning, operation, maintaining of reliable power in any power system and its monitoring in an on-line environment of deregulated power system. In this way, the proposed heuristic methods would contribute the best possible approach to assess multiple objective ATC using integrated methods.

Usability of a patient education and motivation tool using heuristic evaluation.

PubMed

Joshi, Ashish; Arora, Mohit; Dai, Liwei; Price, Kathleen; Vizer, Lisa; Sears, Andrew

2009-11-06

Computer-mediated educational applications can provide a self-paced, interactive environment to deliver educational content to individuals about their health condition. These programs have been used to deliver health-related information about a variety of topics, including breast cancer screening, asthma management, and injury prevention. We have designed the Patient Education and Motivation Tool (PEMT), an interactive computer-based educational program based on behavioral, cognitive, and humanistic learning theories. The tool is designed to educate users and has three key components: screening, learning, and evaluation. The objective of this tutorial is to illustrate a heuristic evaluation using a computer-based patient education program (PEMT) as a case study. The aims were to improve the usability of PEMT through heuristic evaluation of the interface; to report the results of these usability evaluations; to make changes based on the findings of the usability experts; and to describe the benefits and limitations of applying usability evaluations to PEMT. PEMT was evaluated by three usability experts using Nielsen's usability heuristics while reviewing the interface to produce a list of heuristic violations with severity ratings. The violations were sorted by heuristic and ordered from most to least severe within each heuristic. A total of 127 violations were identified with a median severity of 3 (range 0 to 4 with 0 = no problem to 4 = catastrophic problem). Results showed 13 violations for visibility (median severity = 2), 38 violations for match between system and real world (median severity = 2), 6 violations for user control and freedom (median severity = 3), 34 violations for consistency and standards (median severity = 2), 11 violations for error severity (median severity = 3), 1 violation for recognition and control (median severity = 3), 7 violations for flexibility and efficiency (median severity = 2), 9 violations for aesthetic and minimalist design (median severity = 2), 4 violations for help users recognize, diagnose, and recover from errors (median severity = 3), and 4 violations for help and documentation (median severity = 4). We describe the heuristic evaluation method employed to assess the usability of PEMT, a method which uncovers heuristic violations in the interface design in a quick and efficient manner. Bringing together usability experts and health professionals to evaluate a computer-mediated patient education program can help to identify problems in a timely manner. This makes this method particularly well suited to the iterative design process when developing other computer-mediated health education programs. Heuristic evaluations provided a means to assess the user interface of PEMT.

Usability of a Patient Education and Motivation Tool Using Heuristic Evaluation

PubMed Central

Arora, Mohit; Dai, Liwei; Price, Kathleen; Vizer, Lisa; Sears, Andrew

2009-01-01

Background Computer-mediated educational applications can provide a self-paced, interactive environment to deliver educational content to individuals about their health condition. These programs have been used to deliver health-related information about a variety of topics, including breast cancer screening, asthma management, and injury prevention. We have designed the Patient Education and Motivation Tool (PEMT), an interactive computer-based educational program based on behavioral, cognitive, and humanistic learning theories. The tool is designed to educate users and has three key components: screening, learning, and evaluation. Objective The objective of this tutorial is to illustrate a heuristic evaluation using a computer-based patient education program (PEMT) as a case study. The aims were to improve the usability of PEMT through heuristic evaluation of the interface; to report the results of these usability evaluations; to make changes based on the findings of the usability experts; and to describe the benefits and limitations of applying usability evaluations to PEMT. Methods PEMT was evaluated by three usability experts using Nielsen’s usability heuristics while reviewing the interface to produce a list of heuristic violations with severity ratings. The violations were sorted by heuristic and ordered from most to least severe within each heuristic. Results A total of 127 violations were identified with a median severity of 3 (range 0 to 4 with 0 = no problem to 4 = catastrophic problem). Results showed 13 violations for visibility (median severity = 2), 38 violations for match between system and real world (median severity = 2), 6 violations for user control and freedom (median severity = 3), 34 violations for consistency and standards (median severity = 2), 11 violations for error severity (median severity = 3), 1 violation for recognition and control (median severity = 3), 7 violations for flexibility and efficiency (median severity = 2), 9 violations for aesthetic and minimalist design (median severity = 2), 4 violations for help users recognize, diagnose, and recover from errors (median severity = 3), and 4 violations for help and documentation (median severity = 4). Conclusion We describe the heuristic evaluation method employed to assess the usability of PEMT, a method which uncovers heuristic violations in the interface design in a quick and efficient manner. Bringing together usability experts and health professionals to evaluate a computer-mediated patient education program can help to identify problems in a timely manner. This makes this method particularly well suited to the iterative design process when developing other computer-mediated health education programs. Heuristic evaluations provided a means to assess the user interface of PEMT. PMID:19897458

It looks easy! Heuristics for combinatorial optimization problems.

PubMed

Chronicle, Edward P; MacGregor, James N; Ormerod, Thomas C; Burr, Alistair

2006-04-01

Human performance on instances of computationally intractable optimization problems, such as the travelling salesperson problem (TSP), can be excellent. We have proposed a boundary-following heuristic to account for this finding. We report three experiments with TSPs where the capacity to employ this heuristic was varied. In Experiment 1, participants free to use the heuristic produced solutions significantly closer to optimal than did those prevented from doing so. Experiments 2 and 3 together replicated this finding in larger problems and demonstrated that a potential confound had no effect. In all three experiments, performance was closely matched by a boundary-following model. The results implicate global rather than purely local processes. Humans may have access to simple, perceptually based, heuristics that are suited to some combinatorial optimization tasks.

Space communications scheduler: A rule-based approach to adaptive deadline scheduling

NASA Technical Reports Server (NTRS)

Straguzzi, Nicholas

1990-01-01

Job scheduling is a deceptively complex subfield of computer science. The highly combinatorial nature of the problem, which is NP-complete in nearly all cases, requires a scheduling program to intelligently transverse an immense search tree to create the best possible schedule in a minimal amount of time. In addition, the program must continually make adjustments to the initial schedule when faced with last-minute user requests, cancellations, unexpected device failures, quests, cancellations, unexpected device failures, etc. A good scheduler must be quick, flexible, and efficient, even at the expense of generating slightly less-than-optimal schedules. The Space Communication Scheduler (SCS) is an intelligent rule-based scheduling system. SCS is an adaptive deadline scheduler which allocates modular communications resources to meet an ordered set of user-specified job requests on board the NASA Space Station. SCS uses pattern matching techniques to detect potential conflicts through algorithmic and heuristic means. As a result, the system generates and maintains high density schedules without relying heavily on backtracking or blind search techniques. SCS is suitable for many common real-world applications.

Multiphase Simulated Annealing Based on Boltzmann and Bose-Einstein Distribution Applied to Protein Folding Problem.

PubMed

Frausto-Solis, Juan; Liñán-García, Ernesto; Sánchez-Hernández, Juan Paulo; González-Barbosa, J Javier; González-Flores, Carlos; Castilla-Valdez, Guadalupe

2016-01-01

A new hybrid Multiphase Simulated Annealing Algorithm using Boltzmann and Bose-Einstein distributions (MPSABBE) is proposed. MPSABBE was designed for solving the Protein Folding Problem (PFP) instances. This new approach has four phases: (i) Multiquenching Phase (MQP), (ii) Boltzmann Annealing Phase (BAP), (iii) Bose-Einstein Annealing Phase (BEAP), and (iv) Dynamical Equilibrium Phase (DEP). BAP and BEAP are simulated annealing searching procedures based on Boltzmann and Bose-Einstein distributions, respectively. DEP is also a simulated annealing search procedure, which is applied at the final temperature of the fourth phase, which can be seen as a second Bose-Einstein phase. MQP is a search process that ranges from extremely high to high temperatures, applying a very fast cooling process, and is not very restrictive to accept new solutions. However, BAP and BEAP range from high to low and from low to very low temperatures, respectively. They are more restrictive for accepting new solutions. DEP uses a particular heuristic to detect the stochastic equilibrium by applying a least squares method during its execution. MPSABBE parameters are tuned with an analytical method, which considers the maximal and minimal deterioration of problem instances. MPSABBE was tested with several instances of PFP, showing that the use of both distributions is better than using only the Boltzmann distribution on the classical SA.

A python-based docking program utilizing a receptor bound ligand shape: PythDock.

PubMed

Chung, Jae Yoon; Cho, Seung Joo; Hah, Jung-Mi

2011-09-01

PythDock is a heuristic docking program that uses Python programming language with a simple scoring function and a population based search engine. The scoring function considers electrostatic and dispersion/repulsion terms. The search engine utilizes a particle swarm optimization algorithm. A grid potential map is generated using the shape information of a bound ligand within the active site. Therefore, the searching area is more relevant to the ligand binding. To evaluate the docking performance of PythDock, two well-known docking programs (AutoDock and DOCK) were also used with the same data. The accuracy of docked results were measured by the difference of the ligand structure between x-ray structure, and docked pose, i.e., average root mean squared deviation values of the bound ligand were compared for fourteen protein-ligand complexes. Since the number of ligands' rotational flexibility is an important factor affecting the accuracy of a docking, the data set was chosen to have various degrees of flexibility. Although PythDock has a scoring function simpler than those of other programs (AutoDock and DOCK), our results showed that PythDock predicted more accurate poses than both AutoDock4.2 and DOCK6.2. This indicates that PythDock could be a useful tool to study ligand-receptor interactions and could also be beneficial in structure based drug design.

Characterising bias in regulatory risk and decision analysis: An analysis of heuristics applied in health technology appraisal, chemicals regulation, and climate change governance.

PubMed

MacGillivray, Brian H

2017-08-01

In many environmental and public health domains, heuristic methods of risk and decision analysis must be relied upon, either because problem structures are ambiguous, reliable data is lacking, or decisions are urgent. This introduces an additional source of uncertainty beyond model and measurement error - uncertainty stemming from relying on inexact inference rules. Here we identify and analyse heuristics used to prioritise risk objects, to discriminate between signal and noise, to weight evidence, to construct models, to extrapolate beyond datasets, and to make policy. Some of these heuristics are based on causal generalisations, yet can misfire when these relationships are presumed rather than tested (e.g. surrogates in clinical trials). Others are conventions designed to confer stability to decision analysis, yet which may introduce serious error when applied ritualistically (e.g. significance testing). Some heuristics can be traced back to formal justifications, but only subject to strong assumptions that are often violated in practical applications. Heuristic decision rules (e.g. feasibility rules) in principle act as surrogates for utility maximisation or distributional concerns, yet in practice may neglect costs and benefits, be based on arbitrary thresholds, and be prone to gaming. We highlight the problem of rule-entrenchment, where analytical choices that are in principle contestable are arbitrarily fixed in practice, masking uncertainty and potentially introducing bias. Strategies for making risk and decision analysis more rigorous include: formalising the assumptions and scope conditions under which heuristics should be applied; testing rather than presuming their underlying empirical or theoretical justifications; using sensitivity analysis, simulations, multiple bias analysis, and deductive systems of inference (e.g. directed acyclic graphs) to characterise rule uncertainty and refine heuristics; adopting "recovery schemes" to correct for known biases; and basing decision rules on clearly articulated values and evidence, rather than convention. Copyright © 2017. Published by Elsevier Ltd.

Dynamic Inertia Weight Binary Bat Algorithm with Neighborhood Search

PubMed Central

2017-01-01

Binary bat algorithm (BBA) is a binary version of the bat algorithm (BA). It has been proven that BBA is competitive compared to other binary heuristic algorithms. Since the update processes of velocity in the algorithm are consistent with BA, in some cases, this algorithm also faces the premature convergence problem. This paper proposes an improved binary bat algorithm (IBBA) to solve this problem. To evaluate the performance of IBBA, standard benchmark functions and zero-one knapsack problems have been employed. The numeric results obtained by benchmark functions experiment prove that the proposed approach greatly outperforms the original BBA and binary particle swarm optimization (BPSO). Compared with several other heuristic algorithms on zero-one knapsack problems, it also verifies that the proposed algorithm is more able to avoid local minima. PMID:28634487

Dynamic Inertia Weight Binary Bat Algorithm with Neighborhood Search.

PubMed

Huang, Xingwang; Zeng, Xuewen; Han, Rui

2017-01-01

Binary bat algorithm (BBA) is a binary version of the bat algorithm (BA). It has been proven that BBA is competitive compared to other binary heuristic algorithms. Since the update processes of velocity in the algorithm are consistent with BA, in some cases, this algorithm also faces the premature convergence problem. This paper proposes an improved binary bat algorithm (IBBA) to solve this problem. To evaluate the performance of IBBA, standard benchmark functions and zero-one knapsack problems have been employed. The numeric results obtained by benchmark functions experiment prove that the proposed approach greatly outperforms the original BBA and binary particle swarm optimization (BPSO). Compared with several other heuristic algorithms on zero-one knapsack problems, it also verifies that the proposed algorithm is more able to avoid local minima.

A heuristic approach to worst-case carrier-to-interference ratio maximization in satellite system synthesis

NASA Technical Reports Server (NTRS)

Reilly, Charles H.; Walton, Eric K.; Mata, Fernando; Mount-Campbell, Clark A.; Olen, Carl A.

1990-01-01

Consideration is given to the problem of allotting GEO locations to communication satellites so as to maximize the smallest aggregate carrier-to-interference (C/I) ratio calculated at any test point (assumed earth station). The location allotted to each satellite must be within the satellite's service arc, and angular separation constraints are enforced for each pair of satellites to control single-entry EMI. Solutions to this satellite system synthesis problem (SSSP) are found by embedding two heuristic procedures for the satellite location problem (SLP), in a binary search routine to find an estimate of the largest increment to the angular separation values that permits a feasible solution to SLP and SSSP. Numerical results for a 183-satellite, 208-beam example problem are presented.

FASH: A web application for nucleotides sequence search.

PubMed

Veksler-Lublinksy, Isana; Barash, Danny; Avisar, Chai; Troim, Einav; Chew, Paul; Kedem, Klara

2008-05-27

: FASH (Fourier Alignment Sequence Heuristics) is a web application, based on the Fast Fourier Transform, for finding remote homologs within a long nucleic acid sequence. Given a query sequence and a long text-sequence (e.g, the human genome), FASH detects subsequences within the text that are remotely-similar to the query. FASH offers an alternative approach to Blast/Fasta for querying long RNA/DNA sequences. FASH differs from these other approaches in that it does not depend on the existence of contiguous seed-sequences in its initial detection phase. The FASH web server is user friendly and very easy to operate. FASH can be accessed athttps://fash.bgu.ac.il:8443/fash/default.jsp (secured website).

Operations mission planner beyond the baseline

NASA Technical Reports Server (NTRS)

Biefeld, Eric; Cooper, Lynne

1991-01-01

The scheduling of Space Station Freedom must satisfy four major requirements. It must ensure efficient housekeeping operations, maximize the collection of science, respond to changes in tasking and available resources, and accommodate the above changes in a manner that minimizes disruption of the ongoing operations of the station. While meeting these requirements the scheduler must cope with the complexity, scope, and flexibility of SSF operations. This requires the scheduler to deal with an astronomical number of possible schedules. The Operations Mission Planner (OMP) is centered around minimally disruptive replanning and the use of heuristics limit search in scheduling. OMP has already shown several artificial intelligence based scheduling techniques such as Interleaved Iterative Refinement and Bottleneck Identification using Process Chronologies.

Optimization of Boiling Water Reactor Loading Pattern Using Two-Stage Genetic Algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kobayashi, Yoko; Aiyoshi, Eitaro

2002-10-15

A new two-stage optimization method based on genetic algorithms (GAs) using an if-then heuristic rule was developed to generate optimized boiling water reactor (BWR) loading patterns (LPs). In the first stage, the LP is optimized using an improved GA operator. In the second stage, an exposure-dependent control rod pattern (CRP) is sought using GA with an if-then heuristic rule. The procedure of the improved GA is based on deterministic operators that consist of crossover, mutation, and selection. The handling of the encoding technique and constraint conditions by that GA reflects the peculiar characteristics of the BWR. In addition, strategies suchmore » as elitism and self-reproduction are effectively used in order to improve the search speed. The LP evaluations were performed with a three-dimensional diffusion code that coupled neutronic and thermal-hydraulic models. Strong axial heterogeneities and constraints dependent on three dimensions have always necessitated the use of three-dimensional core simulators for BWRs, so that optimization of computational efficiency is required. The proposed algorithm is demonstrated by successfully generating LPs for an actual BWR plant in two phases. One phase is only LP optimization applying the Haling technique. The other phase is an LP optimization that considers the CRP during reactor operation. In test calculations, candidates that shuffled fresh and burned fuel assemblies within a reasonable computation time were obtained.« less

Design by Analogy: Achieving More Patentable Ideas from One Creative Design

NASA Astrophysics Data System (ADS)

Jia, Li-Zhen; Wu, Chun-Long; Zhu, Xue-Hong; Tan, Run-Hua

2018-12-01

A patent is a kind of technical document to protect intellectual property for individuals or enterprises. Patentable idea generation is a crucial step for patent application and analogy is confirmed to be an effective technique to inspire creative ideas. Analogy-based design usually starts from representation of an analogy source and is followed by the retrieval of appropriate analogs, mapping of design knowledge and adaptation of target solution. To diffuse one core idea into other new contexts and achieve more patentable ideas, this paper mainly centered on the first two stages of analogy-based design and proposed a patentable ideation framework. The analogical information of the source system, including source design problems and solution, was mined comprehensively through International Patent Classification analysis and represented in the form of function, behavior and structure. Three heuristics were suggested for searching the set of candidate target systems with a similar design problem, where the source design could be transferred. To systematize the process of source representation, analogs retrieval, idea transfer, and solution generation, an ideation model was put forward. Finally, the bladeless fan was selected as a source design to illustrate the application of this work. The design output shows that the representation and heuristics are beneficial, and this systematic ideation method can help the engineer or designer enhance creativity and discover more patentable opportunities.

An expert system for diagnosing environmentally induced spacecraft anomalies

NASA Technical Reports Server (NTRS)

Rolincik, Mark; Lauriente, Michael; Koons, Harry C.; Gorney, David

1992-01-01

A new rule-based, machine independent analytical tool was designed for diagnosing spacecraft anomalies using an expert system. Expert systems provide an effective method for saving knowledge, allow computers to sift through large amounts of data pinpointing significant parts, and most importantly, use heuristics in addition to algorithms, which allow approximate reasoning and inference and the ability to attack problems not rigidly defined. The knowledge base consists of over two-hundred (200) rules and provides links to historical and environmental databases. The environmental causes considered are bulk charging, single event upsets (SEU), surface charging, and total radiation dose. The system's driver translates forward chaining rules into a backward chaining sequence, prompting the user for information pertinent to the causes considered. The use of heuristics frees the user from searching through large amounts of irrelevant information and allows the user to input partial information (varying degrees of confidence in an answer) or 'unknown' to any question. The modularity of the expert system allows for easy updates and modifications. It not only provides scientists with needed risk analysis and confidence not found in algorithmic programs, but is also an effective learning tool, and the window implementation makes it very easy to use. The system currently runs on a Micro VAX II at Goddard Space Flight Center (GSFC). The inference engine used is NASA's C Language Integrated Production System (CLIPS).

Storage Costs and Heuristics Interact to Produce Patterns of Aphasic Sentence Comprehension Performance

PubMed Central

Clark, David Glenn

2012-01-01

Background: Despite general agreement that aphasic individuals exhibit difficulty understanding complex sentences, the nature of sentence complexity itself is unresolved. In addition, aphasic individuals appear to make use of heuristic strategies for understanding sentences. This research is a comparison of predictions derived from two approaches to the quantification of sentence complexity, one based on the hierarchical structure of sentences, and the other based on dependency locality theory (DLT). Complexity metrics derived from these theories are evaluated under various assumptions of heuristic use. Method: A set of complexity metrics was derived from each general theory of sentence complexity and paired with assumptions of heuristic use. Probability spaces were generated that summarized the possible patterns of performance across 16 different sentence structures. The maximum likelihood of comprehension scores of 42 aphasic individuals was then computed for each probability space and the expected scores from the best-fitting points in the space were recorded for comparison to the actual scores. Predictions were then compared using measures of fit quality derived from linear mixed effects models. Results: All three of the metrics that provide the most consistently accurate predictions of patient scores rely on storage costs based on the DLT. Patients appear to employ an Agent–Theme heuristic, but vary in their tendency to accept heuristically generated interpretations. Furthermore, the ability to apply the heuristic may be degraded in proportion to aphasia severity. Conclusion: DLT-derived storage costs provide the best prediction of sentence comprehension patterns in aphasia. Because these costs are estimated by counting incomplete syntactic dependencies at each point in a sentence, this finding suggests that aphasia is associated with reduced availability of cognitive resources for maintaining these dependencies. PMID:22590462

Storage costs and heuristics interact to produce patterns of aphasic sentence comprehension performance.

PubMed

Clark, David Glenn

2012-01-01

Despite general agreement that aphasic individuals exhibit difficulty understanding complex sentences, the nature of sentence complexity itself is unresolved. In addition, aphasic individuals appear to make use of heuristic strategies for understanding sentences. This research is a comparison of predictions derived from two approaches to the quantification of sentence complexity, one based on the hierarchical structure of sentences, and the other based on dependency locality theory (DLT). Complexity metrics derived from these theories are evaluated under various assumptions of heuristic use. A set of complexity metrics was derived from each general theory of sentence complexity and paired with assumptions of heuristic use. Probability spaces were generated that summarized the possible patterns of performance across 16 different sentence structures. The maximum likelihood of comprehension scores of 42 aphasic individuals was then computed for each probability space and the expected scores from the best-fitting points in the space were recorded for comparison to the actual scores. Predictions were then compared using measures of fit quality derived from linear mixed effects models. All three of the metrics that provide the most consistently accurate predictions of patient scores rely on storage costs based on the DLT. Patients appear to employ an Agent-Theme heuristic, but vary in their tendency to accept heuristically generated interpretations. Furthermore, the ability to apply the heuristic may be degraded in proportion to aphasia severity. DLT-derived storage costs provide the best prediction of sentence comprehension patterns in aphasia. Because these costs are estimated by counting incomplete syntactic dependencies at each point in a sentence, this finding suggests that aphasia is associated with reduced availability of cognitive resources for maintaining these dependencies.

An ant colony optimization based algorithm for identifying gene regulatory elements.

PubMed

Liu, Wei; Chen, Hanwu; Chen, Ling

2013-08-01

It is one of the most important tasks in bioinformatics to identify the regulatory elements in gene sequences. Most of the existing algorithms for identifying regulatory elements are inclined to converge into a local optimum, and have high time complexity. Ant Colony Optimization (ACO) is a meta-heuristic method based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of real ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper designs and implements an ACO based algorithm named ACRI (ant-colony-regulatory-identification) for identifying all possible binding sites of transcription factor from the upstream of co-expressed genes. To accelerate the ants' searching process, a strategy of local optimization is presented to adjust the ants' start positions on the searched sequences. By exploiting the powerful optimization ability of ACO, the algorithm ACRI can not only improve precision of the results, but also achieve a very high speed. Experimental results on real world datasets show that ACRI can outperform other traditional algorithms in the respects of speed and quality of solutions. Copyright © 2013 Elsevier Ltd. All rights reserved.

A hop count based heuristic routing protocol for mobile delay tolerant networks.

PubMed

You, Lei; Li, Jianbo; Wei, Changjiang; Dai, Chenqu; Xu, Jixing; Hu, Lejuan

2014-01-01

Routing in delay tolerant networks (DTNs) is a challenge since it must handle network partitioning, long delays, and dynamic topology. Meanwhile, routing protocols of the traditional mobile ad hoc networks (MANETs) cannot work well due to the failure of its assumption that most network connections are available. In this paper, we propose a hop count based heuristic routing protocol by utilizing the information carried by the peripatetic packets in the network. A heuristic function is defined to help in making the routing decision. We formally define a custom operation for square matrices so as to transform the heuristic value calculation into matrix manipulation. Finally, the performance of our proposed algorithm is evaluated by the simulation results, which show the advantage of such self-adaptive routing protocol in the diverse circumstance of DTNs.

A Hop Count Based Heuristic Routing Protocol for Mobile Delay Tolerant Networks

PubMed Central

Wei, Changjiang; Dai, Chenqu; Xu, Jixing; Hu, Lejuan

2014-01-01

Routing in delay tolerant networks (DTNs) is a challenge since it must handle network partitioning, long delays, and dynamic topology. Meanwhile, routing protocols of the traditional mobile ad hoc networks (MANETs) cannot work well due to the failure of its assumption that most network connections are available. In this paper, we propose a hop count based heuristic routing protocol by utilizing the information carried by the peripatetic packets in the network. A heuristic function is defined to help in making the routing decision. We formally define a custom operation for square matrices so as to transform the heuristic value calculation into matrix manipulation. Finally, the performance of our proposed algorithm is evaluated by the simulation results, which show the advantage of such self-adaptive routing protocol in the diverse circumstance of DTNs. PMID:25110736

Impact of heuristics in clustering large biological networks.

PubMed

Shafin, Md Kishwar; Kabir, Kazi Lutful; Ridwan, Iffatur; Anannya, Tasmiah Tamzid; Karim, Rashid Saadman; Hoque, Mohammad Mozammel; Rahman, M Sohel

2015-12-01

Traditional clustering algorithms often exhibit poor performance for large networks. On the contrary, greedy algorithms are found to be relatively efficient while uncovering functional modules from large biological networks. The quality of the clusters produced by these greedy techniques largely depends on the underlying heuristics employed. Different heuristics based on different attributes and properties perform differently in terms of the quality of the clusters produced. This motivates us to design new heuristics for clustering large networks. In this paper, we have proposed two new heuristics and analyzed the performance thereof after incorporating those with three different combinations in a recently celebrated greedy clustering algorithm named SPICi. We have extensively analyzed the effectiveness of these new variants. The results are found to be promising. Copyright © 2015 Elsevier Ltd. All rights reserved.

China Report, Science and Technology.

DTIC Science & Technology

1987-05-08

RECTANGULAR TANK UNDER LOW-GRAVITY CONDITIONS Beijing QINGHUA DAXUE XUEBAO (ZIRAN KEXUE BAN) [JOURNAL OF TSINGHUA UNIVERSITY (NATURAL SCIENCE)] in...1077 48 APPLIED SCIENCES of NEW WEIGHTED TECHNIQUE IN HEURISTIC SEARCH Beijing QINGHUA DAXUE XUEBAO (ZIRAN KEXUE BAN) [JOURNAL OF TSINGHUA...WITH ECCENTRIC CRACK Beijing QINGHUA DAXUE XUEBAO (ZIRAN KEXUE BAN) [JOURNAL OF TSINGHUA UNIVERSITY (NATURAL SCIENCE)] in Chinese Vol 26 No 3, Jun

Parsing Heuristic and Forward Search in First-Graders' Game-Play Behavior

ERIC Educational Resources Information Center

Paz, Luciano; Goldin, Andrea P.; Diuk, Carlos; Sigman, Mariano

2015-01-01

Seventy-three children between 6 and 7 years of age were presented with a problem having ambiguous subgoal ordering. Performance in this task showed reliable fingerprints: (a) a non-monotonic dependence of performance as a function of the distance between the beginning and the end-states of the problem, (b) very high levels of performance when the…

A new Nawaz-Enscore-Ham-based heuristic for permutation flow-shop problems with bicriteria of makespan and machine idle time

NASA Astrophysics Data System (ADS)

Liu, Weibo; Jin, Yan; Price, Mark

2016-10-01

A new heuristic based on the Nawaz-Enscore-Ham algorithm is proposed in this article for solving a permutation flow-shop scheduling problem. A new priority rule is proposed by accounting for the average, mean absolute deviation, skewness and kurtosis, in order to fully describe the distribution style of processing times. A new tie-breaking rule is also introduced for achieving effective job insertion with the objective of minimizing both makespan and machine idle time. Statistical tests illustrate better solution quality of the proposed algorithm compared to existing benchmark heuristics.

Efficient Network Coding-Based Loss Recovery for Reliable Multicast in Wireless Networks

NASA Astrophysics Data System (ADS)

Chi, Kaikai; Jiang, Xiaohong; Ye, Baoliu; Horiguchi, Susumu

Recently, network coding has been applied to the loss recovery of reliable multicast in wireless networks [19], where multiple lost packets are XOR-ed together as one packet and forwarded via single retransmission, resulting in a significant reduction of bandwidth consumption. In this paper, we first prove that maximizing the number of lost packets for XOR-ing, which is the key part of the available network coding-based reliable multicast schemes, is actually a complex NP-complete problem. To address this limitation, we then propose an efficient heuristic algorithm for finding an approximately optimal solution of this optimization problem. Furthermore, we show that the packet coding principle of maximizing the number of lost packets for XOR-ing sometimes cannot fully exploit the potential coding opportunities, and we then further propose new heuristic-based schemes with a new coding principle. Simulation results demonstrate that the heuristic-based schemes have very low computational complexity and can achieve almost the same transmission efficiency as the current coding-based high-complexity schemes. Furthermore, the heuristic-based schemes with the new coding principle not only have very low complexity, but also slightly outperform the current high-complexity ones.

Tachyon search speeds up retrieval of similar sequences by several orders of magnitude

PubMed Central

Tan, Joshua; Kuchibhatla, Durga; Sirota, Fernanda L.; Sherman, Westley A.; Gattermayer, Tobias; Kwoh, Chia Yee; Eisenhaber, Frank; Schneider, Georg; Maurer-Stroh, Sebastian

2012-01-01

Summary: The usage of current sequence search tools becomes increasingly slower as databases of protein sequences continue to grow exponentially. Tachyon, a new algorithm that identifies closely related protein sequences ~200 times faster than standard BLAST, circumvents this limitation with a reduced database and oligopeptide matching heuristic. Availability and implementation: The tool is publicly accessible as a webserver at http://tachyon.bii.a-star.edu.sg and can also be accessed programmatically through SOAP. Contact: sebastianms@bii.a-star.edu.sg Supplementary information: Supplementary data are available at the Bioinformatics online. PMID:22531216

Development of heuristic bias detection in elementary school.

PubMed

De Neys, Wim; Feremans, Vicky

2013-02-01

Although human reasoning is often biased by intuitive heuristics, recent studies have shown that adults and adolescents detect the biased nature of their judgments. The present study focused on the development of this critical bias sensitivity by examining the detection skills of young children in elementary school. Third and 6th graders were presented with child-friendly versions of classic base-rate problems in which a cued heuristic response could be inconsistent or consistent with the base rates. After each problem children were asked to indicate their subjective response confidence to assess their bias detection skills. Results indicated that 6th graders showed a clear confidence decrease when they gave a heuristic response that conflicted with the base rates. However, this confidence decrease was not observed for 3rd graders, suggesting that they did not yet acknowledge that their judgment was not fully warranted. Implications for the development of efficient training programs and the debate on human rationality are discussed. (c) 2013 APA, all rights reserved.

A novel harmony search-K means hybrid algorithm for clustering gene expression data

PubMed Central

Nazeer, KA Abdul; Sebastian, MP; Kumar, SD Madhu

2013-01-01

Recent progress in bioinformatics research has led to the accumulation of huge quantities of biological data at various data sources. The DNA microarray technology makes it possible to simultaneously analyze large number of genes across different samples. Clustering of microarray data can reveal the hidden gene expression patterns from large quantities of expression data that in turn offers tremendous possibilities in functional genomics, comparative genomics, disease diagnosis and drug development. The k- ¬means clustering algorithm is widely used for many practical applications. But the original k-¬means algorithm has several drawbacks. It is computationally expensive and generates locally optimal solutions based on the random choice of the initial centroids. Several methods have been proposed in the literature for improving the performance of the k-¬means algorithm. A meta-heuristic optimization algorithm named harmony search helps find out near-global optimal solutions by searching the entire solution space. Low clustering accuracy of the existing algorithms limits their use in many crucial applications of life sciences. In this paper we propose a novel Harmony Search-K means Hybrid (HSKH) algorithm for clustering the gene expression data. Experimental results show that the proposed algorithm produces clusters with better accuracy in comparison with the existing algorithms. PMID:23390351

A novel harmony search-K means hybrid algorithm for clustering gene expression data.

PubMed

Nazeer, Ka Abdul; Sebastian, Mp; Kumar, Sd Madhu

2013-01-01

Recent progress in bioinformatics research has led to the accumulation of huge quantities of biological data at various data sources. The DNA microarray technology makes it possible to simultaneously analyze large number of genes across different samples. Clustering of microarray data can reveal the hidden gene expression patterns from large quantities of expression data that in turn offers tremendous possibilities in functional genomics, comparative genomics, disease diagnosis and drug development. The k- ¬means clustering algorithm is widely used for many practical applications. But the original k-¬means algorithm has several drawbacks. It is computationally expensive and generates locally optimal solutions based on the random choice of the initial centroids. Several methods have been proposed in the literature for improving the performance of the k-¬means algorithm. A meta-heuristic optimization algorithm named harmony search helps find out near-global optimal solutions by searching the entire solution space. Low clustering accuracy of the existing algorithms limits their use in many crucial applications of life sciences. In this paper we propose a novel Harmony Search-K means Hybrid (HSKH) algorithm for clustering the gene expression data. Experimental results show that the proposed algorithm produces clusters with better accuracy in comparison with the existing algorithms.

Impact of Blended Learning Environments Based on Algo-Heuristic Theory on Some Variables

ERIC Educational Resources Information Center

Aygün, Mustafa; Korkmaz, Özgen

2012-01-01

In this study, the effects of Algo-Heuristic Theory based blended learning environments on students' computer skills in their preparation of presentations, levels of attitudes towards computers, and levels of motivation regarding the information technology course were investigated. The research sample was composed of 71 students. A semi-empirical…

A hybrid binary particle swarm optimization for large capacitated multi item multi level lot sizing (CMIMLLS) problem

NASA Astrophysics Data System (ADS)

Mishra, S. K.; Sahithi, V. V. D.; Rao, C. S. P.

2016-09-01

The lot sizing problem deals with finding optimal order quantities which minimizes the ordering and holding cost of product mix. when multiple items at multiple levels with all capacity restrictions are considered, the lot sizing problem become NP hard. Many heuristics were developed in the past have inevitably failed due to size, computational complexity and time. However the authors were successful in the development of PSO based technique namely iterative improvement binary particles swarm technique to address very large capacitated multi-item multi level lot sizing (CMIMLLS) problem. First binary particle Swarm Optimization algorithm is used to find a solution in a reasonable time and iterative improvement local search mechanism is employed to improvise the solution obtained by BPSO algorithm. This hybrid mechanism of using local search on the global solution is found to improve the quality of solutions with respect to time thus IIBPSO method is found best and show excellent results.

Aiding Design of Wave Energy Converters via Computational Simulations

NASA Astrophysics Data System (ADS)

Jebeli Aqdam, Hejar; Ahmadi, Babak; Raessi, Mehdi; Tootkaboni, Mazdak

2015-11-01

With the increasing interest in renewable energy sources, wave energy converters will continue to gain attention as a viable alternative to current electricity production methods. It is therefore crucial to develop computational tools for the design and analysis of wave energy converters. A successful design requires balance between the design performance and cost. Here an analytical solution is used for the approximate analysis of interactions between a flap-type wave energy converter (WEC) and waves. The method is verified using other flow solvers and experimental test cases. Then the model is used in conjunction with a powerful heuristic optimization engine, Charged System Search (CSS) to explore the WEC design space. CSS is inspired by charged particles behavior. It searches the design space by considering candidate answers as charged particles and moving them based on the Coulomb's laws of electrostatics and Newton's laws of motion to find the global optimum. Finally the impacts of changes in different design parameters on the power takeout of the superior WEC designs are investigated. National Science Foundation, CBET-1236462.

Paranoid thinking as a heuristic.

PubMed

Preti, Antonio; Cella, Matteo

2010-08-01

Paranoid thinking can be viewed as a human heuristic used by individuals to deal with uncertainty during stressful situations. Under stress, individuals are likely to emphasize the threatening value of neutral stimuli and increase the reliance on paranoia-based heuristic to interpreter events and guide their decisions. Paranoid thinking can also be activated by stress arising from the possibility of losing a good opportunity; this may result in an abnormal allocation of attentional resources to social agents. A better understanding of the interplay between cognitive heuristics and emotional processes may help to detect situations in which paranoid thinking is likely to exacerbate and improve intervention for individuals with delusional disorders.

How Smart Do You Need to Be to Get It Wrong? The Role of Cognitive Capacity in the Development of Heuristic-Based Judgment

ERIC Educational Resources Information Center

Morsanyi, Kinga; Handley, Simon J.

2008-01-01

We examined the relationship between cognitive capacity and heuristic responding on four types of reasoning and decision-making tasks. A total of 84 children, between 5 years 2 months and 11 years 7 months of age, participated in the study. There was a marked increase in heuristic responding with age that was related to increases in cognitive…

A Heuristic Decision Making Model to Mitigate Adverse Consequences in a Network Centric Warfare/Sense and Respond System

DTIC Science & Technology

2005-05-01

made. 4. Do military decision makers identify / analyze adverse consequences presently? Few do based on this research and most don’t do it effectively ...A HEURISTIC DECISION MAKING MODEL TO MITIGATE ADVERSE CONSEQUENCES IN A NETWORK CENTRIC WARFARE / SENSE AND RESPOND SYSTEM...ENS/05-01 A HEURISTIC DECISION MAKING MODEL TO MITIGATE ADVERSE CONSEQUENCES IN A NETWORK CENTRIC WARFARE / SENSE AND RESPOND SYSTEM

Teaching-learning-based Optimization Algorithm for Parameter Identification in the Design of IIR Filters

NASA Astrophysics Data System (ADS)

Singh, R.; Verma, H. K.

2013-12-01

This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.

A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem

PubMed Central

Zamli, Kamal Z.; Din, Fakhrud; Bures, Miroslav

2018-01-01

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Lévy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level. PMID:29771918

A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem.

PubMed

Zamli, Kamal Z; Din, Fakhrud; Ahmed, Bestoun S; Bures, Miroslav

2018-01-01

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Lévy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level.

Parallel Computational Protein Design.

PubMed

Zhou, Yichao; Donald, Bruce R; Zeng, Jianyang

2017-01-01

Computational structure-based protein design (CSPD) is an important problem in computational biology, which aims to design or improve a prescribed protein function based on a protein structure template. It provides a practical tool for real-world protein engineering applications. A popular CSPD method that guarantees to find the global minimum energy solution (GMEC) is to combine both dead-end elimination (DEE) and A* tree search algorithms. However, in this framework, the A* search algorithm can run in exponential time in the worst case, which may become the computation bottleneck of large-scale computational protein design process. To address this issue, we extend and add a new module to the OSPREY program that was previously developed in the Donald lab (Gainza et al., Methods Enzymol 523:87, 2013) to implement a GPU-based massively parallel A* algorithm for improving protein design pipeline. By exploiting the modern GPU computational framework and optimizing the computation of the heuristic function for A* search, our new program, called gOSPREY, can provide up to four orders of magnitude speedups in large protein design cases with a small memory overhead comparing to the traditional A* search algorithm implementation, while still guaranteeing the optimality. In addition, gOSPREY can be configured to run in a bounded-memory mode to tackle the problems in which the conformation space is too large and the global optimal solution cannot be computed previously. Furthermore, the GPU-based A* algorithm implemented in the gOSPREY program can be combined with the state-of-the-art rotamer pruning algorithms such as iMinDEE (Gainza et al., PLoS Comput Biol 8:e1002335, 2012) and DEEPer (Hallen et al., Proteins 81:18-39, 2013) to also consider continuous backbone and side-chain flexibility.

Unified heuristics to solve routing problem of reverse logistics in sustainable supply chain

NASA Astrophysics Data System (ADS)

Anbuudayasankar, S. P.; Ganesh, K.; Lenny Koh, S. C.; Mohandas, K.

2010-03-01

A reverse logistics problem, motivated by many real-life applications, is examined where bottles/cans in which products are delivered from a processing depot to customers in one period are available for return to the depot in the following period. The picked-up bottles/cans need to be adjusted in the place of delivery load. This problem is termed as simultaneous delivery and pick-up problem with constrained capacity (SDPC). We develop three unified heuristics based on extended branch and bound heuristic, genetic algorithm and simulated annealing to solve SDPC. These heuristics are also designed to solve standard travelling salesman problem (TSP) and TSP with simultaneous delivery and pick-up (TSDP). We tested the heuristics on standard, derived and randomly generated datasets of TSP, TSDP and SDPC and obtained satisfying results with high convergence in reasonable time.

Evidence-based Heuristics for Evaluating Demands on eHealth Literacy and Usability in a Mobile Consumer Health Application.

PubMed

Monkman, Helen; Griffith, Janessa; Kushniruk, Andre W

2015-01-01

Heuristic evaluations have proven to be valuable for identifying usability issues in systems. Commonly used sets of heuritics exist; however, they may not always be the most suitable, given the specific goal of the analysis. One such example is seeking to evaluate the demands on eHealth literacy and usability of consumer health information systems. In this study, eight essential heuristics and three optional heuristics subsumed from the evidence on eHealth/health literacy and usability were tested for their utility in assessing a mobile blood pressure tracking application (app). This evaluation revealed a variety of ways the design of the app could both benefit and impede users with limited eHealth literacy. This study demonstrated the utility of a low-cost, single evaluation approach for identifying both eHealth literacy and usability issues based on existing evidence in the literature.

Species tree inference by minimizing deep coalescences.

PubMed

Than, Cuong; Nakhleh, Luay

2009-09-01

In a 1997 seminal paper, W. Maddison proposed minimizing deep coalescences, or MDC, as an optimization criterion for inferring the species tree from a set of incongruent gene trees, assuming the incongruence is exclusively due to lineage sorting. In a subsequent paper, Maddison and Knowles provided and implemented a search heuristic for optimizing the MDC criterion, given a set of gene trees. However, the heuristic is not guaranteed to compute optimal solutions, and its hill-climbing search makes it slow in practice. In this paper, we provide two exact solutions to the problem of inferring the species tree from a set of gene trees under the MDC criterion. In other words, our solutions are guaranteed to find the tree that minimizes the total number of deep coalescences from a set of gene trees. One solution is based on a novel integer linear programming (ILP) formulation, and another is based on a simple dynamic programming (DP) approach. Powerful ILP solvers, such as CPLEX, make the first solution appealing, particularly for very large-scale instances of the problem, whereas the DP-based solution eliminates dependence on proprietary tools, and its simplicity makes it easy to integrate with other genomic events that may cause gene tree incongruence. Using the exact solutions, we analyze a data set of 106 loci from eight yeast species, a data set of 268 loci from eight Apicomplexan species, and several simulated data sets. We show that the MDC criterion provides very accurate estimates of the species tree topologies, and that our solutions are very fast, thus allowing for the accurate analysis of genome-scale data sets. Further, the efficiency of the solutions allow for quick exploration of sub-optimal solutions, which is important for a parsimony-based criterion such as MDC, as we show. We show that searching for the species tree in the compatibility graph of the clusters induced by the gene trees may be sufficient in practice, a finding that helps ameliorate the computational requirements of optimization solutions. Further, we study the statistical consistency and convergence rate of the MDC criterion, as well as its optimality in inferring the species tree. Finally, we show how our solutions can be used to identify potential horizontal gene transfer events that may have caused some of the incongruence in the data, thus augmenting Maddison's original framework. We have implemented our solutions in the PhyloNet software package, which is freely available at: http://bioinfo.cs.rice.edu/phylonet.

Can the inherence heuristic explain vitalistic reasoning?

PubMed

Bastian, Brock

2014-10-01

Inherence is an important component of psychological essentialism. By drawing on vitalism as a way in which to explain this link, however, the authors appear to conflate causal explanations based on fixed features with those based on general causal forces. The disjuncture between these two types of explanatory principles highlights potential new avenues for the inherence heuristic.

Reasoning by analogy as an aid to heuristic theorem proving.

NASA Technical Reports Server (NTRS)

Kling, R. E.

1972-01-01

When heuristic problem-solving programs are faced with large data bases that contain numbers of facts far in excess of those needed to solve any particular problem, their performance rapidly deteriorates. In this paper, the correspondence between a new unsolved problem and a previously solved analogous problem is computed and invoked to tailor large data bases to manageable sizes. This paper outlines the design of an algorithm for generating and exploiting analogies between theorems posed to a resolution-logic system. These algorithms are believed to be the first computationally feasible development of reasoning by analogy to be applied to heuristic theorem proving.

Scheduling and rescheduling with iterative repair

NASA Technical Reports Server (NTRS)

Zweben, Monte; Davis, Eugene; Daun, Brian; Deale, Michael

1992-01-01

This paper describes the GERRY scheduling and rescheduling system being applied to coordinate Space Shuttle Ground Processing. The system uses constraint-based iterative repair, a technique that starts with a complete but possibly flawed schedule and iteratively improves it by using constraint knowledge within repair heuristics. In this paper we explore the tradeoff between the informedness and the computational cost of several repair heuristics. We show empirically that some knowledge can greatly improve the convergence speed of a repair-based system, but that too much knowledge, such as the knowledge embodied within the MIN-CONFLICTS lookahead heuristic, can overwhelm a system and result in degraded performance.

2D photonic crystal complete band gap search using a cyclic cellular automaton refination

NASA Astrophysics Data System (ADS)

González-García, R.; Castañón, G.; Hernández-Figueroa, H. E.

2014-11-01

We present a refination method based on a cyclic cellular automaton (CCA) that simulates a crystallization-like process, aided with a heuristic evolutionary method called differential evolution (DE) used to perform an ordered search of full photonic band gaps (FPBGs) in a 2D photonic crystal (PC). The solution is proposed as a combinatorial optimization of the elements in a binary array. These elements represent the existence or absence of a dielectric material surrounded by air, thus representing a general geometry whose search space is defined by the number of elements in such array. A block-iterative frequency-domain method was used to compute the FPBGs on a PC, when present. DE has proved to be useful in combinatorial problems and we also present an implementation feature that takes advantage of the periodic nature of PCs to enhance the convergence of this algorithm. Finally, we used this methodology to find a PC structure with a 19% bandgap-to-midgap ratio without requiring previous information of suboptimal configurations and we made a statistical study of how it is affected by disorder in the borders of the structure compared with a previous work that uses a genetic algorithm.

An improved exploratory search technique for pure integer linear programming problems

NASA Technical Reports Server (NTRS)

Fogle, F. R.

1990-01-01

The development is documented of a heuristic method for the solution of pure integer linear programming problems. The procedure draws its methodology from the ideas of Hooke and Jeeves type 1 and 2 exploratory searches, greedy procedures, and neighborhood searches. It uses an efficient rounding method to obtain its first feasible integer point from the optimal continuous solution obtained via the simplex method. Since this method is based entirely on simple addition or subtraction of one to each variable of a point in n-space and the subsequent comparison of candidate solutions to a given set of constraints, it facilitates significant complexity improvements over existing techniques. It also obtains the same optimal solution found by the branch-and-bound technique in 44 of 45 small to moderate size test problems. Two example problems are worked in detail to show the inner workings of the method. Furthermore, using an established weighted scheme for comparing computational effort involved in an algorithm, a comparison of this algorithm is made to the more established and rigorous branch-and-bound method. A computer implementation of the procedure, in PC compatible Pascal, is also presented and discussed.

Multipass Target Search in Natural Environments

PubMed Central

Otte, Michael W.; Sofge, Donald; Gupta, Satyandra K.

2017-01-01

Consider a disaster scenario where search and rescue workers must search difficult to access buildings during an earthquake or flood. Often, finding survivors a few hours sooner results in a dramatic increase in saved lives, suggesting the use of drones for expedient rescue operations. Entropy can be used to quantify the generation and resolution of uncertainty. When searching for targets, maximizing mutual information of future sensor observations will minimize expected target location uncertainty by minimizing the entropy of the future estimate. Motion planning for multi-target autonomous search requires planning over an area with an imperfect sensor and may require multiple passes, which is hindered by the submodularity property of mutual information. Further, mission duration constraints must be handled accordingly, requiring consideration of the vehicle’s dynamics to generate feasible trajectories and must plan trajectories spanning the entire mission duration, something which most information gathering algorithms are incapable of doing. If unanticipated changes occur in an uncertain environment, new plans must be generated quickly. In addition, planning multipass trajectories requires evaluating path dependent rewards, requiring planning in the space of all previously selected actions, compounding the problem. We present an anytime algorithm for autonomous multipass target search in natural environments. The algorithm is capable of generating long duration dynamically feasible multipass coverage plans that maximize mutual information using a variety of techniques such as ϵ-admissible heuristics to speed up the search. To the authors’ knowledge this is the first attempt at efficiently solving multipass target search problems of such long duration. The proposed algorithm is based on best first branch and bound and is benchmarked against state of the art algorithms adapted to the problem in natural Simplex environments, gathering the most information in the given search time. PMID:29099087

Cognitive Mechanisms of Insight: The Role of Heuristics and Representational Change in Solving the Eight-Coin Problem

ERIC Educational Resources Information Center

Öllinger, Michael; Jones, Gary; Faber, Amory H.; Knoblich, Günther

2013-01-01

The 8-coin insight problem requires the problem solver to move 2 coins so that each coin touches exactly 3 others. Ormerod, MacGregor, and Chronicle (2002) explained differences in task performance across different versions of the 8-coin problem using the availability of particular moves in a 2-dimensional search space. We explored 2 further…

A Heuristic Model of Media Conflict Systems in the Wake of Expose of Cover-Ups.

ERIC Educational Resources Information Center

Nager, Norman R.

The purpose of this study was to stimulate scholarly investigation of a conflict systems model for news media and the interaction of sources in the wake of an expose. A search of the literature led to nine tentative axioms that focus attention on systemic conflict of media and sources related to cover-ups and suggested additional theory…

An exploration of group-based HIV/AIDS treatment and care models in Sub-Saharan Africa using a realist evaluation (Intervention-Context-Actor-Mechanism-Outcome) heuristic tool: a systematic review.

PubMed

Mukumbang, Ferdinand C; Van Belle, Sara; Marchal, Bruno; van Wyk, Brian

2017-08-25

It is increasingly acknowledged that differentiated care models hold potential to manage large volumes of patients on antiretroviral therapy (ART). Various group-based models of ART service delivery aimed at decongesting local health facilities, encouraging patient retention in care, and enhancing adherence to medication have been implemented across sub-Saharan Africa. Evidence from the literature suggests that these models of ART service delivery are more effective than corresponding facility-based care and superior to individual-based models. Nevertheless, there is little understanding of how these care models work to achieve their intended outcomes. The aim of this study was to review the theories explicating how and why group-based ART models work using a realist evaluation framework. A systematic review of the literature on group-based ART support models in sub-Saharan Africa was conducted. We searched the Google Scholar and PubMed databases and supplemented these with a reference chase of the identified articles. We applied a theory-driven approach-narrative synthesis-to synthesise the data. Data were analysed using the thematic content analysis method and synthesised according to aspects of the Intervention-Context-Actor-Mechanism-Outcome heuristic-analytic tool-a realist evaluation theory building tool. Twelve articles reporting primary studies on group-based models of ART service delivery were included in the review. The six studies that employed a quantitative study design failed to identify aspects of the context and mechanisms that work to trigger the outcomes of group-based models. While the other four studies that applied a qualitative and the two using a mixed methods design identified some of the aspects of the context and mechanisms that could trigger the outcomes of group-based ART models, these studies did not explain the relationship(s) between the theory elements and how they interact to produce the outcome(s). Although we could distill various components of the Intervention-Context-Actor-Mechanism-Outcome analytic tool from different studies exploring group-based programmes, we could not, however, identify a salient programme theory based on the Intervention-Context-Actor-Mechanism-Outcome heuristic analysis. The scientific community, policy makers and programme implementers would benefit more if explanatory findings of how, why, for whom and in what circumstances programmes work are presented rather than just reporting on the outcomes of the interventions.

Ant Colony Optimization for Mapping, Scheduling and Placing in Reconfigurable Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ferrandi, Fabrizio; Lanzi, Pier Luca; Pilato, Christian

Modern heterogeneous embedded platforms, com- posed of several digital signal, application specific and general purpose processors, also include reconfigurable devices support- ing partial dynamic reconfiguration. These devices can change the behavior of some of their parts during execution, allowing hardware acceleration of more sections of the applications. Never- theless, partial dynamic reconfiguration imposes severe overheads in terms of latency. For such systems, a critical part of the design phase is deciding on which processing elements (mapping) and when (scheduling) executing a task, but also how to place them on the reconfigurable device to guarantee the most efficient reuse of themore » programmable logic. In this paper we propose an algorithm based on Ant Colony Optimization (ACO) that simultaneously executes the scheduling, the mapping and the linear placing of tasks, hiding reconfiguration overheads through prefetching. Our heuristic gradually constructs solutions and then searches around the best ones, cutting out non-promising areas of the design space. We show how to consider the partial dynamic reconfiguration constraints in the scheduling, placing and mapping problems and compare our formulation to other heuristics that address the same problems. We demonstrate that our proposal is more general and robust, and finds better solutions (16.5% in average) with respect to competing solutions.« less

Put a limit on it: The protective effects of scarcity heuristics when self-control is low

PubMed Central

Cheung, Tracy TL; Kroese, Floor M; Fennis, Bob M; De Ridder, Denise TD

2015-01-01

Low self-control is a state in which consumers are assumed to be vulnerable to making impulsive choices that hurt long-term goals. Rather than increasing self-control, the current research exploits the tendency for heuristic-based thinking in low self-control by employing scarcity heuristics to promote better consumption choices. Results indicate that consumers low in self-control especially benefited and selected more healthy choices when marketed as “scarce” (Study 1), and that a demand (vs supply) scarcity heuristic was most effective in promoting utilitarian products (Study 2) suggests low self-control involves both an enhanced reward orientation and increased tendency to conform to descriptive norms. PMID:28070377

A two-stage stochastic rule-based model to determine pre-assembly buffer content

NASA Astrophysics Data System (ADS)

Gunay, Elif Elcin; Kula, Ufuk

2018-01-01

This study considers instant decision-making needs of the automobile manufactures for resequencing vehicles before final assembly (FA). We propose a rule-based two-stage stochastic model to determine the number of spare vehicles that should be kept in the pre-assembly buffer to restore the altered sequence due to paint defects and upstream department constraints. First stage of the model decides the spare vehicle quantities, where the second stage model recovers the scrambled sequence respect to pre-defined rules. The problem is solved by sample average approximation (SAA) algorithm. We conduct a numerical study to compare the solutions of heuristic model with optimal ones and provide following insights: (i) as the mismatch between paint entrance and scheduled sequence decreases, the rule-based heuristic model recovers the scrambled sequence as good as the optimal resequencing model, (ii) the rule-based model is more sensitive to the mismatch between the paint entrance and scheduled sequences for recovering the scrambled sequence, (iii) as the defect rate increases, the difference in recovery effectiveness between rule-based heuristic and optimal solutions increases, (iv) as buffer capacity increases, the recovery effectiveness of the optimization model outperforms heuristic model, (v) as expected the rule-based model holds more inventory than the optimization model.

Systematic search for wide periodic windows and bounds for the set of regular parameters for the quadratic map.

PubMed

Galias, Zbigniew

2017-05-01

An efficient method to find positions of periodic windows for the quadratic map f(x)=ax(1-x) and a heuristic algorithm to locate the majority of wide periodic windows are proposed. Accurate rigorous bounds of positions of all periodic windows with periods below 37 and the majority of wide periodic windows with longer periods are found. Based on these results, we prove that the measure of the set of regular parameters in the interval [3,4] is above 0.613960137. The properties of periodic windows are studied numerically. The results of the analysis are used to estimate that the true value of the measure of the set of regular parameters is close to 0.6139603.

Response demands and the recruitment of heuristic strategies in syllogistic reasoning.

PubMed

Reverberi, Carlo; Rusconi, Patrice; Paulesu, Eraldo; Cherubini, Paolo

2009-03-01

Two experiments investigated whether dealing with a homogeneous subset of syllogisms with time-constrained responses encouraged participants to develop and use heuristics for abstract (Experiment 1) and thematic (Experiment 2) syllogisms. An atmosphere-based heuristic accounted for most responses with both abstract and thematic syllogisms. With thematic syllogisms, a weaker effect of a belief heuristic was also observed, mainly where the correct response was inconsistent with the atmosphere of the premises. Analytic processes appear to have played little role in the time-constrained condition, whereas their involvement increased in a self-paced, unconstrained condition. From a dual-process perspective, the results further specify how task demands affect the recruitment of heuristic and analytic systems of reasoning. Because the syllogisms and experimental procedure were the same as those used in a previous neuroimaging study by Goel, Buchel, Frith, and Dolan (2000), the result also deepen our understanding of the cognitive processes investigated by that study.

Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment.

PubMed

Madni, Syed Hamid Hussain; Abd Latiff, Muhammad Shafie; Abdullahi, Mohammed; Abdulhamid, Shafi'i Muhammad; Usman, Mohammed Joda

2017-01-01

Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.

Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment

PubMed Central

Madni, Syed Hamid Hussain; Abd Latiff, Muhammad Shafie; Abdullahi, Mohammed; Usman, Mohammed Joda

2017-01-01

Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing. PMID:28467505

A health literacy and usability heuristic evaluation of a mobile consumer health application.

PubMed

Monkman, Helen; Kushniruk, Andre

2013-01-01

Usability and health literacy are two critical factors in the design and evaluation of consumer health information systems. However, methods for evaluating these two factors in conjunction remain limited. This study adapted a set of existing guidelines for the design of consumer health Web sites into evidence-based evaluation heuristics tailored specifically for mobile consumer health applications. In order to test the approach, a mobile consumer health application (app) was then evaluated using these heuristics. In addition to revealing ways to improve the usability of the system, this analysis identified opportunities to augment the content to make it more understandable by users with limited health literacy. This study successfully demonstrated the utility of converting existing design guidelines into heuristics for the evaluation of usability and health literacy. The heuristics generated could be applied for assessing and revising other existing consumer health information systems.

Reasoning heuristics across the psychosis continuum: the contribution of hypersalient evidence-hypothesis matches.

PubMed

Balzan, Ryan; Delfabbro, Paul; Galletly, Cherrie; Woodward, Todd

2012-01-01

Hypersalience of evidence-hypothesis matches has recently been proposed as the cognitive mechanism responsible for the cognitive biases which, in turn, may contribute to the formation and maintenance of delusions. However, the construct lacks empirical support. The current paper investigates the possibility that individuals with delusions are hypersalient to evidence-hypothesis matches using a series of cognitive tasks designed to elicit the representativeness and availability reasoning heuristics. It was hypothesised that hypersalience of evidence-hypothesis matches may increase a person's propensity to rely on judgements of representativeness (i.e., when the probability of an outcome is based on its similarity with its parent population) and availability (i.e., estimates of frequency based on the ease with which relevant events come to mind). A total of 75 participants (25 diagnosed with schizophrenia with a history of delusions; 25 nonclinical delusion-prone; 25 nondelusion-prone controls) completed four heuristics tasks based on the original Tversky and Kahnemann experiments. These included two representativeness tasks ("coin-toss" random sequence task; "lawyer-engineer" base-rates task) and two availability tasks ("famous-names" and "letter-frequency" tasks). The results across these four heuristics tasks showed that participants with schizophrenia were more susceptible than nonclinical groups to both the representativeness and availability reasoning heuristics. These results suggest that delusional ideation is linked to a hypersalience of evidence-hypothesis matches. The theoretical implications of this cognitive mechanism on the formation and maintenance of delusions are discussed.

Heuristic Evaluation on Mobile Interfaces: A New Checklist

PubMed Central

Yáñez Gómez, Rosa; Cascado Caballero, Daniel; Sevillano, José-Luis

2014-01-01

The rapid evolution and adoption of mobile devices raise new usability challenges, given their limitations (in screen size, battery life, etc.) as well as the specific requirements of this new interaction. Traditional evaluation techniques need to be adapted in order for these requirements to be met. Heuristic evaluation (HE), an Inspection Method based on evaluation conducted by experts over a real system or prototype, is based on checklists which are desktop-centred and do not adequately detect mobile-specific usability issues. In this paper, we propose a compilation of heuristic evaluation checklists taken from the existing bibliography but readapted to new mobile interfaces. Selecting and rearranging these heuristic guidelines offer a tool which works well not just for evaluation but also as a best-practices checklist. The result is a comprehensive checklist which is experimentally evaluated as a design tool. This experimental evaluation involved two software engineers without any specific knowledge about usability, a group of ten users who compared the usability of a first prototype designed without our heuristics, and a second one after applying the proposed checklist. The results of this experiment show the usefulness of the proposed checklist for avoiding usability gaps even with nontrained developers. PMID:25295300

Negations in syllogistic reasoning: evidence for a heuristic-analytic conflict.

PubMed

Stupple, Edward J N; Waterhouse, Eleanor F

2009-08-01

An experiment utilizing response time measures was conducted to test dominant processing strategies in syllogistic reasoning with the expanded quantifier set proposed by Roberts (2005). Through adding negations to existing quantifiers it is possible to change problem surface features without altering logical validity. Biases based on surface features such as atmosphere, matching, and the probability heuristics model (PHM; Chater & Oaksford, 1999; Wetherick & Gilhooly, 1995) would not be expected to show variance in response latencies, but participant responses should be highly sensitive to changes in the surface features of the quantifiers. In contrast, according to analytic accounts such as mental models theory and mental logic (e.g., Johnson-Laird & Byrne, 1991; Rips, 1994) participants should exhibit increased response times for negated premises, but not be overly impacted upon by the surface features of the conclusion. Data indicated that the dominant response strategy was based on a matching heuristic, but also provided evidence of a resource-demanding analytic procedure for dealing with double negatives. The authors propose that dual-process theories offer a stronger account of these data whereby participants employ competing heuristic and analytic strategies and fall back on a heuristic response when analytic processing fails.

Configurable pattern-based evolutionary biclustering of gene expression data

PubMed Central

2013-01-01

Background Biclustering algorithms for microarray data aim at discovering functionally related gene sets under different subsets of experimental conditions. Due to the problem complexity and the characteristics of microarray datasets, heuristic searches are usually used instead of exhaustive algorithms. Also, the comparison among different techniques is still a challenge. The obtained results vary in relevant features such as the number of genes or conditions, which makes it difficult to carry out a fair comparison. Moreover, existing approaches do not allow the user to specify any preferences on these properties. Results Here, we present the first biclustering algorithm in which it is possible to particularize several biclusters features in terms of different objectives. This can be done by tuning the specified features in the algorithm or also by incorporating new objectives into the search. Furthermore, our approach bases the bicluster evaluation in the use of expression patterns, being able to recognize both shifting and scaling patterns either simultaneously or not. Evolutionary computation has been chosen as the search strategy, naming thus our proposal Evo-Bexpa (Evolutionary Biclustering based in Expression Patterns). Conclusions We have conducted experiments on both synthetic and real datasets demonstrating Evo-Bexpa abilities to obtain meaningful biclusters. Synthetic experiments have been designed in order to compare Evo-Bexpa performance with other approaches when looking for perfect patterns. Experiments with four different real datasets also confirm the proper performing of our algorithm, whose results have been biologically validated through Gene Ontology. PMID:23433178

Heuristic evaluation of eNote: an electronic notes system.

PubMed

Bright, Tiffani J; Bakken, Suzanne; Johnson, Stephen B

2006-01-01

eNote is an electronic health record (EHR) system based on semi-structured narrative documents. A heuristic evaluation was conducted with a sample of five usability experts. eNote performed highly in: 1)consistency with standards and 2)recognition rather than recall. eNote needs improvement in: 1)help and documentation, 2)aesthetic and minimalist design, 3)error prevention, 4)helping users recognize, diagnosis, and recover from errors, and 5)flexibility and efficiency of use. The heuristic evaluation was an efficient method of evaluating our interface.

SHRIF, a General-Purpose System for Heuristic Retrieval of Information and Facts, Applied to Medical Knowledge Processing.

ERIC Educational Resources Information Center

Findler, Nicholas V.; And Others

1992-01-01

Describes SHRIF, a System for Heuristic Retrieval of Information and Facts, and the medical knowledge base that was used in its development. Highlights include design decisions; the user-machine interface, including the language processor; and the organization of the knowledge base in an artificial intelligence (AI) project like this one. (57…

A new graph-based method for pairwise global network alignment

PubMed Central

Klau, Gunnar W

2009-01-01

Background In addition to component-based comparative approaches, network alignments provide the means to study conserved network topology such as common pathways and more complex network motifs. Yet, unlike in classical sequence alignment, the comparison of networks becomes computationally more challenging, as most meaningful assumptions instantly lead to NP-hard problems. Most previous algorithmic work on network alignments is heuristic in nature. Results We introduce the graph-based maximum structural matching formulation for pairwise global network alignment. We relate the formulation to previous work and prove NP-hardness of the problem. Based on the new formulation we build upon recent results in computational structural biology and present a novel Lagrangian relaxation approach that, in combination with a branch-and-bound method, computes provably optimal network alignments. The Lagrangian algorithm alone is a powerful heuristic method, which produces solutions that are often near-optimal and – unlike those computed by pure heuristics – come with a quality guarantee. Conclusion Computational experiments on the alignment of protein-protein interaction networks and on the classification of metabolic subnetworks demonstrate that the new method is reasonably fast and has advantages over pure heuristics. Our software tool is freely available as part of the LISA library. PMID:19208162

KAM (Knowledge Acquisition Module): A tool to simplify the knowledge acquisition process

NASA Technical Reports Server (NTRS)

Gettig, Gary A.

1988-01-01

Analysts, knowledge engineers and information specialists are faced with increasing volumes of time-sensitive data in text form, either as free text or highly structured text records. Rapid access to the relevant data in these sources is essential. However, due to the volume and organization of the contents, and limitations of human memory and association, frequently: (1) important information is not located in time; (2) reams of irrelevant data are searched; and (3) interesting or critical associations are missed due to physical or temporal gaps involved in working with large files. The Knowledge Acquisition Module (KAM) is a microcomputer-based expert system designed to assist knowledge engineers, analysts, and other specialists in extracting useful knowledge from large volumes of digitized text and text-based files. KAM formulates non-explicit, ambiguous, or vague relations, rules, and facts into a manageable and consistent formal code. A library of system rules or heuristics is maintained to control the extraction of rules, relations, assertions, and other patterns from the text. These heuristics can be added, deleted or customized by the user. The user can further control the extraction process with optional topic specifications. This allows the user to cluster extracts based on specific topics. Because KAM formalizes diverse knowledge, it can be used by a variety of expert systems and automated reasoning applications. KAM can also perform important roles in computer-assisted training and skill development. Current research efforts include the applicability of neural networks to aid in the extraction process and the conversion of these extracts into standard formats.

Heuristic evaluation of infusion pumps: implications for patient safety in Intensive Care Units.

PubMed

Graham, Mark J; Kubose, Tate K; Jordan, Desmond; Zhang, Jiajie; Johnson, Todd R; Patel, Vimla L

2004-11-01

The goal of this research was to use a heuristic evaluation methodology to uncover design and interface deficiencies of infusion pumps that are currently in use in Intensive Care Units (ICUs). Because these infusion systems cannot be readily replaced due to lease agreements and large-scale institutional purchasing procedures, we argue that it is essential to systematically identify the existing usability problems so that the possible causes of errors can be better understood, passed on to the end-users (e.g., critical care nurses), and used to make policy recommendations. Four raters conducted the heuristic evaluation of the three-channel infusion pump interface. Three raters had a cognitive science background as well as experience with the heuristic evaluation methodology. The fourth rater was a veteran critical care nurse who had extensive experience operating the pumps. The usability experts and the domain expert independently evaluated the user interface and physical design of the infusion pump and generated a list of heuristic violations based upon a set of 14 heuristics developed in previous research. The lists were compiled and then rated on the severity of the violation. From 14 usability heuristics considered in this evaluation of the Infusion Pump, there were 231 violations. Two heuristics, "Consistency" and "Language", were found to have the most violations. The one with fewest violations was "Document". While some heuristic evaluation categories had more violations than others, the most severe ones were not confined to one type. The Primary interface location (e.g., where loading the pump, changing doses, and confirming drug settings takes place) had the most occurrences of heuristic violations. We believe that the Heuristic Evaluation methodology provides a simple and cost-effective approach to discovering medical device deficiencies that affect a patient's general well being. While this methodology provides information for the infusion pump designs of the future, it also identifies important insights concerning equipment that is currently in use in critical care environments.

How Monte Carlo heuristics aid to identify the physical processes of drug release kinetics.

PubMed

Lecca, Paola

2018-01-01

We implement a Monte Carlo heuristic algorithm to model drug release from a solid dosage form. We show that with Monte Carlo simulations it is possible to identify and explain the causes of the unsatisfactory predictive power of current drug release models. It is well known that the power-law, the exponential models, as well as those derived from or inspired by them accurately reproduce only the first 60% of the release curve of a drug from a dosage form. In this study, by using Monte Carlo simulation approaches, we show that these models fit quite accurately almost the entire release profile when the release kinetics is not governed by the coexistence of different physico-chemical mechanisms. We show that the accuracy of the traditional models are comparable with those of Monte Carlo heuristics when these heuristics approximate and oversimply the phenomenology of drug release. This observation suggests to develop and use novel Monte Carlo simulation heuristics able to describe the complexity of the release kinetics, and consequently to generate data more similar to those observed in real experiments. Implementing Monte Carlo simulation heuristics of the drug release phenomenology may be much straightforward and efficient than hypothesizing and implementing from scratch complex mathematical models of the physical processes involved in drug release. Identifying and understanding through simulation heuristics what processes of this phenomenology reproduce the observed data and then formalize them in mathematics may allow avoiding time-consuming, trial-error based regression procedures. Three bullet points, highlighting the customization of the procedure. •An efficient heuristics based on Monte Carlo methods for simulating drug release from solid dosage form encodes is presented. It specifies the model of the physical process in a simple but accurate way in the formula of the Monte Carlo Micro Step (MCS) time interval.•Given the experimentally observed curve of drug release, we point out how Monte Carlo heuristics can be integrated in an evolutionary algorithmic approach to infer the mode of MCS best fitting the observed data, and thus the observed release kinetics.•The software implementing the method is written in R language, the free most used language in the bioinformaticians community.

Quantifying Heuristic Bias: Anchoring, Availability, and Representativeness.

PubMed

Richie, Megan; Josephson, S Andrew

2018-01-01

Construct: Authors examined whether a new vignette-based instrument could isolate and quantify heuristic bias. Heuristics are cognitive shortcuts that may introduce bias and contribute to error. There is no standardized instrument available to quantify heuristic bias in clinical decision making, limiting future study of educational interventions designed to improve calibration of medical decisions. This study presents validity data to support a vignette-based instrument quantifying bias due to the anchoring, availability, and representativeness heuristics. Participants completed questionnaires requiring assignment of probabilities to potential outcomes of medical and nonmedical scenarios. The instrument randomly presented scenarios in one of two versions: Version A, encouraging heuristic bias, and Version B, worded neutrally. The primary outcome was the difference in probability judgments for Version A versus Version B scenario options. Of 167 participants recruited, 139 enrolled. Participants assigned significantly higher mean probability values to Version A scenario options (M = 9.56, SD = 3.75) than Version B (M = 8.98, SD = 3.76), t(1801) = 3.27, p = .001. This result remained significant analyzing medical scenarios alone (Version A, M = 9.41, SD = 3.92; Version B, M = 8.86, SD = 4.09), t(1204) = 2.36, p = .02. Analyzing medical scenarios by heuristic revealed a significant difference between Version A and B for availability (Version A, M = 6.52, SD = 3.32; Version B, M = 5.52, SD = 3.05), t(404) = 3.04, p = .003, and representativeness (Version A, M = 11.45, SD = 3.12; Version B, M = 10.67, SD = 3.71), t(396) = 2.28, p = .02, but not anchoring. Stratifying by training level, students maintained a significant difference between Version A and B medical scenarios (Version A, M = 9.83, SD = 3.75; Version B, M = 9.00, SD = 3.98), t(465) = 2.29, p = .02, but not residents or attendings. Stratifying by heuristic and training level, availability maintained significance for students (Version A, M = 7.28, SD = 3.46; Version B, M = 5.82, SD = 3.22), t(153) = 2.67, p = .008, and residents (Version A, M = 7.19, SD = 3.24; Version B, M = 5.56, SD = 2.72), t(77) = 2.32, p = .02, but not attendings. Authors developed an instrument to isolate and quantify bias produced by the availability and representativeness heuristics, and illustrated the utility of their instrument by demonstrating decreased heuristic bias within medical contexts at higher training levels.

Decision-theoretic control of EUVE telescope scheduling

NASA Technical Reports Server (NTRS)

Hansson, Othar; Mayer, Andrew

1993-01-01

This paper describes a decision theoretic scheduler (DTS) designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems and using probabilistic inference to aggregate this information in light of the features of a given problem. The Bayesian Problem-Solver (BPS) introduced a similar approach to solving single agent and adversarial graph search patterns yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.

Experiments with a decision-theoretic scheduler

NASA Technical Reports Server (NTRS)

Hansson, Othar; Holt, Gerhard; Mayer, Andrew

1992-01-01

This paper describes DTS, a decision-theoretic scheduler designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems, and using probabilistic inference to aggregate this information in light of features of a given problem. BPS, the Bayesian Problem-Solver, introduced a similar approach to solving single-agent and adversarial graph search problems, yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.

The Application of LT-Table in TRIZ Contradiction Resolving Process

NASA Astrophysics Data System (ADS)

Wei, Zihui; Li, Qinghai; Wang, Donglin; Tian, Yumei

TRIZ is used to resolve invention problems. ARIZ is the most powerful systematic method which integrates all of TRIZ heuristics. Definition of ideal final result (IFR), identification of contradictions and resource utilization are main lines of ARIZ. But resource searching of ARIZ has fault of blindness. Alexandr sets up mathematical model of transformation of the hereditary information in an invention problem using the theory of catastrophes, and provides method of resource searching using LT-table. The application of LT-table on contradiction resolving is introduced. Resource utilization using LT-table is joined into ARIZ step as an addition of TRIZ, apply this method in separator paper punching machine design.

Methodology based on genetic heuristics for in-vivo characterizing the patient-specific biomechanical behavior of the breast tissues

PubMed Central

Lago, M. A.; Rúperez, M. J.; Martínez-Martínez, F.; Martínez-Sanchis, S.; Bakic, P. R.; Monserrat, C.

2015-01-01

This paper presents a novel methodology to in-vivo estimate the elastic constants of a constitutive model proposed to characterize the mechanical behavior of the breast tissues. An iterative search algorithm based on genetic heuristics was constructed to in-vivo estimate these parameters using only medical images, thus avoiding invasive measurements of the mechanical response of the breast tissues. For the first time, a combination of overlap and distance coefficients were used for the evaluation of the similarity between a deformed MRI of the breast and a simulation of that deformation. The methodology was validated using breast software phantoms for virtual clinical trials, compressed to mimic MRI-guided biopsies. The biomechanical model chosen to characterize the breast tissues was an anisotropic neo-Hookean hyperelastic model. Results from this analysis showed that the algorithm is able to find the elastic constants of the constitutive equations of the proposed model with a mean relative error of about 10%. Furthermore, the overlap between the reference deformation and the simulated deformation was of around 95% showing the good performance of the proposed methodology. This methodology can be easily extended to characterize the real biomechanical behavior of the breast tissues, which means a great novelty in the field of the simulation of the breast behavior for applications such as surgical planing, surgical guidance or cancer diagnosis. This reveals the impact and relevance of the presented work. PMID:27103760

Methodology based on genetic heuristics for in-vivo characterizing the patient-specific biomechanical behavior of the breast tissues.

PubMed

Lago, M A; Rúperez, M J; Martínez-Martínez, F; Martínez-Sanchis, S; Bakic, P R; Monserrat, C

2015-11-30

This paper presents a novel methodology to in-vivo estimate the elastic constants of a constitutive model proposed to characterize the mechanical behavior of the breast tissues. An iterative search algorithm based on genetic heuristics was constructed to in-vivo estimate these parameters using only medical images, thus avoiding invasive measurements of the mechanical response of the breast tissues. For the first time, a combination of overlap and distance coefficients were used for the evaluation of the similarity between a deformed MRI of the breast and a simulation of that deformation. The methodology was validated using breast software phantoms for virtual clinical trials, compressed to mimic MRI-guided biopsies. The biomechanical model chosen to characterize the breast tissues was an anisotropic neo-Hookean hyperelastic model. Results from this analysis showed that the algorithm is able to find the elastic constants of the constitutive equations of the proposed model with a mean relative error of about 10%. Furthermore, the overlap between the reference deformation and the simulated deformation was of around 95% showing the good performance of the proposed methodology. This methodology can be easily extended to characterize the real biomechanical behavior of the breast tissues, which means a great novelty in the field of the simulation of the breast behavior for applications such as surgical planing, surgical guidance or cancer diagnosis. This reveals the impact and relevance of the presented work.

A Heuristic Placement Selection of Live Virtual Machine Migration for Energy-Saving in Cloud Computing Environment

PubMed Central

Zhao, Jia; Hu, Liang; Ding, Yan; Xu, Gaochao; Hu, Ming

2014-01-01

The field of live VM (virtual machine) migration has been a hotspot problem in green cloud computing. Live VM migration problem is divided into two research aspects: live VM migration mechanism and live VM migration policy. In the meanwhile, with the development of energy-aware computing, we have focused on the VM placement selection of live migration, namely live VM migration policy for energy saving. In this paper, a novel heuristic approach PS-ES is presented. Its main idea includes two parts. One is that it combines the PSO (particle swarm optimization) idea with the SA (simulated annealing) idea to achieve an improved PSO-based approach with the better global search's ability. The other one is that it uses the Probability Theory and Mathematical Statistics and once again utilizes the SA idea to deal with the data obtained from the improved PSO-based process to get the final solution. And thus the whole approach achieves a long-term optimization for energy saving as it has considered not only the optimization of the current problem scenario but also that of the future problem. The experimental results demonstrate that PS-ES evidently reduces the total incremental energy consumption and better protects the performance of VM running and migrating compared with randomly migrating and optimally migrating. As a result, the proposed PS-ES approach has capabilities to make the result of live VM migration events more high-effective and valuable. PMID:25251339

A heuristic placement selection of live virtual machine migration for energy-saving in cloud computing environment.

PubMed

Zhao, Jia; Hu, Liang; Ding, Yan; Xu, Gaochao; Hu, Ming

2014-01-01

The field of live VM (virtual machine) migration has been a hotspot problem in green cloud computing. Live VM migration problem is divided into two research aspects: live VM migration mechanism and live VM migration policy. In the meanwhile, with the development of energy-aware computing, we have focused on the VM placement selection of live migration, namely live VM migration policy for energy saving. In this paper, a novel heuristic approach PS-ES is presented. Its main idea includes two parts. One is that it combines the PSO (particle swarm optimization) idea with the SA (simulated annealing) idea to achieve an improved PSO-based approach with the better global search's ability. The other one is that it uses the Probability Theory and Mathematical Statistics and once again utilizes the SA idea to deal with the data obtained from the improved PSO-based process to get the final solution. And thus the whole approach achieves a long-term optimization for energy saving as it has considered not only the optimization of the current problem scenario but also that of the future problem. The experimental results demonstrate that PS-ES evidently reduces the total incremental energy consumption and better protects the performance of VM running and migrating compared with randomly migrating and optimally migrating. As a result, the proposed PS-ES approach has capabilities to make the result of live VM migration events more high-effective and valuable.

Characterizing internet health information seeking strategies by socioeconomic status: a mixed methods approach.

PubMed

Perez, Susan L; Kravitz, Richard L; Bell, Robert A; Chan, Man Shan; Paterniti, Debora A

2016-08-09

The Internet is valuable for those with limited access to health care services because of its low cost and wealth of information. Our objectives were to investigate how the Internet is used to obtain health-related information and how individuals with differing socioeconomic resources navigate it when presented with a health decision. Study participants were recruited from public settings and social service agencies. Participants listened to one of two clinical scenarios - consistent with influenza or bacterial meningitis - and then conducted an Internet search. Screen-capture video software captured the Internet search. Participant Internet search strategies were analyzed and coded for pre- and post-Internet search guess at diagnosis and information seeking patterns. Individuals who did not have a college degree and were recruited from locations offering social services were categorized as "lower socioeconomic status" (SES); the remainder was categorized as "higher SES." Participants were 78 Internet health information seekers, ranging from 21-35 years of age, who experienced barriers to accessing health care services. Lower-SES individuals were more likely to use an intuitive, rather than deliberative, approach to Internet health information seeking. Lower- and higher-SES participants did not differ in the tendency to make diagnostic guesses based on Internet searches. Lower-SES participants were more likely than their higher-SES counterparts to narrow the scope of their search. Our findings suggest that individuals with different levels of socioeconomic status vary in the heuristics and search patterns they rely upon to direct their searches. The influence and use of credible information in the process of making a decision is associated with education and prior experiences with healthcare services. Those with limited resources may be disadvantaged when turning to the Internet to make a health decision.

Efficient Discovery of De-identification Policies Through a Risk-Utility Frontier

PubMed Central

Xia, Weiyi; Heatherly, Raymond; Ding, Xiaofeng; Li, Jiuyong; Malin, Bradley

2014-01-01

Modern information technologies enable organizations to capture large quantities of person-specific data while providing routine services. Many organizations hope, or are legally required, to share such data for secondary purposes (e.g., validation of research findings) in a de-identified manner. In previous work, it was shown de-identification policy alternatives could be modeled on a lattice, which could be searched for policies that met a prespecified risk threshold (e.g., likelihood of re-identification). However, the search was limited in several ways. First, its definition of utility was syntactic - based on the level of the lattice - and not semantic - based on the actual changes induced in the resulting data. Second, the threshold may not be known in advance. The goal of this work is to build the optimal set of policies that trade-off between privacy risk (R) and utility (U), which we refer to as a R-U frontier. To model this problem, we introduce a semantic definition of utility, based on information theory, that is compatible with the lattice representation of policies. To solve the problem, we initially build a set of policies that define a frontier. We then use a probability-guided heuristic to search the lattice for policies likely to update the frontier. To demonstrate the effectiveness of our approach, we perform an empirical analysis with the Adult dataset of the UCI Machine Learning Repository. We show that our approach can construct a frontier closer to optimal than competitive approaches by searching a smaller number of policies. In addition, we show that a frequently followed de-identification policy (i.e., the Safe Harbor standard of the HIPAA Privacy Rule) is suboptimal in comparison to the frontier discovered by our approach. PMID:25520961

Efficient Discovery of De-identification Policies Through a Risk-Utility Frontier.

PubMed

Xia, Weiyi; Heatherly, Raymond; Ding, Xiaofeng; Li, Jiuyong; Malin, Bradley

2013-01-01

Modern information technologies enable organizations to capture large quantities of person-specific data while providing routine services. Many organizations hope, or are legally required, to share such data for secondary purposes (e.g., validation of research findings) in a de-identified manner. In previous work, it was shown de-identification policy alternatives could be modeled on a lattice, which could be searched for policies that met a prespecified risk threshold (e.g., likelihood of re-identification). However, the search was limited in several ways. First, its definition of utility was syntactic - based on the level of the lattice - and not semantic - based on the actual changes induced in the resulting data. Second, the threshold may not be known in advance. The goal of this work is to build the optimal set of policies that trade-off between privacy risk (R) and utility (U), which we refer to as a R-U frontier. To model this problem, we introduce a semantic definition of utility, based on information theory, that is compatible with the lattice representation of policies. To solve the problem, we initially build a set of policies that define a frontier. We then use a probability-guided heuristic to search the lattice for policies likely to update the frontier. To demonstrate the effectiveness of our approach, we perform an empirical analysis with the Adult dataset of the UCI Machine Learning Repository. We show that our approach can construct a frontier closer to optimal than competitive approaches by searching a smaller number of policies. In addition, we show that a frequently followed de-identification policy (i.e., the Safe Harbor standard of the HIPAA Privacy Rule) is suboptimal in comparison to the frontier discovered by our approach.

UAV Mission Planning under Uncertainty

DTIC Science & Technology

2006-06-01

algorithm , adapted from [13] . 57 4-5 Robust Optimization considers only a subset of the feasible region . 61 5-1 Overview of simulation with parameter...incorporates the robust optimization method suggested by Bertsimas and Sim [12], and is solved with a standard Branch- and-Cut algorithm . The chapter... algorithms , and the heuristic methods of Local Search methods and Simulated Annealing. With each method, we attempt to give a review of research that has

A Formal Model of Ambiguity and its Applications in Machine Translation

DTIC Science & Technology

2010-01-01

structure indicates linguisti- cally implausible segmentation that might be generated using dictionary - driven approaches...derivation. As was done in the monolingual case, the functions LHS, RHSi, RHSo and υ can be extended to a derivation δ. D(q) where q ∈V denotes the... monolingual parses. My algorithm runs more efficiently than O(n6) with many grammars (including those that required using heuristic search with other parsers

A modified generalized extremal optimization algorithm for the quay crane scheduling problem with interference constraints

NASA Astrophysics Data System (ADS)

Guo, Peng; Cheng, Wenming; Wang, Yi

2014-10-01

The quay crane scheduling problem (QCSP) determines the handling sequence of tasks at ship bays by a set of cranes assigned to a container vessel such that the vessel's service time is minimized. A number of heuristics or meta-heuristics have been proposed to obtain the near-optimal solutions to overcome the NP-hardness of the problem. In this article, the idea of generalized extremal optimization (GEO) is adapted to solve the QCSP with respect to various interference constraints. The resulting GEO is termed the modified GEO. A randomized searching method for neighbouring task-to-QC assignments to an incumbent task-to-QC assignment is developed in executing the modified GEO. In addition, a unidirectional search decoding scheme is employed to transform a task-to-QC assignment to an active quay crane schedule. The effectiveness of the developed GEO is tested on a suite of benchmark problems introduced by K.H. Kim and Y.M. Park in 2004 (European Journal of Operational Research, Vol. 156, No. 3). Compared with other well-known existing approaches, the experiment results show that the proposed modified GEO is capable of obtaining the optimal or near-optimal solution in a reasonable time, especially for large-sized problems.

Cognitive mechanisms of insight: the role of heuristics and representational change in solving the eight-coin problem.

PubMed

Öllinger, Michael; Jones, Gary; Faber, Amory H; Knoblich, Günther

2013-05-01

The 8-coin insight problem requires the problem solver to move 2 coins so that each coin touches exactly 3 others. Ormerod, MacGregor, and Chronicle (2002) explained differences in task performance across different versions of the 8-coin problem using the availability of particular moves in a 2-dimensional search space. We explored 2 further explanations by developing 6 new versions of the 8-coin problem in order to investigate the influence of grouping and self-imposed constraints on solutions. The results identified 2 sources of problem difficulty: first, the necessity to overcome the constraint that a solution can be found in 2-dimensional space and, second, the necessity to decompose perceptual groupings. A detailed move analysis suggested that the selection of moves was driven by the established representation rather than the application of the appropriate heuristics. Both results support the assumptions of representational change theory (Ohlsson, 1992).

Hybrid flower pollination algorithm strategies for t-way test suite generation.

PubMed

Nasser, Abdullah B; Zamli, Kamal Z; Alsewari, AbdulRahman A; Ahmed, Bestoun S

2018-01-01

The application of meta-heuristic algorithms for t-way testing has recently become prevalent. Consequently, many useful meta-heuristic algorithms have been developed on the basis of the implementation of t-way strategies (where t indicates the interaction strength). Mixed results have been reported in the literature to highlight the fact that no single strategy appears to be superior compared with other configurations. The hybridization of two or more algorithms can enhance the overall search capabilities, that is, by compensating the limitation of one algorithm with the strength of others. Thus, hybrid variants of the flower pollination algorithm (FPA) are proposed in the current work. Four hybrid variants of FPA are considered by combining FPA with other algorithmic components. The experimental results demonstrate that FPA hybrids overcome the problems of slow convergence in the original FPA and offers statistically superior performance compared with existing t-way strategies in terms of test suite size.

Hybrid flower pollination algorithm strategies for t-way test suite generation

PubMed Central

Zamli, Kamal Z.; Alsewari, AbdulRahman A.

2018-01-01

The application of meta-heuristic algorithms for t-way testing has recently become prevalent. Consequently, many useful meta-heuristic algorithms have been developed on the basis of the implementation of t-way strategies (where t indicates the interaction strength). Mixed results have been reported in the literature to highlight the fact that no single strategy appears to be superior compared with other configurations. The hybridization of two or more algorithms can enhance the overall search capabilities, that is, by compensating the limitation of one algorithm with the strength of others. Thus, hybrid variants of the flower pollination algorithm (FPA) are proposed in the current work. Four hybrid variants of FPA are considered by combining FPA with other algorithmic components. The experimental results demonstrate that FPA hybrids overcome the problems of slow convergence in the original FPA and offers statistically superior performance compared with existing t-way strategies in terms of test suite size. PMID:29718918

Size-guided multi-seed heuristic method for geometry optimization of clusters: Application to benzene clusters.

PubMed

Takeuchi, Hiroshi

2018-05-08

Since searching for the global minimum on the potential energy surface of a cluster is very difficult, many geometry optimization methods have been proposed, in which initial geometries are randomly generated and subsequently improved with different algorithms. In this study, a size-guided multi-seed heuristic method is developed and applied to benzene clusters. It produces initial configurations of the cluster with n molecules from the lowest-energy configurations of the cluster with n - 1 molecules (seeds). The initial geometries are further optimized with the geometrical perturbations previously used for molecular clusters. These steps are repeated until the size n satisfies a predefined one. The method locates putative global minima of benzene clusters with up to 65 molecules. The performance of the method is discussed using the computational cost, rates to locate the global minima, and energies of initial geometries. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

The normalization heuristic: an untested hypothesis that may misguide medical decisions.

PubMed

Aberegg, Scott K; O'Brien, James M

2009-06-01

Medical practice is increasingly informed by the evidence from randomized controlled trials. When such evidence is not available, clinical hypotheses based on pathophysiological reasoning and common sense guide clinical decision making. One commonly utilized general clinical hypothesis is the assumption that normalizing abnormal laboratory values and physiological parameters will lead to improved patient outcomes. We refer to the general use of this clinical hypothesis to guide medical therapeutics as the "normalization heuristic". In this paper, we operationally define this heuristic and discuss its limitations as a rule of thumb for clinical decision making. We review historical and contemporaneous examples of normalization practices as empirical evidence for the normalization heuristic and to highlight its frailty as a guide for clinical decision making.

What is behind the priority heuristic? A mathematical analysis and comment on Brandstätter, Gigerenzer, and Hertwig (2006).

PubMed

Rieger, Marc Oliver; Wang, Mei

2008-01-01

Comments on the article by E. Brandstätter, G. Gigerenzer, and R. Hertwig. The authors discuss the priority heuristic, a recent model for decisions under risk. They reanalyze the experimental validity of this approach and discuss how these results compare with cumulative prospect theory, the currently most established model in behavioral economics. They also discuss how general models for decisions under risk based on a heuristic approach can be understood mathematically to gain some insight in their limitations. They finally consider whether the priority heuristic model can lead to some understanding of the decision process of individuals or whether it is better seen as an as-if model. (c) 2008 APA, all rights reserved

Fourth Graders' Heuristic Problem-Solving Behavior.

ERIC Educational Resources Information Center

Lee, Kil S.

1982-01-01

Eight boys and eight girls from a rural elementary school participated in the investigation. Specific heuristics were adopted from Polya; and the students selected represented two substages of Piaget's concrete operational stage. Five hypotheses were generated, based on observed results and the study's theoretical rationale. (MP)

An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas

PubMed Central

Shao, Jing; Yang, Lina; Peng, Ling; Chi, Tianhe; Wang, Xiaomeng

2015-01-01

China is facing ecological and environmental challenges as its urban growth rate continues to rise, and zoning protected ecological areas is recognized as an effective response measure. Zoning inherently involves both site attributes and aggregation attributes, and the combination of mathematical models and heuristic algorithms have proven advantageous. In this article, an improved artificial bee colony (IABC)-based approach is proposed for zoning protected ecological areas at a regional scale. Three main improvements were made: the first is the use of multiple strategies to generate the initial bee population of a specific quality and diversity, the second is an exploitation search procedure to generate neighbor solutions combining “replace” and “alter” operations, and the third is a “swap” strategy to enable a local search for the iterative optimal solution. The IABC algorithm was verified using simulated data. Then it was applied to define an optimum scheme of protected ecological areas of Sanya (in the Hainan province of China), and a reasonable solution was obtained. Finally, a comparison experiment with other methods (agent-based land allocation model, ant colony optimization, and density slicing) was conducted and demonstrated that the IABC algorithm was more effective and efficient than the other methods. Through this study, we aimed to provide a scientifically sound, practical approach for zoning procedures. PMID:26394148

The HMMER Web Server for Protein Sequence Similarity Search.

PubMed

Prakash, Ananth; Jeffryes, Matt; Bateman, Alex; Finn, Robert D

2017-12-08

Protein sequence similarity search is one of the most commonly used bioinformatics methods for identifying evolutionarily related proteins. In general, sequences that are evolutionarily related share some degree of similarity, and sequence-search algorithms use this principle to identify homologs. The requirement for a fast and sensitive sequence search method led to the development of the HMMER software, which in the latest version (v3.1) uses a combination of sophisticated acceleration heuristics and mathematical and computational optimizations to enable the use of profile hidden Markov models (HMMs) for sequence analysis. The HMMER Web server provides a common platform by linking the HMMER algorithms to databases, thereby enabling the search for homologs, as well as providing sequence and functional annotation by linking external databases. This unit describes three basic protocols and two alternate protocols that explain how to use the HMMER Web server using various input formats and user defined parameters. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

Mindfulness-Based Symptom and Stress Management Apps for Adults With Chronic Lung Disease: Systematic Search in App Stores

PubMed Central

2018-01-01

Background Up to 70% of lung cancer survivors are affected by chronic obstructive pulmonary disease (COPD), a common, debilitating, comorbid disease. Lung cancer and COPD are both characterized by symptoms such as breathlessness, fatigue, and psychological distress. These distressing chronic symptoms are exacerbated by stress and detract from an individual’s quality of life. Objective The aim of this study was to identify and evaluate evidence-based, commercially available apps for promoting mindfulness-based strategies among adults with a COPD or lung cancer history (ie, chronic lung disease). Methods For this review, an interdisciplinary research team used 19 keyword combinations in the search engines of Google and iOS app stores in May 2017. Evaluations were conducted on the apps’ (1) content, (2) usability heuristics, (3) grade-level readability, and (4) cultural sensitivity. Results The search resulted in 768 apps (508 in iOS and 260 in Google stores). A total of 9 apps met the inclusion criteria and received further evaluation. Only 1 app had below an eighth-grade reading level; the ninth one did not have enough text to calculate a readability score. None of the 9 apps met the cultural sensitivity evaluation criteria. Conclusions This systematic review identified critical design flaws that may affect the ease of using the apps in this study. Few mobile apps promote mindfulness-based strategies among adults with chronic lung disease (ie, COPD or lung cancer or both), but those that exist, overall, do not meet the latest scientific evidence. Recommendations include more stringent regulation of health-related apps, use of evidence-based frameworks and participatory design processes, following evidence-based usability practices, use of culturally sensitive language and images, and ensuring that content is written in plain language. PMID:29764800

Operational Planning of Channel Airlift Missions Using Forecasted Demand

DTIC Science & Technology

2013-03-01

tailored to the specific problem ( Metaheuristics , 2005). As seen in the section Cargo Loading Algorithm , heuristic methods are often iterative...that are equivalent to the forecasted cargo amount. The simulated pallets are then used in a heuristic cargo loading algorithm . The loading... algorithm places cargo onto available aircraft (based on real schedules) given the date and the destination and outputs statistics based on the aircraft ton

A Simulation of Readiness-Based Sparing Policies

DTIC Science & Technology

2017-06-01

variant of a greedy heuristic algorithm to set stock levels and estimate overall WS availability. Our discrete event simulation is then used to test the...available in the optimization tools. 14. SUBJECT TERMS readiness-based sparing, discrete event simulation, optimization, multi-indenture...variant of a greedy heuristic algorithm to set stock levels and estimate overall WS availability. Our discrete event simulation is then used to test the

Neural basis of scientific innovation induced by heuristic prototype.

PubMed

Luo, Junlong; Li, Wenfu; Qiu, Jiang; Wei, Dongtao; Liu, Yijun; Zhang, Qinlin

2013-01-01

A number of major inventions in history have been based on bionic imitation. Heuristics, by applying biological systems to the creation of artificial devices and machines, might be one of the most critical processes in scientific innovation. In particular, prototype heuristics propositions that innovation may engage automatic activation of a prototype such as a biological system to form novel associations between a prototype's function and problem-solving. We speculated that the cortical dissociation between the automatic activation and forming novel associations in innovation is critical point to heuristic creativity. In the present study, novel and old scientific innovations (NSI and OSI) were selected as experimental materials in using learning-testing paradigm to explore the neural basis of scientific innovation induced by heuristic prototype. College students were required to resolve NSI problems (to which they did not know the answers) and OSI problems (to which they knew the answers). From two fMRI experiments, our results showed that the subjects could resolve NSI when provided with heuristic prototypes. In Experiment 1, it was found that the lingual gyrus (LG; BA18) might be related to prototype heuristics in college students resolving NSI after learning a relative prototype. In Experiment 2, the LG (BA18) and precuneus (BA31) were significantly activated for NSI compared to OSI when college students learned all prototypes one day before the test. In addition, the mean beta-values of these brain regions of NSI were all correlated with the behavior accuracy of NSI. As our hypothesis indicated, the findings suggested that the LG might be involved in forming novel associations using heuristic information, while the precuneus might be involved in the automatic activation of heuristic prototype during scientific innovation.

Neural Basis of Scientific Innovation Induced by Heuristic Prototype

PubMed Central

Qiu, Jiang; Wei, Dongtao; Liu, Yijun; Zhang, Qinlin

2013-01-01

A number of major inventions in history have been based on bionic imitation. Heuristics, by applying biological systems to the creation of artificial devices and machines, might be one of the most critical processes in scientific innovation. In particular, prototype heuristics propositions that innovation may engage automatic activation of a prototype such as a biological system to form novel associations between a prototype's function and problem-solving. We speculated that the cortical dissociation between the automatic activation and forming novel associations in innovation is critical point to heuristic creativity. In the present study, novel and old scientific innovations (NSI and OSI) were selected as experimental materials in using learning-testing paradigm to explore the neural basis of scientific innovation induced by heuristic prototype. College students were required to resolve NSI problems (to which they did not know the answers) and OSI problems (to which they knew the answers). From two fMRI experiments, our results showed that the subjects could resolve NSI when provided with heuristic prototypes. In Experiment 1, it was found that the lingual gyrus (LG; BA18) might be related to prototype heuristics in college students resolving NSI after learning a relative prototype. In Experiment 2, the LG (BA18) and precuneus (BA31) were significantly activated for NSI compared to OSI when college students learned all prototypes one day before the test. In addition, the mean beta-values of these brain regions of NSI were all correlated with the behavior accuracy of NSI. As our hypothesis indicated, the findings suggested that the LG might be involved in forming novel associations using heuristic information, while the precuneus might be involved in the automatic activation of heuristic prototype during scientific innovation. PMID:23372641

Toward a More Usable Home-Based Video Telemedicine System: A Heuristic Evaluation of the Clinician User Interfaces of Home-Based Video Telemedicine Systems

PubMed Central

Welch, Brandon; Brinda, FNU

2017-01-01

Background Telemedicine is the use of technology to provide and support health care when distance separates the clinical service and the patient. Home-based telemedicine systems involve the use of such technology for medical support and care connecting the patient from the comfort of their homes with the clinician. In order for such a system to be used extensively, it is necessary to understand not only the issues faced by the patients in using them but also the clinician. Objectives The aim of this study was to conduct a heuristic evaluation of 4 telemedicine software platforms—Doxy.me, Polycom, Vidyo, and VSee—to assess possible problems and limitations that could affect the usability of the system from the clinician’s perspective. Methods It was found that 5 experts individually evaluated all four systems using Nielsen’s list of heuristics, classifying the issues based on a severity rating scale. Results A total of 46 unique problems were identified by the experts. The heuristics most frequently violated were visibility of system status and Error prevention amounting to 24% (11/46 issues) each. Esthetic and minimalist design was second contributing to 13% (6/46 issues) of the total errors. Conclusions Heuristic evaluation coupled with a severity rating scale was found to be an effective method for identifying problems with the systems. Prioritization of these problems based on the rating provides a good starting point for resolving the issues affecting these platforms. There is a need for better transparency and a more streamlined approach for how physicians use telemedicine systems. Visibility of the system status and speaking the users’ language are keys for achieving this. PMID:28438724

Autobiographical Elaboration Reduces Memory Distortion: Cognitive Operations and the Distinctiveness Heuristic

ERIC Educational Resources Information Center

McDonough, Ian M.; Gallo, David A.

2008-01-01

Retrieval monitoring enhances episodic memory accuracy. For instance, false recognition is reduced when participants base their decisions on more distinctive recollections, a retrieval monitoring process called the distinctiveness heuristic. The experiments reported here tested the hypothesis that autobiographical elaboration during study (i.e.,…

A simple and effective figure caption detection system for old-style documents

NASA Astrophysics Data System (ADS)

Liu, Zongyi; Zhou, Hanning

2011-01-01

Identifying figure captions has wide applications in producing high quality e-books such as kindle books or ipad books. In this paper, we present a rule-based system to detect horizontal figure captions in old-style documents. Our algorithm consists of three steps: (i) segment images into regions of different types such as text and figures, (ii) search the best caption region candidate based on heuristic rules such as region alignments and distances, and (iii) expand caption regions identified in step (ii) with its neighboring text-regions in order to correct oversegmentation errors. We test our algorithm using 81 images collected from old-style books, with each image containing at least one figure area. We show that the approach is able to correctly detect figure captions from images with different layouts, and we also measure its performances in terms of both precision rate and recall rate.

Enhanced ICP for the Registration of Large-Scale 3D Environment Models: An Experimental Study

PubMed Central

Han, Jianda; Yin, Peng; He, Yuqing; Gu, Feng

2016-01-01

One of the main applications of mobile robots is the large-scale perception of the outdoor environment. One of the main challenges of this application is fusing environmental data obtained by multiple robots, especially heterogeneous robots. This paper proposes an enhanced iterative closest point (ICP) method for the fast and accurate registration of 3D environmental models. First, a hierarchical searching scheme is combined with the octree-based ICP algorithm. Second, an early-warning mechanism is used to perceive the local minimum problem. Third, a heuristic escape scheme based on sampled potential transformation vectors is used to avoid local minima and achieve optimal registration. Experiments involving one unmanned aerial vehicle and one unmanned surface vehicle were conducted to verify the proposed technique. The experimental results were compared with those of normal ICP registration algorithms to demonstrate the superior performance of the proposed method. PMID:26891298

Identification of Disease Critical Genes Using Collective Meta-heuristic Approaches: An Application to Preeclampsia.

PubMed

Biswas, Surama; Dutta, Subarna; Acharyya, Sriyankar

2017-12-01

Identifying a small subset of disease critical genes out of a large size of microarray gene expression data is a challenge in computational life sciences. This paper has applied four meta-heuristic algorithms, namely, honey bee mating optimization (HBMO), harmony search (HS), differential evolution (DE) and genetic algorithm (basic version GA) to find disease critical genes of preeclampsia which affects women during gestation. Two hybrid algorithms, namely, HBMO-kNN and HS-kNN have been newly proposed here where kNN (k nearest neighbor classifier) is used for sample classification. Performances of these new approaches have been compared with other two hybrid algorithms, namely, DE-kNN and SGA-kNN. Three datasets of different sizes have been used. In a dataset, the set of genes found common in the output of each algorithm is considered here as disease critical genes. In different datasets, the percentage of classification or classification accuracy of meta-heuristic algorithms varied between 92.46 and 100%. HBMO-kNN has the best performance (99.64-100%) in almost all data sets. DE-kNN secures the second position (99.42-100%). Disease critical genes obtained here match with clinically revealed preeclampsia genes to a large extent.

Testing a videogame intervention to recalibrate physician heuristics in trauma triage: study protocol for a randomized controlled trial.

PubMed

Mohan, Deepika; Rosengart, Matthew R; Fischhoff, Baruch; Angus, Derek C; Farris, Coreen; Yealy, Donald M; Wallace, David J; Barnato, Amber E

2016-11-11

Between 30 and 40 % of patients with severe injuries receive treatment at non-trauma centers (under-triage), largely because of physician decision making. Existing interventions to improve triage by physicians ignore the role that intuition (heuristics) plays in these decisions. One such heuristic is to form an initial impression based on representativeness (how typical does a patient appear of one with severe injuries). We created a video game (Night Shift) to recalibrate physician's representativeness heuristic in trauma triage. We developed Night Shift in collaboration with emergency medicine physicians, trauma surgeons, behavioral scientists, and game designers. Players take on the persona of Andy Jordan, an emergency medicine physician, who accepts a new job in a small town. Through a series of cases that go awry, they gain experience with the contextual cues that distinguish patients with minor and severe injuries (based on the theory of analogical encoding) and receive emotionally-laden feedback on their performance (based on the theory of narrative engagement). The planned study will compare the effect of Night Shift with that of an educational program on physician triage decisions and on physician heuristics. Psychological theory predicts that cognitive load increases reliance on heuristics, thereby increasing the under-triage rate when heuristics are poorly calibrated. We will randomize physicians (n = 366) either to play the game or to review an educational program, and will assess performance using a validated virtual simulation. The validated simulation includes both control and cognitive load conditions. We will compare rates of under-triage after exposure to the two interventions (primary outcome) and will compare the effect of cognitive load on physicians' under-triage rates (secondary outcome). We hypothesize that: a) physicians exposed to Night Shift will have lower rates of under-triage compared to those exposed to the educational program, and b) cognitive load will not degrade triage performance among physicians exposed to Night Shift as much as it will among those exposed to the educational program. Serious games offer a new approach to the problem of poorly-calibrated heuristics in trauma triage. The results of this trial will contribute to the understanding of physician quality improvement and the efficacy of video games as behavioral interventions. clinicaltrials.gov; NCT02857348 ; August 2, 2016.

Scoring-and-unfolding trimmed tree assembler: concepts, constructs and comparisons.

PubMed

Narzisi, Giuseppe; Mishra, Bud

2011-01-15

Mired by its connection to a well-known -complete combinatorial optimization problem-namely, the Shortest Common Superstring Problem (SCSP)-historically, the whole-genome sequence assembly (WGSA) problem has been assumed to be amenable only to greedy and heuristic methods. By placing efficiency as their first priority, these methods opted to rely only on local searches, and are thus inherently approximate, ambiguous or error prone, especially, for genomes with complex structures. Furthermore, since choice of the best heuristics depended critically on the properties of (e.g. errors in) the input data and the available long range information, these approaches hindered designing an error free WGSA pipeline. We dispense with the idea of limiting the solutions to just the approximated ones, and instead favor an approach that could potentially lead to an exhaustive (exponential-time) search of all possible layouts. Its computational complexity thus must be tamed through a constrained search (Branch-and-Bound) and quick identification and pruning of implausible overlays. For his purpose, such a method necessarily relies on a set of score functions (oracles) that can combine different structural properties (e.g. transitivity, coverage, physical maps, etc.). We give a detailed description of this novel assembly framework, referred to as Scoring-and-Unfolding Trimmed Tree Assembler (SUTTA), and present experimental results on several bacterial genomes using next-generation sequencing technology data. We also report experimental evidence that the assembly quality strongly depends on the choice of the minimum overlap parameter k. SUTTA's binaries are freely available to non-profit institutions for research and educational purposes at http://www.bioinformatics.nyu.edu.

When Does Diversity Trump Ability (and Vice Versa) in Group Decision Making? A Simulation Study

PubMed Central

Luan, Shenghua; Katsikopoulos, Konstantinos V.; Reimer, Torsten

2012-01-01

It is often unclear which factor plays a more critical role in determining a group's performance: the diversity among members of the group or their individual abilities. In this study, we addressed this “diversity vs. ability” issue in a decision-making task. We conducted three simulation studies in which we manipulated agents' individual ability (or accuracy, in the context of our investigation) and group diversity by varying (1) the heuristics agents used to search task-relevant information (i.e., cues); (2) the size of their groups; (3) how much they had learned about a good cue search order; and (4) the magnitude of errors in the information they searched. In each study, we found that a manipulation reducing agents' individual accuracy simultaneously increased their group's diversity, leading to a conflict between the two. These conflicts enabled us to identify certain conditions under which diversity trumps individual accuracy, and vice versa. Specifically, we found that individual accuracy is more important in task environments in which cues differ greatly in the quality of their information, and diversity matters more when such differences are relatively small. Changing the size of a group and the amount of learning by an agent had a limited impact on this general effect of task environment. Furthermore, we found that a group achieves its highest accuracy when there is an intermediate amount of errors in the cue information, regardless of the environment and the heuristic used, an effect that we believe has not been previously reported and warrants further investigation. PMID:22359562

Incorporating metacognition into morbidity and mortality rounds: The next frontier in quality improvement.

PubMed

Katz, David; Detsky, Allan S

2016-02-01

This Perspective proposes the introduction of metacognition (thinking about thinking) into the existing format of hospital-based morbidity and mortality rounds. It is placed in the context of historical movements to advance quality improvement by expanding the spectrum of the causes of medical error from systems-based issues to flawed human decision-making capabilities. We suggest that the current approach that focuses on systems-based issues can be improved by exploiting the opportunities to educate physicians about predictable errors committed by reliance on cognitive heuristics. In addition, because the field of educating clinicians about cognitive heuristics has shown mixed results, this proposal represents fertile ground for further research. Educating clinicians about cognitive heuristics may improve metacognition and perhaps be the next frontier in quality improvement. © 2015 Society of Hospital Medicine.

Fast optimization of multipump Raman amplifiers based on a simplified wavelength and power budget heuristic

NASA Astrophysics Data System (ADS)

de O. Rocha, Helder R.; Castellani, Carlos E. S.; Silva, Jair A. L.; Pontes, Maria J.; Segatto, Marcelo E. V.

2015-01-01

We report a simple budget heuristic for a fast optimization of multipump Raman amplifiers based on the reallocation of the pump wavelengths and the optical powers. A set of different optical fibers are analyzed as the Raman gain medium, and a four-pump amplifier setup is optimized for each of them in order to achieve ripples close to 1 dB and gains up to 20 dB in the C band. Later, a comparison between our proposed heuristic and a multiobjective optimization based on a nondominated sorting genetic algorithm is made, highlighting the fact that our new approach can give similar solutions after at least an order of magnitude fewer iterations. The results shown in this paper can potentially pave the way for real-time optimization of multipump Raman amplifier systems.

Neural substrates of similarity and rule-based strategies in judgment

PubMed Central

von Helversen, Bettina; Karlsson, Linnea; Rasch, Björn; Rieskamp, Jörg

2014-01-01

Making accurate judgments is a core human competence and a prerequisite for success in many areas of life. Plenty of evidence exists that people can employ different judgment strategies to solve identical judgment problems. In categorization, it has been demonstrated that similarity-based and rule-based strategies are associated with activity in different brain regions. Building on this research, the present work tests whether solving two identical judgment problems recruits different neural substrates depending on people's judgment strategies. Combining cognitive modeling of judgment strategies at the behavioral level with functional magnetic resonance imaging (fMRI), we compare brain activity when using two archetypal judgment strategies: a similarity-based exemplar strategy and a rule-based heuristic strategy. Using an exemplar-based strategy should recruit areas involved in long-term memory processes to a larger extent than a heuristic strategy. In contrast, using a heuristic strategy should recruit areas involved in the application of rules to a larger extent than an exemplar-based strategy. Largely consistent with our hypotheses, we found that using an exemplar-based strategy led to relatively higher BOLD activity in the anterior prefrontal and inferior parietal cortex, presumably related to retrieval and selective attention processes. In contrast, using a heuristic strategy led to relatively higher activity in areas in the dorsolateral prefrontal and the temporal-parietal cortex associated with cognitive control and information integration. Thus, even when people solve identical judgment problems, different neural substrates can be recruited depending on the judgment strategy involved. PMID:25360099

The probability heuristics model of syllogistic reasoning.

PubMed

Chater, N; Oaksford, M

1999-03-01

A probability heuristic model (PHM) for syllogistic reasoning is proposed. An informational ordering over quantified statements suggests simple probability based heuristics for syllogistic reasoning. The most important is the "min-heuristic": choose the type of the least informative premise as the type of the conclusion. The rationality of this heuristic is confirmed by an analysis of the probabilistic validity of syllogistic reasoning which treats logical inference as a limiting case of probabilistic inference. A meta-analysis of past experiments reveals close fits with PHM. PHM also compares favorably with alternative accounts, including mental logics, mental models, and deduction as verbal reasoning. Crucially, PHM extends naturally to generalized quantifiers, such as Most and Few, which have not been characterized logically and are, consequently, beyond the scope of current mental logic and mental model theories. Two experiments confirm the novel predictions of PHM when generalized quantifiers are used in syllogistic arguments. PHM suggests that syllogistic reasoning performance may be determined by simple but rational informational strategies justified by probability theory rather than by logic. Copyright 1999 Academic Press.

A derived heuristics based multi-objective optimization procedure for micro-grid scheduling

NASA Astrophysics Data System (ADS)

Li, Xin; Deb, Kalyanmoy; Fang, Yanjun

2017-06-01

With the availability of different types of power generators to be used in an electric micro-grid system, their operation scheduling as the load demand changes with time becomes an important task. Besides satisfying load balance constraints and the generator's rated power, several other practicalities, such as limited availability of grid power and restricted ramping of power output from generators, must all be considered during the operation scheduling process, which makes it difficult to decide whether the optimization results are accurate and satisfactory. In solving such complex practical problems, heuristics-based customized optimization algorithms are suggested. However, due to nonlinear and complex interactions of variables, it is difficult to come up with heuristics in such problems off-hand. In this article, a two-step strategy is proposed in which the first task deciphers important heuristics about the problem and the second task utilizes the derived heuristics to solve the original problem in a computationally fast manner. Specifically, the specific operation scheduling is considered from a two-objective (cost and emission) point of view. The first task develops basic and advanced level knowledge bases offline from a series of prior demand-wise optimization runs and then the second task utilizes them to modify optimized solutions in an application scenario. Results on island and grid connected modes and several pragmatic formulations of the micro-grid operation scheduling problem clearly indicate the merit of the proposed two-step procedure.

Community-aware task allocation for social networked multiagent systems.

PubMed

Wang, Wanyuan; Jiang, Yichuan

2014-09-01

In this paper, we propose a novel community-aware task allocation model for social networked multiagent systems (SN-MASs), where the agent' cooperation domain is constrained in community and each agent can negotiate only with its intracommunity member agents. Under such community-aware scenarios, we prove that it remains NP-hard to maximize system overall profit. To solve this problem effectively, we present a heuristic algorithm that is composed of three phases: 1) task selection: select the desirable task to be allocated preferentially; 2) allocation to community: allocate the selected task to communities based on a significant task-first heuristics; and 3) allocation to agent: negotiate resources for the selected task based on a nonoverlap agent-first and breadth-first resource negotiation mechanism. Through the theoretical analyses and experiments, the advantages of our presented heuristic algorithm and community-aware task allocation model are validated. 1) Our presented heuristic algorithm performs very closely to the benchmark exponential brute-force optimal algorithm and the network flow-based greedy algorithm in terms of system overall profit in small-scale applications. Moreover, in the large-scale applications, the presented heuristic algorithm achieves approximately the same overall system profit, but significantly reduces the computational load compared with the greedy algorithm. 2) Our presented community-aware task allocation model reduces the system communication cost compared with the previous global-aware task allocation model and improves the system overall profit greatly compared with the previous local neighbor-aware task allocation model.

Generalized “Satisfaction of Search”: Adverse Influences on Dual-Target Search Accuracy

PubMed Central

Fleck, Mathias S.; Samei, Ehsan; Mitroff, Stephen R.

2013-01-01

The successful detection of a target in a radiological search can reduce the detectability of a second target, a phenomenon termed satisfaction of search (SOS). Given the potential consequences, here we investigate the generality of SOS with the goal of simultaneously informing radiology, cognitive psychology, and nonmedical searches such as airport luggage screening. Ten experiments utilizing nonmedical searches and untrained searchers suggest that SOS is affected by a diverse array of factors, including (1) the relative frequency of different target types, (2) external pressures (reward and time), and (3) expectations about the number of targets present. Collectively, these experiments indicate that SOS arises when searchers have a biased expectation about the low likelihood of specific targets or events, and when they are under pressure to perform efficiently. This first demonstration of SOS outside of radiology implicates a general heuristic applicable to many kinds of searches. In an example like airport luggage screening, the current data suggest that the detection of an easy-to-spot target (e.g., a water bottle) might reduce detection of a hard-to-spot target (e.g., a box cutter). PMID:20350044

A similarity score-based two-phase heuristic approach to solve the dynamic cellular facility layout for manufacturing systems

NASA Astrophysics Data System (ADS)

Kumar, Ravi; Singh, Surya Prakash

2017-11-01

The dynamic cellular facility layout problem (DCFLP) is a well-known NP-hard problem. It has been estimated that the efficient design of DCFLP reduces the manufacturing cost of products by maintaining the minimum material flow among all machines in all cells, as the material flow contributes around 10-30% of the total product cost. However, being NP hard, solving the DCFLP optimally is very difficult in reasonable time. Therefore, this article proposes a novel similarity score-based two-phase heuristic approach to solve the DCFLP optimally considering multiple products in multiple times to be manufactured in the manufacturing layout. In the first phase of the proposed heuristic, a machine-cell cluster is created based on similarity scores between machines. This is provided as an input to the second phase to minimize inter/intracell material handling costs and rearrangement costs over the entire planning period. The solution methodology of the proposed approach is demonstrated. To show the efficiency of the two-phase heuristic approach, 21 instances are generated and solved using the optimization software package LINGO. The results show that the proposed approach can optimally solve the DCFLP in reasonable time.

Why some women have an optimistic or a pessimistic bias about their breast cancer risk: experiences, heuristics, and knowledge of risk factors.

PubMed

Katapodi, Maria C; Dodd, Marylin J; Facione, Noreen C; Humphreys, Janice C; Lee, Kathryn A

2010-01-01

Perceived risk to a health problem is formed by inferential rules called heuristics and by comparative judgments that assess how one's risk compares to the risk of others. The purpose of this cross-sectional, community-based survey was to examine how experiences with breast cancer, knowledge of risk factors, and specific heuristics inform risk judgments for oneself, for friends/peers, and comparative judgments for breast cancer (risk friends/peers - risk self). We recruited an English-speaking, multicultural (57% nonwhite) sample of 184 middle-aged (47 + or - 12 years old), well-educated women. Fifty percent of participants perceived that their breast cancer risk was the same as the risk of their friends/peers; 10% were pessimistic (risk friends/peers - risk self < 0), whereas 40% were optimistic (risk friends/peers - risk self > 0). Family history of breast cancer and worry informed risk judgments for oneself. The availability and cultural heuristics specific for black women informed risk judgments for friends/peers. Knowledge of risk factors and interactions of knowledge with the availability, representativeness, and simulation heuristics informed comparative judgments (risk friends/peers - risk self). We discuss cognitive mechanisms with which experiences, knowledge, and heuristics influence comparative breast cancer risk judgments. Risk communication interventions should assess knowledge deficits, contextual variables, and specific heuristics that activate differential information processing mechanisms.

Heuristic Reasoning in Chemistry: Making decisions about acid strength

NASA Astrophysics Data System (ADS)

McClary, LaKeisha; Talanquer, Vicente

2011-07-01

The characterization of students' reasoning strategies is of central importance in the development of instructional strategies that foster meaningful learning. In particular, the identification of shortcut reasoning procedures (heuristics) used by students to reduce cognitive load can help us devise strategies to facilitate the development of more analytical ways of thinking. The central goal of this qualitative study was thus to investigate heuristic reasoning as used by organic chemistry college students, focusing our attention on their ability to predict the relative acid strength of chemical compounds represented using explicit composition and structural features (i.e., structural formulas). Our results indicated that many study participants relied heavily on one or more of the following heuristics to make most of their decisions: reduction, representativeness, and lexicographic. Despite having visual access to reach structural information about the substances included in each ranking task, many students relied on isolated composition features to make their decisions. However, the specific characteristics of the tasks seemed to trigger heuristic reasoning in different ways. Although the use of heuristics allowed students to simplify some components of the ranking tasks and generate correct responses, it often led them astray. Very few study participants predicted the correct trends based on scientifically acceptable arguments. Our results suggest the need for instructional interventions that explicitly develop college chemistry students' abilities to monitor their thinking and evaluate the effectiveness of analytical versus heuristic reasoning strategies in different contexts.

Best-First Heuristic Search for Multicore Machines

DTIC Science & Technology

2010-01-01

Otto, 1998) to implement an asynchronous version of PRA* that they call Hash Distributed A* ( HDA *). HDA * distributes nodes using a hash function in...nodes which are being communicated between peers are in transit. In contact with the authors of HDA *, we have created an implementation of HDA * for...Also, our implementation of HDA * allows us to make a fair comparison between algorithms by sharing common data structures such as priority queues and

On the suitability of fast and frugal heuristics for designing values clarification methods in patient decision aids: a critical analysis.

PubMed

Pieterse, Arwen H; de Vries, Marieke

2013-09-01

Increasingly, patient decision aids and values clarification methods (VCMs) are being developed to support patients in making preference-sensitive health-care decisions. Many VCMs encourage extensive deliberation about options, without solid theoretical or empirical evidence showing that deliberation is advantageous. Research suggests that simple, fast and frugal heuristic decision strategies sometimes result in better judgments and decisions. Durand et al. have developed two fast and frugal heuristic-based VCMs. To critically analyse the suitability of the 'take the best' (TTB) and 'tallying' fast and frugal heuristics in the context of patient decision making. Analysis of the structural similarities between the environments in which the TTB and tallying heuristics have been proven successful and the context of patient decision making and of the potential of these heuristic decision processes to support patient decision making. The specific nature of patient preference-sensitive decision making does not seem to resemble environments in which the TTB and tallying heuristics have proven successful. Encouraging patients to consider less rather than more relevant information potentially even deteriorates their values clarification process. Values clarification methods promoting the use of more intuitive decision strategies may sometimes be more effective. Nevertheless, we strongly recommend further theoretical thinking about the expected value of such heuristics and of other more intuitive decision strategies in this context, as well as empirical assessments of the mechanisms by which inducing such decision strategies may impact the quality and outcome of values clarification. © 2011 John Wiley & Sons Ltd.

Systematic Heuristic Evaluation of Computerized Consultation Order Templates: Clinicians' and Human Factors Engineers' Perspectives.

PubMed

Savoy, April; Patel, Himalaya; Flanagan, Mindy E; Weiner, Michael; Russ, Alissa L

2017-08-01

We assessed the usability of consultation order templates and identified problems to prioritize in design efforts for improving referral communication. With a sample of 26 consultation order templates, three evaluators performed a usability heuristic evaluation. The evaluation used 14 domain-independent heuristics and the following three supplemental references: 1 new domain-specific heuristic, 6 usability goals, and coded clinicians' statements regarding ease of use for 10 sampled templates. Evaluators found 201 violations, a mean of 7.7 violations per template. Minor violations outnumbered major violations almost twofold, 115 (57%) to 62 (31%). Approximately 68% of violations were linked to 5 heuristics: aesthetic and minimalist design (17%), error prevention (16%), consistency and standards (14%), recognition rather than recall (11%), and meet referrers' information needs (10%). Severe violations were attributed mostly to meet referrers' information needs and recognition rather than recall. Recorded violations yielded potential negative consequences for efficiency, effectiveness, safety, learnability, and utility. Evaluators and clinicians demonstrated 80% agreement in usability assessment. Based on frequency and severity of usability heuristic violations, the consultation order templates reviewed may impede clinical efficiency and risk patient safety. Results support the following design considerations: communicate consultants' requirements, facilitate information seeking, and support communication. While the most frequent heuristic violations involved interaction design and presentation, the most severe violations lacked information desired by referring clinicians. Violations related to templates' inability to support referring clinicians' information needs had the greatest potential negative impact on efficiency and safety usability goals. Heuristics should be prioritized in future design efforts.

Decompositions of large-scale biological systems based on dynamical properties.

PubMed

Soranzo, Nicola; Ramezani, Fahimeh; Iacono, Giovanni; Altafini, Claudio

2012-01-01

Given a large-scale biological network represented as an influence graph, in this article we investigate possible decompositions of the network aimed at highlighting specific dynamical properties. The first decomposition we study consists in finding a maximal directed acyclic subgraph of the network, which dynamically corresponds to searching for a maximal open-loop subsystem of the given system. Another dynamical property investigated is strong monotonicity. We propose two methods to deal with this property, both aimed at decomposing the system into strongly monotone subsystems, but with different structural characteristics: one method tends to produce a single large strongly monotone component, while the other typically generates a set of smaller disjoint strongly monotone subsystems. Original heuristics for the methods investigated are described in the article. altafini@sissa.it

Take the first heuristic, self-efficacy, and decision-making in sport.

PubMed

Hepler, Teri J; Feltz, Deborah L

2012-06-01

Can taking the first (TTF) option in decision-making lead to the best decisions in sports contexts? And, is one's decision-making self-efficacy in that context linked to TTF decisions? The purpose of this study was to examine the role of the TTF heuristic and self-efficacy in decision-making on a simulated sports task. Undergraduate and graduate students (N = 72) participated in the study and performed 13 trials in each of two video-based basketball decision tasks. One task required participants to verbally generate options before making a final decision on what to do next, while the other task simply asked participants to make a decision regarding the next move as quickly as possible. Decision-making self-efficacy was assessed using a 10-item questionnaire comprising various aspects of decision-making in basketball. Participants also rated their confidence in the final decision. Results supported many of the tenets of the TTF heuristic, such that people used the heuristic on a majority of the trials (70%), earlier generated options were better than later ones, first options were meaningfully generated, and final options were meaningfully selected. Results did not support differences in dynamic inconsistency or decision confidence based on the number of options. Findings also supported the link between self-efficacy and the TTF heuristic. Participants with higher self-efficacy beliefs used TTF more frequently and generated fewer options than those with low self-efficacy. Thus, not only is TTF an important heuristic when making decisions in dynamic, time-pressure situations, but self-efficacy plays an influential role in TTF.

Electricity Usage Scheduling in Smart Building Environments Using Smart Devices

PubMed Central

Lee, Eunji; Bahn, Hyokyung

2013-01-01

With the recent advances in smart grid technologies as well as the increasing dissemination of smart meters, the electricity usage of every moment can be detected in modern smart building environments. Thus, the utility company adopts different price of electricity at each time slot considering the peak time. This paper presents a new electricity usage scheduling algorithm for smart buildings that adopts real-time pricing of electricity. The proposed algorithm detects the change of electricity prices by making use of a smart device and changes the power mode of each electric device dynamically. Specifically, we formulate the electricity usage scheduling problem as a real-time task scheduling problem and show that it is a complex search problem that has an exponential time complexity. An efficient heuristic based on genetic algorithms is performed on a smart device to cut down the huge searching space and find a reasonable schedule within a feasible time budget. Experimental results with various building conditions show that the proposed algorithm reduces the electricity charge of a smart building by 25.6% on average and up to 33.4%. PMID:24453860

Scientific discovery as a combinatorial optimisation problem: How best to navigate the landscape of possible experiments?

PubMed Central

Kell, Douglas B

2012-01-01

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a ‘landscape’ representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems ‘hard’, but as such these are to be seen as combinatorial optimisation problems that are best attacked by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the ‘best’ experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes. PMID:22252984

Optical Symbolic Computing

NASA Astrophysics Data System (ADS)

Neff, John A.

1989-12-01

Experiments originating from Gestalt psychology have shown that representing information in a symbolic form provides a more effective means to understanding. Computer scientists have been struggling for the last two decades to determine how best to create, manipulate, and store collections of symbolic structures. In the past, much of this struggling led to software innovations because that was the path of least resistance. For example, the development of heuristics for organizing the searching through knowledge bases was much less expensive than building massively parallel machines that could search in parallel. That is now beginning to change with the emergence of parallel architectures which are showing the potential for handling symbolic structures. This paper will review the relationships between symbolic computing and parallel computing architectures, and will identify opportunities for optics to significantly impact the performance of such computing machines. Although neural networks are an exciting subset of massively parallel computing structures, this paper will not touch on this area since it is receiving a great deal of attention in the literature. That is, the concepts presented herein do not consider the distributed representation of knowledge.

Electricity usage scheduling in smart building environments using smart devices.

PubMed

Lee, Eunji; Bahn, Hyokyung

2013-01-01

With the recent advances in smart grid technologies as well as the increasing dissemination of smart meters, the electricity usage of every moment can be detected in modern smart building environments. Thus, the utility company adopts different price of electricity at each time slot considering the peak time. This paper presents a new electricity usage scheduling algorithm for smart buildings that adopts real-time pricing of electricity. The proposed algorithm detects the change of electricity prices by making use of a smart device and changes the power mode of each electric device dynamically. Specifically, we formulate the electricity usage scheduling problem as a real-time task scheduling problem and show that it is a complex search problem that has an exponential time complexity. An efficient heuristic based on genetic algorithms is performed on a smart device to cut down the huge searching space and find a reasonable schedule within a feasible time budget. Experimental results with various building conditions show that the proposed algorithm reduces the electricity charge of a smart building by 25.6% on average and up to 33.4%.

Multiple-reason decision making based on automatic processing.

PubMed

Glöckner, Andreas; Betsch, Tilmann

2008-09-01

It has been repeatedly shown that in decisions under time constraints, individuals predominantly use noncompensatory strategies rather than complex compensatory ones. The authors argue that these findings might be due not to limitations of cognitive capacity but instead to limitations of information search imposed by the commonly used experimental tool Mouselab (J. W. Payne, J. R. Bettman, & E. J. Johnson, 1988). The authors tested this assumption in 3 experiments. In the 1st experiment, information was openly presented, whereas in the 2nd experiment, the standard Mouselab program was used under different time limits. The results indicate that individuals are able to compute weighted additive decision strategies extremely quickly if information search is not restricted by the experimental procedure. In a 3rd experiment, these results were replicated using more complex decision tasks, and the major alternative explanations that individuals use more complex heuristics or that they merely encode the constellation of cues were ruled out. In sum, the findings challenge the fundaments of bounded rationality and highlight the importance of automatic processes in decision making. (c) 2008 APA, all rights reserved.

Scientific discovery as a combinatorial optimisation problem: how best to navigate the landscape of possible experiments?

PubMed

Kell, Douglas B

2012-03-01

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a 'landscape' representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems 'hard', but as such these are to be seen as combinatorial optimisation problems that are best attacked by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the 'best' experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes. Copyright © 2012 WILEY Periodicals, Inc.

An investigation of messy genetic algorithms

NASA Technical Reports Server (NTRS)

Goldberg, David E.; Deb, Kalyanmoy; Korb, Bradley

1990-01-01

Genetic algorithms (GAs) are search procedures based on the mechanics of natural selection and natural genetics. They combine the use of string codings or artificial chromosomes and populations with the selective and juxtapositional power of reproduction and recombination to motivate a surprisingly powerful search heuristic in many problems. Despite their empirical success, there has been a long standing objection to the use of GAs in arbitrarily difficult problems. A new approach was launched. Results to a 30-bit, order-three-deception problem were obtained using a new type of genetic algorithm called a messy genetic algorithm (mGAs). Messy genetic algorithms combine the use of variable-length strings, a two-phase selection scheme, and messy genetic operators to effect a solution to the fixed-coding problem of standard simple GAs. The results of the study of mGAs in problems with nonuniform subfunction scale and size are presented. The mGA approach is summarized, both its operation and the theory of its use. Experiments on problems of varying scale, varying building-block size, and combined varying scale and size are presented.

Automatic creation of object hierarchies for ray tracing

NASA Technical Reports Server (NTRS)

Goldsmith, Jeffrey; Salmon, John

1987-01-01

Various methods for evaluating generated trees are proposed. The use of the hierarchical extent method of Rubin and Whitted (1980) to find the objects that will be hit by a ray is examined. This method employs tree searching; the construction of a tree of bounding volumes in order to determine the number of objects that will be hit by a ray is discussed. A tree generation algorithm, which uses a heuristic tree search strategy, is described. The effects of shuffling and sorting on the input data are investigated. The cost of inserting an object into the hierarchy during the construction of a tree algorithm is estimated. The steps involved in estimating the number of intersection calculations are presented.

Common-sense chemistry: The use of assumptions and heuristics in problem solving

NASA Astrophysics Data System (ADS)

Maeyer, Jenine Rachel

Students experience difficulty learning and understanding chemistry at higher levels, often because of cognitive biases stemming from common sense reasoning constraints. These constraints can be divided into two categories: assumptions (beliefs held about the world around us) and heuristics (the reasoning strategies or rules used to build predictions and make decisions). A better understanding and characterization of these constraints are of central importance in the development of curriculum and teaching strategies that better support student learning in science. It was the overall goal of this thesis to investigate student reasoning in chemistry, specifically to better understand and characterize the assumptions and heuristics used by undergraduate chemistry students. To achieve this, two mixed-methods studies were conducted, each with quantitative data collected using a questionnaire and qualitative data gathered through semi-structured interviews. The first project investigated the reasoning heuristics used when ranking chemical substances based on the relative value of a physical or chemical property, while the second study characterized the assumptions and heuristics used when making predictions about the relative likelihood of different types of chemical processes. Our results revealed that heuristics for cue selection and decision-making played a significant role in the construction of answers during the interviews. Many study participants relied frequently on one or more of the following heuristics to make their decisions: recognition, representativeness, one-reason decision-making, and arbitrary trend. These heuristics allowed students to generate answers in the absence of requisite knowledge, but often led students astray. When characterizing assumptions, our results indicate that students relied on intuitive, spurious, and valid assumptions about the nature of chemical substances and processes in building their responses. In particular, many interviewees seemed to view chemical reactions as macroscopic reassembling processes where favorability was related to the perceived ease with which reactants broke apart or products formed. Students also expressed spurious chemical assumptions based on the misinterpretation and overgeneralization of periodicity and electronegativity. Our findings suggest the need to create more opportunities for college chemistry students to monitor their thinking, develop and apply analytical ways of reasoning, and evaluate the effectiveness of shortcut reasoning procedures in different contexts.

Introducing TreeCollapse: a novel greedy algorithm to solve the cophylogeny reconstruction problem.

PubMed

Drinkwater, Benjamin; Charleston, Michael A

2014-01-01

Cophylogeny mapping is used to uncover deep coevolutionary associations between two or more phylogenetic histories at a macro coevolutionary scale. As cophylogeny mapping is NP-Hard, this technique relies heavily on heuristics to solve all but the most trivial cases. One notable approach utilises a metaheuristic to search only a subset of the exponential number of fixed node orderings possible for the phylogenetic histories in question. This is of particular interest as it is the only known heuristic that guarantees biologically feasible solutions. This has enabled research to focus on larger coevolutionary systems, such as coevolutionary associations between figs and their pollinator wasps, including over 200 taxa. Although able to converge on solutions for problem instances of this size, a reduction from the current cubic running time is required to handle larger systems, such as Wolbachia and their insect hosts. Rather than solving this underlying problem optimally this work presents a greedy algorithm called TreeCollapse, which uses common topological patterns to recover an approximation of the coevolutionary history where the internal node ordering is fixed. This approach offers a significant speed-up compared to previous methods, running in linear time. This algorithm has been applied to over 100 well-known coevolutionary systems converging on Pareto optimal solutions in over 68% of test cases, even where in some cases the Pareto optimal solution has not previously been recoverable. Further, while TreeCollapse applies a local search technique, it can guarantee solutions are biologically feasible, making this the fastest method that can provide such a guarantee. As a result, we argue that the newly proposed algorithm is a valuable addition to the field of coevolutionary research. Not only does it offer a significantly faster method to estimate the cost of cophylogeny mappings but by using this approach, in conjunction with existing heuristics, it can assist in recovering a larger subset of the Pareto front than has previously been possible.

SAM: String-based sequence search algorithm for mitochondrial DNA database queries

PubMed Central

Röck, Alexander; Irwin, Jodi; Dür, Arne; Parsons, Thomas; Parson, Walther

2011-01-01

The analysis of the haploid mitochondrial (mt) genome has numerous applications in forensic and population genetics, as well as in disease studies. Although mtDNA haplotypes are usually determined by sequencing, they are rarely reported as a nucleotide string. Traditionally they are presented in a difference-coded position-based format relative to the corrected version of the first sequenced mtDNA. This convention requires recommendations for standardized sequence alignment that is known to vary between scientific disciplines, even between laboratories. As a consequence, database searches that are vital for the interpretation of mtDNA data can suffer from biased results when query and database haplotypes are annotated differently. In the forensic context that would usually lead to underestimation of the absolute and relative frequencies. To address this issue we introduce SAM, a string-based search algorithm that converts query and database sequences to position-free nucleotide strings and thus eliminates the possibility that identical sequences will be missed in a database query. The mere application of a BLAST algorithm would not be a sufficient remedy as it uses a heuristic approach and does not address properties specific to mtDNA, such as phylogenetically stable but also rapidly evolving insertion and deletion events. The software presented here provides additional flexibility to incorporate phylogenetic data, site-specific mutation rates, and other biologically relevant information that would refine the interpretation of mitochondrial DNA data. The manuscript is accompanied by freeware and example data sets that can be used to evaluate the new software (http://stringvalidation.org). PMID:21056022

CMOS analogue amplifier circuits optimisation using hybrid backtracking search algorithm with differential evolution

NASA Astrophysics Data System (ADS)

Mallick, S.; Kar, R.; Mandal, D.; Ghoshal, S. P.

2016-07-01

This paper proposes a novel hybrid optimisation algorithm which combines the recently proposed evolutionary algorithm Backtracking Search Algorithm (BSA) with another widely accepted evolutionary algorithm, namely, Differential Evolution (DE). The proposed algorithm called BSA-DE is employed for the optimal designs of two commonly used analogue circuits, namely Complementary Metal Oxide Semiconductor (CMOS) differential amplifier circuit with current mirror load and CMOS two-stage operational amplifier (op-amp) circuit. BSA has a simple structure that is effective, fast and capable of solving multimodal problems. DE is a stochastic, population-based heuristic approach, having the capability to solve global optimisation problems. In this paper, the transistors' sizes are optimised using the proposed BSA-DE to minimise the areas occupied by the circuits and to improve the performances of the circuits. The simulation results justify the superiority of BSA-DE in global convergence properties and fine tuning ability, and prove it to be a promising candidate for the optimal design of the analogue CMOS amplifier circuits. The simulation results obtained for both the amplifier circuits prove the effectiveness of the proposed BSA-DE-based approach over DE, harmony search (HS), artificial bee colony (ABC) and PSO in terms of convergence speed, design specifications and design parameters of the optimal design of the analogue CMOS amplifier circuits. It is shown that BSA-DE-based design technique for each amplifier circuit yields the least MOS transistor area, and each designed circuit is shown to have the best performance parameters such as gain, power dissipation, etc., as compared with those of other recently reported literature.

Quantum annealing for combinatorial clustering

NASA Astrophysics Data System (ADS)

Kumar, Vaibhaw; Bass, Gideon; Tomlin, Casey; Dulny, Joseph

2018-02-01

Clustering is a powerful machine learning technique that groups "similar" data points based on their characteristics. Many clustering algorithms work by approximating the minimization of an objective function, namely the sum of within-the-cluster distances between points. The straightforward approach involves examining all the possible assignments of points to each of the clusters. This approach guarantees the solution will be a global minimum; however, the number of possible assignments scales quickly with the number of data points and becomes computationally intractable even for very small datasets. In order to circumvent this issue, cost function minima are found using popular local search-based heuristic approaches such as k-means and hierarchical clustering. Due to their greedy nature, such techniques do not guarantee that a global minimum will be found and can lead to sub-optimal clustering assignments. Other classes of global search-based techniques, such as simulated annealing, tabu search, and genetic algorithms, may offer better quality results but can be too time-consuming to implement. In this work, we describe how quantum annealing can be used to carry out clustering. We map the clustering objective to a quadratic binary optimization problem and discuss two clustering algorithms which are then implemented on commercially available quantum annealing hardware, as well as on a purely classical solver "qbsolv." The first algorithm assigns N data points to K clusters, and the second one can be used to perform binary clustering in a hierarchical manner. We present our results in the form of benchmarks against well-known k-means clustering and discuss the advantages and disadvantages of the proposed techniques.

Reducing a Knowledge-Base Search Space When Data Are Missing

NASA Technical Reports Server (NTRS)

James, Mark

2007-01-01

This software addresses the problem of how to efficiently execute a knowledge base in the presence of missing data. Computationally, this is an exponentially expensive operation that without heuristics generates a search space of 1 + 2n possible scenarios, where n is the number of rules in the knowledge base. Even for a knowledge base of the most modest size, say 16 rules, it would produce 65,537 possible scenarios. The purpose of this software is to reduce the complexity of this operation to a more manageable size. The problem that this system solves is to develop an automated approach that can reason in the presence of missing data. This is a meta-reasoning capability that repeatedly calls a diagnostic engine/model to provide prognoses and prognosis tracking. In the big picture, the scenario generator takes as its input the current state of a system, including probabilistic information from Data Forecasting. Using model-based reasoning techniques, it returns an ordered list of fault scenarios that could be generated from the current state, i.e., the plausible future failure modes of the system as it presently stands. The scenario generator models a Potential Fault Scenario (PFS) as a black box, the input of which is a set of states tagged with priorities and the output of which is one or more potential fault scenarios tagged by a confidence factor. The results from the system are used by a model-based diagnostician to predict the future health of the monitored system.

Gaussian mixed model in support of semiglobal matching leveraged by ground control points

NASA Astrophysics Data System (ADS)

Ma, Hao; Zheng, Shunyi; Li, Chang; Li, Yingsong; Gui, Li

2017-04-01

Semiglobal matching (SGM) has been widely applied in large aerial images because of its good tradeoff between complexity and robustness. The concept of ground control points (GCPs) is adopted to make SGM more robust. We model the effect of GCPs as two data terms for stereo matching between high-resolution aerial epipolar images in an iterative scheme. One term based on GCPs is formulated by Gaussian mixture model, which strengths the relation between GCPs and the pixels to be estimated and encodes some degree of consistency between them with respect to disparity values. Another term depends on pixel-wise confidence, and we further design a confidence updating equation based on three rules. With this confidence-based term, the assignment of disparity can be heuristically selected among disparity search ranges during the iteration process. Several iterations are sufficient to bring out satisfactory results according to our experiments. Experimental results validate that the proposed method outperforms surface reconstruction, which is a representative variant of SGM and behaves excellently on aerial images.

Augmented design and analysis of computer experiments: a novel tolerance embedded global optimization approach applied to SWIR hyperspectral illumination design.

PubMed

Keresztes, Janos C; John Koshel, R; D'huys, Karlien; De Ketelaere, Bart; Audenaert, Jan; Goos, Peter; Saeys, Wouter

2016-12-26

A novel meta-heuristic approach for minimizing nonlinear constrained problems is proposed, which offers tolerance information during the search for the global optimum. The method is based on the concept of design and analysis of computer experiments combined with a novel two phase design augmentation (DACEDA), which models the entire merit space using a Gaussian process, with iteratively increased resolution around the optimum. The algorithm is introduced through a series of cases studies with increasing complexity for optimizing uniformity of a short-wave infrared (SWIR) hyperspectral imaging (HSI) illumination system (IS). The method is first demonstrated for a two-dimensional problem consisting of the positioning of analytical isotropic point sources. The method is further applied to two-dimensional (2D) and five-dimensional (5D) SWIR HSI IS versions using close- and far-field measured source models applied within the non-sequential ray-tracing software FRED, including inherent stochastic noise. The proposed method is compared to other heuristic approaches such as simplex and simulated annealing (SA). It is shown that DACEDA converges towards a minimum with 1 % improvement compared to simplex and SA, and more importantly requiring only half the number of simulations. Finally, a concurrent tolerance analysis is done within DACEDA for to the five-dimensional case such that further simulations are not required.

Example-Based Learning in Heuristic Domains: A Cognitive Load Theory Account

ERIC Educational Resources Information Center

Renkl, Alexander; Hilbert, Tatjana; Schworm, Silke

2009-01-01

One classical instructional effect of cognitive load theory (CLT) is the worked-example effect. Although the vast majority of studies have focused on well-structured and algorithmic sub-domains of mathematics or physics, more recent studies have also analyzed learning with examples from complex domains in which only heuristic solution strategies…

The Heuristic Sandbox: Developing Teacher Know-How through Play in simSchool

ERIC Educational Resources Information Center

Hopper, Susan B.

2018-01-01

simSchool is a game-based, virtual, and interactive tool that allows pre-service teachers to acquire new skills while constructing knowledge through experimentation with learning situations. Pre-service teachers develop know-how--or heuristic knowledge--through repeated practice in the "Personality Plus Higher-Order Thinking" module to…

Balancing Self-Directed Learning with Expert Mentoring: The Science Writing Heuristic Approach

ERIC Educational Resources Information Center

Shelley, Mack; Fostvedt, Luke; Gonwa-Reeves, Christopher; Baenziger, Joan; McGill, Michael; Seefeld, Ashley; Hand, Brian; Therrien, William; Taylor, Jonte; Villanueva, Mary Grace

2012-01-01

This study focuses on the implementation of the Science Writing Heuristic (SWH) curriculum (Hand, 2007), which combines current understandings of learning as a cognitive and negotiated process with the techniques of argument-based inquiry, critical thinking skills, and writing to strengthen student outcomes. Success of SWH is dependent on the…

One-Reason Decision Making Unveiled: A Measurement Model of the Recognition Heuristic

ERIC Educational Resources Information Center

Hilbig, Benjamin E.; Erdfelder, Edgar; Pohl, Rudiger F.

2010-01-01

The fast-and-frugal recognition heuristic (RH) theory provides a precise process description of comparative judgments. It claims that, in suitable domains, judgments between pairs of objects are based on recognition alone, whereas further knowledge is ignored. However, due to the confound between recognition and further knowledge, previous…

Web-Based Family Life Education: Spotlight on User Experience

ERIC Educational Resources Information Center

Doty, Jennifer; Doty, Matthew; Dwrokin, Jodi

2011-01-01

Family Life Education (FLE) websites can benefit from the field of user experience, which makes technology easy to use. A heuristic evaluation of five FLE sites was performed using Neilson's heuristics, guidelines for making sites user friendly. Greater site complexity resulted in more potential user problems. Sites most frequently had problems…

Clinical evaluation of the effects of signal integrity and saturation on data availability and accuracy of Masimo SE and Nellcor N-395 oximeters in children.

PubMed

Robertson, Frederick A; Hoffman, George M

2004-03-01

Pulse oximetry manufacturers have introduced technologies that claim improved detection of hypoxemic events. Because improvements in signal processing and data rejection algorithms may differentially affect data reporting, we compared the data reporting and signal heuristic performance and agreement among the Nellcor N-395, Masimo SET, and GE Solar 8000 oximeters under a spectrum of conditions of signal integrity and arterial oxygen saturations. A blinded side-by-side comparison of technologies was performed in 27 patients, and data were analyzed for time of data availability, measures of agreement and signal heuristics, and warnings stratified by signal integrity and SpO(2). The Solar 8000 had less total data dropout than either of the new technologies. Masimo's LoSIQ (signal quality) heuristic rejected less data than Nellcor's MOT/PS (motion/pulse search) flag. When no signal heuristic was displayed, there was little difference in precision and bias between the two newer technologies; however, agreement between devices deteriorated in the presence of SIQ, MOT, or hypoxemia. Both newer devices flagged questionable data, but their use of different rejection algorithms resulted in different probabilities of presenting data. Therefore, with poor SIQ or during hypoxemia, the Nellcor N-395 and Masimo oximeters are not clinically equivalent to each other or to the older Solar 8000 oximeter. We compared new pulse oximeters from Nellcor and Masimo and found that, with good signal conditions, both new devices performed similarly to older technology. Overall, Masimo reported less data as questionable than Nellcor. With poor signal conditions or during hypoxemia, the new devices are not clinically equivalent to each other or to the older technology.

Automated unit-level testing with heuristic rules

NASA Technical Reports Server (NTRS)

Carlisle, W. Homer; Chang, Kai-Hsiung; Cross, James H.; Keleher, William; Shackelford, Keith

1990-01-01

Software testing plays a significant role in the development of complex software systems. Current testing methods generally require significant effort to generate meaningful test cases. The QUEST/Ada system is a prototype system designed using CLIPS to experiment with expert system based test case generation. The prototype is designed to test for condition coverage, and attempts to generate test cases to cover all feasible branches contained in an Ada program. This paper reports on heuristics sued by the system. These heuristics vary according to the amount of knowledge obtained by preprocessing and execution of the boolean conditions in the program.

Fast content-based image retrieval using dynamic cluster tree

NASA Astrophysics Data System (ADS)

Chen, Jinyan; Sun, Jizhou; Wu, Rongteng; Zhang, Yaping

2008-03-01

A novel content-based image retrieval data structure is developed in present work. It can improve the searching efficiency significantly. All images are organized into a tree, in which every node is comprised of images with similar features. Images in a children node have more similarity (less variance) within themselves in relative to its parent. It means that every node is a cluster and each of its children nodes is a sub-cluster. Information contained in a node includes not only the number of images, but also the center and the variance of these images. Upon the addition of new images, the tree structure is capable of dynamically changing to ensure the minimization of total variance of the tree. Subsequently, a heuristic method has been designed to retrieve the information from this tree. Given a sample image, the probability of a tree node that contains the similar images is computed using the center of the node and its variance. If the probability is higher than a certain threshold, this node will be recursively checked to locate the similar images. So will its children nodes if their probability is also higher than that threshold. If no sufficient similar images were founded, a reduced threshold value would be adopted to initiate a new seeking from the root node. The search terminates when it found sufficient similar images or the threshold value is too low to give meaningful sense. Experiments have shown that the proposed dynamic cluster tree is able to improve the searching efficiency notably.

Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling.

PubMed

Yang, S; Wang, D

2000-01-01

This paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed neural network can be easily constructed and can adaptively adjust its weights of connections and biases of units based on the sequence and resource constraints of the job-shop scheduling problem during its processing. Several heuristics that can be combined with the neural network are also presented. In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve the performance of the neural network and the quality of the obtained solutions. Simulations have shown that the proposed neural network and its combined approaches are efficient with respect to the quality of solutions and the solving speed.

Design and usability of heuristic-based deliberation tools for women facing amniocentesis.

PubMed

Durand, Marie-Anne; Wegwarth, Odette; Boivin, Jacky; Elwyn, Glyn

2012-03-01

Evidence suggests that in decision contexts characterized by uncertainty and time constraints (e.g. health-care decisions), fast and frugal decision-making strategies (heuristics) may perform better than complex rules of reasoning. To examine whether it is possible to design deliberation components in decision support interventions using simple models (fast and frugal heuristics). The 'Take The Best' heuristic (i.e. selection of a 'most important reason') and 'The Tallying' integration algorithm (i.e. unitary weighing of pros and cons) were used to develop two deliberation components embedded in a Web-based decision support intervention for women facing amniocentesis testing. Ten researchers (recruited from 15), nine health-care providers (recruited from 28) and ten pregnant women (recruited from 14) who had recently been offered amniocentesis testing appraised evolving versions of 'your most important reason' (Take The Best) and 'weighing it up' (Tallying). Most researchers found the tools useful in facilitating decision making although emphasized the need for simple instructions and clear layouts. Health-care providers however expressed concerns regarding the usability and clarity of the tools. By contrast, 7 out of 10 pregnant women found the tools useful in weighing up the pros and cons of each option, helpful in structuring and clarifying their thoughts and visualizing their decision efforts. Several pregnant women felt that 'weighing it up' and 'your most important reason' were not appropriate when facing such a difficult and emotional decision. Theoretical approaches based on fast and frugal heuristics can be used to develop deliberation tools that provide helpful support to patients facing real-world decisions about amniocentesis. © 2011 Blackwell Publishing Ltd.

A multi-stage heuristic algorithm for matching problem in the modified miniload automated storage and retrieval system of e-commerce

NASA Astrophysics Data System (ADS)

Wang, Wenrui; Wu, Yaohua; Wu, Yingying

2016-05-01

E-commerce, as an emerging marketing mode, has attracted more and more attention and gradually changed the way of our life. However, the existing layout of distribution centers can't fulfill the storage and picking demands of e-commerce sufficiently. In this paper, a modified miniload automated storage/retrieval system is designed to fit these new characteristics of e-commerce in logistics. Meanwhile, a matching problem, concerning with the improvement of picking efficiency in new system, is studied in this paper. The problem is how to reduce the travelling distance of totes between aisles and picking stations. A multi-stage heuristic algorithm is proposed based on statement and model of this problem. The main idea of this algorithm is, with some heuristic strategies based on similarity coefficients, minimizing the transportations of items which can not arrive in the destination picking stations just through direct conveyors. The experimental results based on the cases generated by computers show that the average reduced rate of indirect transport times can reach 14.36% with the application of multi-stage heuristic algorithm. For the cases from a real e-commerce distribution center, the order processing time can be reduced from 11.20 h to 10.06 h with the help of the modified system and the proposed algorithm. In summary, this research proposed a modified system and a multi-stage heuristic algorithm that can reduce the travelling distance of totes effectively and improve the whole performance of e-commerce distribution center.

Strategy selection in cue-based decision making.

PubMed

Bryant, David J

2014-06-01

People can make use of a range of heuristic and rational, compensatory strategies to perform a multiple-cue judgment task. It has been proposed that people are sensitive to the amount of cognitive effort required to employ decision strategies. Experiment 1 employed a dual-task methodology to investigate whether participants' preference for heuristic versus compensatory decision strategies can be altered by increasing the cognitive demands of the task. As indicated by participants' decision times, a secondary task interfered more with the performance of a heuristic than compensatory decision strategy but did not affect the proportions of participants using either type of strategy. A stimulus set effect suggested that the conjunction of cue salience and cue validity might play a determining role in strategy selection. The results of Experiment 2 indicated that when a perceptually salient cue was also the most valid, the majority of participants preferred a single-cue heuristic strategy. Overall, the results contradict the view that heuristics are more likely to be adopted when a task is made more cognitively demanding. It is argued that people employ 2 learning processes during training, one an associative learning process in which cue-outcome associations are developed by sampling multiple cues, and another that involves the sequential examination of single cues to serve as a basis for a single-cue heuristic.

The fallacy of financial heuristics.

PubMed

Langabeer, James

2007-01-01

In turbulent times, the financial policies and decisions about cash and debt make or break hospitals' financial condition. Decisions about whether to continue saving cash or reduce debt burdens are probably the most vital policy decision for the hospital CFO. Unfortunately, my research shows that most administrators are relying on judgment, or best-guess heuristics to address these policy issues. This article explores one of the most common heuristics in health finance-ratios gauging debt and cash on hand. The subject is explored through the research and analysis of over 40 hospitals in a very competitive marketplace-the boroughs of New York City. Analyses of financial strength, through various statistical models, were conducted to explore the linkages between traditional heuristics and long-term economic results. Data were collected for 30 operational and financial indicators. Findings suggest that organizations require different cash-debt positions based on their overall financial health, and that a one-number heuristic does not fit all. Extremely financially constrained hospitals (those approaching bankruptcy conditions) should be building free cash flow and minimizing debt service, while financially secure hospitals need to minimize cash on hand while reducing debt. If all hospitals continue to try to meet an arbitrary days of cash heuristic, this simplification could cripple an organization. A much more effective metric requires each organization to model decisions more comprehensively.

Strategies for de-identification and anonymization of electronic health record data for use in multicenter research studies.

PubMed

Kushida, Clete A; Nichols, Deborah A; Jadrnicek, Rik; Miller, Ric; Walsh, James K; Griffin, Kara

2012-07-01

De-identification and anonymization are strategies that are used to remove patient identifiers in electronic health record data. The use of these strategies in multicenter research studies is paramount in importance, given the need to share electronic health record data across multiple environments and institutions while safeguarding patient privacy. Systematic literature search using keywords of de-identify, deidentify, de-identification, deidentification, anonymize, anonymization, data scrubbing, and text scrubbing. Search was conducted up to June 30, 2011 and involved 6 different common literature databases. A total of 1798 prospective citations were identified, and 94 full-text articles met the criteria for review and the corresponding articles were obtained. Search results were supplemented by review of 26 additional full-text articles; a total of 120 full-text articles were reviewed. A final sample of 45 articles met inclusion criteria for review and discussion. Articles were grouped into text, images, and biological sample categories. For text-based strategies, the approaches were segregated into heuristic, lexical, and pattern-based systems versus statistical learning-based systems. For images, approaches that de-identified photographic facial images and magnetic resonance image data were described. For biological samples, approaches that managed the identifiers linked with these samples were discussed, particularly with respect to meeting the anonymization requirements needed for Institutional Review Board exemption under the Common Rule. Current de-identification strategies have their limitations, and statistical learning-based systems have distinct advantages over other approaches for the de-identification of free text. True anonymization is challenging, and further work is needed in the areas of de-identification of datasets and protection of genetic information.

Smart strategies for doctors and doctors-in-training: heuristics in medicine.

PubMed

Wegwarth, Odette; Gaissmaier, Wolfgang; Gigerenzer, Gerd

2009-08-01

How do doctors make sound decisions when confronted with probabilistic data, time pressures and a heavy workload? One theory that has been embraced by many researchers is based on optimisation, which emphasises the need to integrate all information in order to arrive at sound decisions. This notion makes heuristics, which use less than complete information, appear as second-best strategies. In this article, we challenge this pessimistic view of heuristics. We introduce two medical problems that involve decision making to the reader: one concerns coronary care issues and the other macrolide prescriptions. In both settings, decision-making tools grounded in the principles of optimisation and heuristics, respectively, have been developed to assist doctors in making decisions. We explain the structure of each of these tools and compare their performance in terms of their facilitation of correct predictions. For decisions concerning both the coronary care unit and the prescribing of macrolides, we demonstrate that sacrificing information does not necessarily imply a forfeiting of predictive accuracy, but can sometimes even lead to better decisions. Subsequently, we discuss common misconceptions about heuristics and explain when and why ignoring parts of the available information can lead to the making of more robust predictions. Heuristics are neither good nor bad per se, but, if applied in situations to which they have been adapted, can be helpful companions for doctors and doctors-in-training. This, however, requires that heuristics in medicine be openly discussed, criticised, refined and then taught to doctors-in-training rather than being simply dismissed as harmful or irrelevant. A more uniform use of explicit and accepted heuristics has the potential to reduce variations in diagnoses and to improve medical care for patients.

User interface issues in supporting human-computer integrated scheduling

NASA Technical Reports Server (NTRS)

Cooper, Lynne P.; Biefeld, Eric W.

1991-01-01

Explored here is the user interface problems encountered with the Operations Missions Planner (OMP) project at the Jet Propulsion Laboratory (JPL). OMP uses a unique iterative approach to planning that places additional requirements on the user interface, particularly to support system development and maintenance. These requirements are necessary to support the concepts of heuristically controlled search, in-progress assessment, and iterative refinement of the schedule. The techniques used to address the OMP interface needs are given.

Application of artificial intelligence to search ground-state geometry of clusters

NASA Astrophysics Data System (ADS)

Lemes, Maurício Ruv; Marim, L. R.; dal Pino, A.

2002-08-01

We introduce a global optimization procedure, the neural-assisted genetic algorithm (NAGA). It combines the power of an artificial neural network (ANN) with the versatility of the genetic algorithm. This method is suitable to solve optimization problems that depend on some kind of heuristics to limit the search space. If a reasonable amount of data is available, the ANN can ``understand'' the problem and provide the genetic algorithm with a selected population of elements that will speed up the search for the optimum solution. We tested the method in a search for the ground-state geometry of silicon clusters. We trained the ANN with information about the geometry and energetics of small silicon clusters. Next, the ANN learned how to restrict the configurational space for larger silicon clusters. For Si10 and Si20, we noticed that the NAGA is at least three times faster than the ``pure'' genetic algorithm. As the size of the cluster increases, it is expected that the gain in terms of time will increase as well.

Approach to design neural cryptography: a generalized architecture and a heuristic rule.

PubMed

Mu, Nankun; Liao, Xiaofeng; Huang, Tingwen

2013-06-01

Neural cryptography, a type of public key exchange protocol, is widely considered as an effective method for sharing a common secret key between two neural networks on public channels. How to design neural cryptography remains a great challenge. In this paper, in order to provide an approach to solve this challenge, a generalized network architecture and a significant heuristic rule are designed. The proposed generic framework is named as tree state classification machine (TSCM), which extends and unifies the existing structures, i.e., tree parity machine (TPM) and tree committee machine (TCM). Furthermore, we carefully study and find that the heuristic rule can improve the security of TSCM-based neural cryptography. Therefore, TSCM and the heuristic rule can guide us to designing a great deal of effective neural cryptography candidates, in which it is possible to achieve the more secure instances. Significantly, in the light of TSCM and the heuristic rule, we further expound that our designed neural cryptography outperforms TPM (the most secure model at present) on security. Finally, a series of numerical simulation experiments are provided to verify validity and applicability of our results.

On the psychology of the recognition heuristic: retrieval primacy as a key determinant of its use.

PubMed

Pachur, Thorsten; Hertwig, Ralph

2006-09-01

The recognition heuristic is a prime example of a boundedly rational mind tool that rests on an evolved capacity, recognition, and exploits environmental structures. When originally proposed, it was conjectured that no other probabilistic cue reverses the recognition-based inference (D. G. Goldstein & G. Gigerenzer, 2002). More recent studies challenged this view and gave rise to the argument that recognition enters inferences just like any other probabilistic cue. By linking research on the heuristic with research on recognition memory, the authors argue that the retrieval of recognition information is not tantamount to the retrieval of other probabilistic cues. Specifically, the retrieval of subjective recognition precedes that of an objective probabilistic cue and occurs at little to no cognitive cost. This retrieval primacy gives rise to 2 predictions, both of which have been empirically supported: Inferences in line with the recognition heuristic (a) are made faster than inferences inconsistent with it and (b) are more prevalent under time pressure. Suspension of the heuristic, in contrast, requires additional time, and direct knowledge of the criterion variable, if available, can trigger such suspension. Copyright 2006 APA

Perspectives of young Chinese Singaporean women on seeking and processing information to decide about vaccinating against human papillomavirus.

PubMed

Basnyat, Iccha; Lim, Cheryl

2017-07-06

Human papillomavirus (HPV) vaccination uptake in Singapore is low among young women. Low uptake has been found to be linked to low awareness. Thus, this study aimed to understand active and passive vaccine information-seeking behavior. Furthermore, guided by the Elaboration Likelihood Model (ELM), this study examined young women's (aged 21-26 years) processing of information they acquired in their decision to get vaccinated. ELM postulates that information processing could be through the central (i.e., logic-based) or peripheral (i.e., heuristic-based) route. Twenty-six in-depth interviews were conducted from January to March 2016. Data were analyzed using thematic analysis. Two meta-themes-information acquisition and vaccination decision-revealed the heuristic-based information processing was employed. These young women acquired information passively within their social network and actively in healthcare settings. However, they used heuristic cues, such as closeness and trust, to process the information. Similarly, vaccination decisions revealed that women relied on heuristic cues, such as sense of belonging and validation among peers and source credibility and likability in medical settings, in their decision to get vaccinated. The findings of this study highlight that intervention efforts should focus on strengthening social support among personal networks to increase the uptake of the vaccine.

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets

PubMed Central

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details. PMID:26158662

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets.

PubMed

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.

Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm

NASA Astrophysics Data System (ADS)

Piroozfard, Hamed; Wong, Kuan Yew

2015-05-01

The efforts of finding optimal schedules for the job shop scheduling problems are highly important for many real-world industrial applications. In this paper, a multi-objective based job shop scheduling problem by simultaneously minimizing makespan and tardiness is taken into account. The problem is considered to be more complex due to the multiple business criteria that must be satisfied. To solve the problem more efficiently and to obtain a set of non-dominated solutions, a meta-heuristic based non-dominated sorting genetic algorithm is presented. In addition, task based representation is used for solution encoding, and tournament selection that is based on rank and crowding distance is applied for offspring selection. Swapping and insertion mutations are employed to increase diversity of population and to perform intensive search. To evaluate the modified non-dominated sorting genetic algorithm, a set of modified benchmarking job shop problems obtained from the OR-Library is used, and the results are considered based on the number of non-dominated solutions and quality of schedules obtained by the algorithm.

Morphological decomposition of 2-D binary shapes into convex polygons: a heuristic algorithm.

PubMed

Xu, J

2001-01-01

In many morphological shape decomposition algorithms, either a shape can only be decomposed into shape components of extremely simple forms or a time consuming search process is employed to determine a decomposition. In this paper, we present a morphological shape decomposition algorithm that decomposes a two-dimensional (2-D) binary shape into a collection of convex polygonal components. A single convex polygonal approximation for a given image is first identified. This first component is determined incrementally by selecting a sequence of basic shape primitives. These shape primitives are chosen based on shape information extracted from the given shape at different scale levels. Additional shape components are identified recursively from the difference image between the given image and the first component. Simple operations are used to repair certain concavities caused by the set difference operation. The resulting hierarchical structure provides descriptions for the given shape at different detail levels. The experiments show that the decomposition results produced by the algorithm seem to be in good agreement with the natural structures of the given shapes. The computational cost of the algorithm is significantly lower than that of an earlier search-based convex decomposition algorithm. Compared to nonconvex decomposition algorithms, our algorithm allows accurate approximations for the given shapes at low coding costs.

On the Psychology of the Recognition Heuristic: Retrieval Primacy as a Key Determinant of Its Use

ERIC Educational Resources Information Center

Pachur, Thorsten; Hertwig, Ralph

2006-01-01

The recognition heuristic is a prime example of a boundedly rational mind tool that rests on an evolved capacity, recognition, and exploits environmental structures. When originally proposed, it was conjectured that no other probabilistic cue reverses the recognition-based inference (D. G. Goldstein & G. Gigerenzer, 2002). More recent studies…

Heuristic Chemistry--A Qualitative Study on Teaching Domain-Specific Strategies for the Six-Electron Case

ERIC Educational Resources Information Center

Graulich, Nicole; Tiemann, Rudiger; Schreiner, Peter R.

2012-01-01

We investigate the efficiency of domain-specific heuristic strategies in mastering and predicting pericyclic six-electron rearrangements. Based on recent research findings on these types of reactions a new concept has been developed that should help students identify and describe six-electron rearrangements more readily in complex molecules. The…

Heuristics for Planning University Study at a Distance.

ERIC Educational Resources Information Center

Dodds, Agnes E.; Lawrence, Jeanette A.

A model to describe how adults work on university courses at a distance from campus was developed at an Australian university. The model was designed to describe how students define the task/goal and plan their study, based on G. Ploya's (1957) Heuristic and A. Newell's and H. A. Simon's (1972) General Problem Solver. Verbal reports were obtained…

Ignorance- versus Evidence-Based Decision Making: A Decision Time Analysis of the Recognition Heuristic

ERIC Educational Resources Information Center

Hilbig, Benjamin E.; Pohl, Rudiger F.

2009-01-01

According to part of the adaptive toolbox notion of decision making known as the recognition heuristic (RH), the decision process in comparative judgments--and its duration--is determined by whether recognition discriminates between objects. By contrast, some recently proposed alternative models predict that choices largely depend on the amount of…

Crawling The Web for Libre: Selecting, Integrating, Extending and Releasing Open Source Software

NASA Astrophysics Data System (ADS)

Truslove, I.; Duerr, R. E.; Wilcox, H.; Savoie, M.; Lopez, L.; Brandt, M.

2012-12-01

Libre is a project developed by the National Snow and Ice Data Center (NSIDC). Libre is devoted to liberating science data from its traditional constraints of publication, location, and findability. Libre embraces and builds on the notion of making knowledge freely available, and both Creative Commons licensed content and Open Source Software are crucial building blocks for, as well as required deliverable outcomes of the project. One important aspect of the Libre project is to discover cryospheric data published on the internet without prior knowledge of the location or even existence of that data. Inspired by well-known search engines and their underlying web crawling technologies, Libre has explored tools and technologies required to build a search engine tailored to allow users to easily discover geospatial data related to the polar regions. After careful consideration, the Libre team decided to base its web crawling work on the Apache Nutch project (http://nutch.apache.org). Nutch is "an open source web-search software project" written in Java, with good documentation, a significant user base, and an active development community. Nutch was installed and configured to search for the types of data of interest, and the team created plugins to customize the default Nutch behavior to better find and categorize these data feeds. This presentation recounts the Libre team's experiences selecting, using, and extending Nutch, and working with the Nutch user and developer community. We will outline the technical and organizational challenges faced in order to release the project's software as Open Source, and detail the steps actually taken. We distill these experiences into a set of heuristics and recommendations for using, contributing to, and releasing Open Source Software.

A hybrid artificial bee colony algorithm for numerical function optimization

NASA Astrophysics Data System (ADS)

Alqattan, Zakaria N.; Abdullah, Rosni

2015-02-01

Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

Coalescent-based species tree inference from gene tree topologies under incomplete lineage sorting by maximum likelihood.

PubMed

Wu, Yufeng

2012-03-01

Incomplete lineage sorting can cause incongruence between the phylogenetic history of genes (the gene tree) and that of the species (the species tree), which can complicate the inference of phylogenies. In this article, I present a new coalescent-based algorithm for species tree inference with maximum likelihood. I first describe an improved method for computing the probability of a gene tree topology given a species tree, which is much faster than an existing algorithm by Degnan and Salter (2005). Based on this method, I develop a practical algorithm that takes a set of gene tree topologies and infers species trees with maximum likelihood. This algorithm searches for the best species tree by starting from initial species trees and performing heuristic search to obtain better trees with higher likelihood. This algorithm, called STELLS (which stands for Species Tree InfErence with Likelihood for Lineage Sorting), has been implemented in a program that is downloadable from the author's web page. The simulation results show that the STELLS algorithm is more accurate than an existing maximum likelihood method for many datasets, especially when there is noise in gene trees. I also show that the STELLS algorithm is efficient and can be applied to real biological datasets. © 2011 The Author. Evolution© 2011 The Society for the Study of Evolution.

In search of a consumer-focused food classification system. An experimental heuristic approach to differentiate degrees of quality.

PubMed

Torres-Ruiz, Francisco J; Marano-Marcolini, Carla; Lopez-Zafra, Esther

2018-06-01

The present paper focuses on the problems that arise in food classification systems (FCSs), especially when the food product type has different levels or grades of quality. Despite the principal function of these systems being to assist the consumer (to inform, clarify and facilitate choice and purchase), they frequently have the opposite effect. Thus, the main aim of the present research involves providing orientations for the design of effective food classification systems. To address this objective, considering the context of food product consumption (related to heuristic processing), we conducted an experimental study with 720 participants. We analysed the usefulness of heuristic elements by a factorial 2 (category length: short and long) × 3 (visual signs: colours, numbers and images) design in relation to recall and recognition activities. The results showed that the elements used to make the classification more effective for consumers vary depending on whether the user seeks to prioritize the recall or the recognition of product categories. Thus, long categories with images significantly improve recognition, and short categories with colours improve recall. A series of recommendations are provided that can help to enhance FCSs and to make them more intuitive and easier to understand for consumers. Implications with regard to theory and practice are discussed. Copyright © 2018 Elsevier Ltd. All rights reserved.

Plans, Patterns, and Move Categories Guiding a Highly Selective Search

NASA Astrophysics Data System (ADS)

Trippen, Gerhard

In this paper we present our ideas for an Arimaa-playing program (also called a bot) that uses plans and pattern matching to guide a highly selective search. We restrict move generation to moves in certain move categories to reduce the number of moves considered by the bot significantly. Arimaa is a modern board game that can be played with a standard Chess set. However, the rules of the game are not at all like those of Chess. Furthermore, Arimaa was designed to be as simple and intuitive as possible for humans, yet challenging for computers. While all established Arimaa bots use alpha-beta search with a variety of pruning techniques and other heuristics ending in an extensive positional leaf node evaluation, our new bot, Rat, starts with a positional evaluation of the current position. Based on features found in the current position - supported by pattern matching using a directed position graph - our bot Rat decides which of a given set of plans to follow. The plan then dictates what types of moves can be chosen. This is another major difference from bots that generate "all" possible moves for a particular position. Rat is only allowed to generate moves that belong to certain categories. Leaf nodes are evaluated only by a straightforward material evaluation to help avoid moves that lose material. This highly selective search looks, on average, at only 5 moves out of 5,000 to over 40,000 possible moves in a middle game position.

Optimization of the graph model of the water conduit network, based on the approach of search space reducing

NASA Astrophysics Data System (ADS)

Korovin, Iakov S.; Tkachenko, Maxim G.

2018-03-01

In this paper we present a heuristic approach, improving the efficiency of methods, used for creation of efficient architecture of water distribution networks. The essence of the approach is a procedure of search space reduction the by limiting the range of available pipe diameters that can be used for each edge of the network graph. In order to proceed the reduction, two opposite boundary scenarios for the distribution of flows are analysed, after which the resulting range is further narrowed by applying a flow rate limitation for each edge of the network. The first boundary scenario provides the most uniform distribution of the flow in the network, the opposite scenario created the net with the highest possible flow level. The parameters of both distributions are calculated by optimizing systems of quadratic functions in a confined space, which can be effectively performed with small time costs. This approach was used to modify the genetic algorithm (GA). The proposed GA provides a variable number of variants of each gene, according to the number of diameters in list, taking into account flow restrictions. The proposed approach was implemented to the evaluation of a well-known test network - the Hanoi water distribution network [1], the results of research were compared with a classical GA with an unlimited search space. On the test data, the proposed trip significantly reduced the search space and provided faster and more obvious convergence in comparison with the classical version of GA.

Toward a More Usable Home-Based Video Telemedicine System: A Heuristic Evaluation of the Clinician User Interfaces of Home-Based Video Telemedicine Systems.

PubMed

Agnisarman, Sruthy; Narasimha, Shraddhaa; Chalil Madathil, Kapil; Welch, Brandon; Brinda, Fnu; Ashok, Aparna; McElligott, James

2017-04-24

Telemedicine is the use of technology to provide and support health care when distance separates the clinical service and the patient. Home-based telemedicine systems involve the use of such technology for medical support and care connecting the patient from the comfort of their homes with the clinician. In order for such a system to be used extensively, it is necessary to understand not only the issues faced by the patients in using them but also the clinician. The aim of this study was to conduct a heuristic evaluation of 4 telemedicine software platforms-Doxy.me, Polycom, Vidyo, and VSee-to assess possible problems and limitations that could affect the usability of the system from the clinician's perspective. It was found that 5 experts individually evaluated all four systems using Nielsen's list of heuristics, classifying the issues based on a severity rating scale. A total of 46 unique problems were identified by the experts. The heuristics most frequently violated were visibility of system status and Error prevention amounting to 24% (11/46 issues) each. Esthetic and minimalist design was second contributing to 13% (6/46 issues) of the total errors. Heuristic evaluation coupled with a severity rating scale was found to be an effective method for identifying problems with the systems. Prioritization of these problems based on the rating provides a good starting point for resolving the issues affecting these platforms. There is a need for better transparency and a more streamlined approach for how physicians use telemedicine systems. Visibility of the system status and speaking the users' language are keys for achieving this. ©Sruthy Agnisarman, Shraddhaa Narasimha, Kapil Chalil Madathil, Brandon Welch, FNU Brinda, Aparna Ashok, James McElligott. Originally published in JMIR Human Factors (http://humanfactors.jmir.org), 24.04.2017.