Software For Genetic Algorithms
NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steve E.
1992-01-01
SPLICER computer program is genetic-algorithm software tool used to solve search and optimization problems. Provides underlying framework and structure for building genetic-algorithm application program. Written in Think C.
Where Genetic Algorithms Excel
Eric B. Baum; Dan Boneh; Charles Garrett
2001-01-01
We analyze the performance of a genetic algorithm (GA) we call Culling, and a variety of other algorithms, on a problem we refer to as the Additive Search Problem (ASP). We show that the problem of learning the Ising perceptron is reducible to a noisy version of ASP. Noisy ASP is the first problem we are aware of where a
Genetic Algorithms GA Quick Overview
Xiao, Jing
Genetic Algorithms #12;GA Quick Overview Developed: USA in the 1970's Early names: J. Holland;Genetic algorithms Holland's original GA is now known as the simple genetic algorithm (SGA) Other GAs use different: Representations Mutations Crossovers Selection mechanisms #12;SGA technical summary
Scheduling with genetic algorithms
NASA Technical Reports Server (NTRS)
Fennel, Theron R.; Underbrink, A. J., Jr.; Williams, George P. W., Jr.
1994-01-01
In many domains, scheduling a sequence of jobs is an important function contributing to the overall efficiency of the operation. At Boeing, we develop schedules for many different domains, including assembly of military and commercial aircraft, weapons systems, and space vehicles. Boeing is under contract to develop scheduling systems for the Space Station Payload Planning System (PPS) and Payload Operations and Integration Center (POIC). These applications require that we respect certain sequencing restrictions among the jobs to be scheduled while at the same time assigning resources to the jobs. We call this general problem scheduling and resource allocation. Genetic algorithms (GA's) offer a search method that uses a population of solutions and benefits from intrinsic parallelism to search the problem space rapidly, producing near-optimal solutions. Good intermediate solutions are probabalistically recombined to produce better offspring (based upon some application specific measure of solution fitness, e.g., minimum flowtime, or schedule completeness). Also, at any point in the search, any intermediate solution can be accepted as a final solution; allowing the search to proceed longer usually produces a better solution while terminating the search at virtually any time may yield an acceptable solution. Many processes are constrained by restrictions of sequence among the individual jobs. For a specific job, other jobs must be completed beforehand. While there are obviously many other constraints on processes, it is these on which we focussed for this research: how to allocate crews to jobs while satisfying job precedence requirements and personnel, and tooling and fixture (or, more generally, resource) requirements.
Genetic Algorithms for Simultaneous Equation
Giménez, Domingo
chromosome c and the set of variables Y and X 2. SOLVE the system 3. COMPUTE the error between the variables Genetic Algorithms for selecting the best SEM Defining a valid chromosome Initialization and EndConditions Evaluating a chromosome Crossover Mutation Random Search Experimental results Conclusions and future works
Simultaneous stabilization using genetic algorithms
Benson, R.W.; Schmitendorf, W.E. (California Univ., Irvine, CA (USA). Dept. of Mechanical Engineering)
1991-01-01
This paper considers the problem of simultaneously stabilizing a set of plants using full state feedback. The problem is converted to a simple optimization problem which is solved by a genetic algorithm. Several examples demonstrate the utility of this method. 14 refs., 8 figs.
Genetic Algorithm for Optimization: Preprocessor and Algorithm
NASA Technical Reports Server (NTRS)
Sen, S. K.; Shaykhian, Gholam A.
2006-01-01
Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.
A Genetic Algorithm Tutorial Darrell Whitley
Potter, Don
A Genetic Algorithm Tutorial Darrell Whitley Computer Science Department, Colorado State University Fort Collins, CO 80523 whitley@cs.colostate.edu Abstract This tutorial covers the canonical genetic algorithm as well as more experimental formsof genetic algorithms, including parallel island models
Digital Image Compression Using a Genetic Algorithm
Cheng Yimin; Wang Yixiao; Sun Qibin; Sun Longxiang
1999-01-01
Adigital image compression method based on a VQ coding technique is presented in this paper. Genetic algorithm is used to generate a good global optimal codebook. In the genetic algorithm, it is proposed that movable genes be used to improve the computing effect of the algorithm. Both the encoding and decoding have been simulated on a computer and the reconstructed
Dynamic Parameter Encoding for Genetic Algorithms
Schraudolph, Nicol N.
to genetic drift, a process aggravated by genetic hitchhiking -- incid- ental associations with highlyDynamic Parameter Encoding for Genetic Algorithms Nicol N. Schraudolph Richard K. Belew nici-valued parameters of the phen- otype in Holland's genetic algorithm (GA) forces either the sacrifice
Dynamic Parameter Encoding for Genetic Algorithms
Belew, Richard K.
will converge to an ar bitrary value due to genetic drift, a process aggravated by genetic hitchhikingDynamic Parameter Encoding for Genetic Algorithms Nicol N. Schraudolph Richard K. Belew nici for realvalued parameters of the phe notype in Holland's genetic algorithm (GA) forces either
Genetic algorithms for DNA sequence assembly
Parsons, R.; Burks, C. [Los Alamos National Lab., NM (United States); Forrest, S. [New Mexico Univ., Albuquerque, NM (United States). Dept. of Computer Science
1993-04-13
This paper describes a genetic algorithm application to the DNA fragment assembly problems. The genetic algorithm uses a random key representation for representing the orderings of fragments. Two different fitness functions, both based on pairwise overlap strengths between fragments, were tested. The paper concludes that the genetic algorithm is a promising method for fragment assembly problems, achieving usable solutions quickly, but that the current fitness functions are flawed and that other representations might be more appropriate.
Adaptive Reservoir Genetic Algorithm: Convergence Analysis
CRISTIAN MUNTEANU; AGOSTINHO ROSA; Superior Técnico
This paper extends the theoretical analysis of the Adaptive Reservoir Genetic Algorithm (ARGA), a variant of a Genetic Algorithm (GA) proposed by the authors in (4). We show that ARGA visits the global optimum after a fin ite number of iterations with probability one, regardless of the initialization of the population.
A Hybrid Genetic Algorithm for Classification
James D. Kelly Jr.; Lawrence Davis
1991-01-01
In this paper we describe a method for hybridiz ing a genetic algorithm and a k nearest neighbors classification algorithm. We use the genetic algo rithm and a training data set to learn real-valued weights associated with individual attributes in the data set. We use the k nearest neighbors algo rithm to classify new data records based on their weighted
Genetic algorithms for route discovery.
Gelenbe, Erol; Liu, Peixiang; Lainé, Jeremy
2006-12-01
Packet routing in networks requires knowledge about available paths, which can be either acquired dynamically while the traffic is being forwarded, or statically (in advance) based on prior information of a network's topology. This paper describes an experimental investigation of path discovery using genetic algorithms (GAs). We start with the quality-of-service (QoS)-driven routing protocol called "cognitive packet network" (CPN), which uses smart packets (SPs) to dynamically select routes in a distributed autonomic manner based on a user's QoS requirements. We extend it by introducing a GA at the source routers, which modifies and filters the paths discovered by the CPN. The GA can combine the paths that were previously discovered to create new untested but valid source-to-destination paths, which are then selected on the basis of their "fitness." We present an implementation of this approach, where the GA runs in background mode so as not to overload the ingress routers. Measurements conducted on a network test bed indicate that when the background-traffic load of the network is light to medium, the GA can result in improved QoS. When the background-traffic load is high, it appears that the use of the GA may be detrimental to the QoS experienced by users as compared to CPN routing because the GA uses less timely state information in its decision making. PMID:17186801
Genetic algorithms and the immune system
Forrest, S. (New Mexico Univ., Albuquerque, NM (USA). Dept. of Computer Science); Perelson, A.S. (Los Alamos National Lab., NM (USA))
1990-01-01
Using genetic algorithm techniques we introduce a model to examine the hypothesis that antibody and T cell receptor genes evolved so as to encode the information needed to recognize schemas that characterize common pathogens. We have implemented the algorithm on the Connection Machine for 16,384 64-bit antigens and 512 64-bit antibodies. 8 refs.
Terrainosaurus: realistic terrain synthesis using genetic algorithms
Saunders, Ryan L.
2007-04-25
genetic algorithm to blend together chunks of elevation data from the example height fields in a visually plausible manner. This method has the advantage of producing an unlimited diversity of reasonably realistic results, while requiring relatively little...
Genetic algorithms at UC Davis/LLNL
Vemuri, V.R.
1993-12-31
A tutorial introduction to genetic algorithms is given. This brief tutorial should serve the purpose of introducing the subject to the novice. The tutorial is followed by a brief commentary on the term project reports that follow.
Genetic algorithm based tomographic flow visualization
Lyons, Donald Paul
1997-01-01
A new nonlinear tomographic flow visualization technique for use in limited data situations is developed using techniques from the emerging field of evolutionary computing. The new technique uses both pure and hybrid genetic algorithms from...
A Distributed Pool Architecture for Genetic Algorithms
Roy, Gautam
2011-02-22
The genetic algorithm paradigm is a well-known heuristic for solving many problems in science and engineering in which candidate solutions, or “individuals”, are manipulated in ways analogous to biological evolution, to ...
Statistical Algorithms in Population Genetics
=pubmed #12;Solutions · Many use Hidden Markov Models (HMM) · A Markov Process is "memory- less" · A Hidden Markov process is one where you don't know the states directly, and you just observe the events Example-based algorithm #12;My goals for the summer · Dr Chen's lab works with different species of yeast, and I
Reactive power optimization by genetic algorithm
Iba, Kenji )
1994-05-01
This paper presents a new approach to optimal reactive power planning based on a genetic algorithm. Many outstanding methods to this problem have been proposed in the past. However, most of these approaches have the common defect of being caught to a local minimum solution. The integer problem which yields integer value solutions for discrete controllers/banks still remains as a difficult one. The genetic algorithm is a kind of search algorithm based on the mechanics of natural selection and genetics. This algorithm can search for a global solution using multiple paths and treat integer problems naturally. The proposed method was applied to practical 51-bus and 224-bus systems to show its feasibility and capabilities. Although this method is not as fast as sophisticated traditional methods, the concept is quite promising and useful.
Genetic Algorithms for multiple objective vehicle routing
Geiger, Martin Josef
2008-01-01
The talk describes a general approach of a genetic algorithm for multiple objective optimization problems. A particular dominance relation between the individuals of the population is used to define a fitness operator, enabling the genetic algorithm to adress even problems with efficient, but convex-dominated alternatives. The algorithm is implemented in a multilingual computer program, solving vehicle routing problems with time windows under multiple objectives. The graphical user interface of the program shows the progress of the genetic algorithm and the main parameters of the approach can be easily modified. In addition to that, the program provides powerful decision support to the decision maker. The software has proved it's excellence at the finals of the European Academic Software Award EASA, held at the Keble college/ University of Oxford/ Great Britain.
Combinatorial Multiobjective Optimization Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Crossley, William A.; Martin. Eric T.
2002-01-01
The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.
Birefringent filter design by use of a modified genetic algorithm
Yao, Jianping
Birefringent filter design by use of a modified genetic algorithm Mengtao Wen and Jianping Yao A modified genetic algorithm is proposed for the optimization of fiber birefringent filters. The orientation of the filters. Being different from the normal genetic algorithm, the algorithm proposed reduces the problem
Genetic algorithm used in interference filter's design
NASA Astrophysics Data System (ADS)
Li, Jinsong; Fang, Ying; Gao, Xiumin
2009-11-01
An approach for designing of interference filter is presented by using genetic algorithm (here after refer to as GA) here. We use GA to design band stop filter and narrow-band filter. Interference filter designed here can calculate the optimal reflectivity or transmission rate. Evaluation function used in our genetic algorithm is different from the others before. Using characteristic matrix to calculate the photonic band gap of one-dimensional photonic crystal is similar to electronic structure of doped. If the evaluation is sensitive to the deviation of photonic crystal structure, the approach by genetic algorithm is effective. A summary and explains towards some uncompleted issues are given at the end of this paper.
Applying a Genetic Algorithm to Reconfigurable Hardware
NASA Technical Reports Server (NTRS)
Wells, B. Earl; Weir, John; Trevino, Luis; Patrick, Clint; Steincamp, Jim
2004-01-01
This paper investigates the feasibility of applying genetic algorithms to solve optimization problems that are implemented entirely in reconfgurable hardware. The paper highlights the pe$ormance/design space trade-offs that must be understood to effectively implement a standard genetic algorithm within a modem Field Programmable Gate Array, FPGA, reconfgurable hardware environment and presents a case-study where this stochastic search technique is applied to standard test-case problems taken from the technical literature. In this research, the targeted FPGA-based platform and high-level design environment was the Starbridge Hypercomputing platform, which incorporates multiple Xilinx Virtex II FPGAs, and the Viva TM graphical hardware description language.
GENETIC ALGORITHM FORECASTING FOR TELECOMMUNICATIONS PRODUCTS
Havlicek, Joebob
1 GENETIC ALGORITHM FORECASTING FOR TELECOMMUNICATIONS PRODUCTS STEPHEN D. SLOAN, RAYMOND W. SAW's) for forecasting long-term quarterly sales of products in the telecommunications technology sector using widely desirable capability for many companies operating in the increasingly volatile telecommunications technology
Tuning fuzzy logic controllers by genetic algorithms
Francisco Herrera; Manuel Lozano; José L. Verdegay
1995-01-01
The performance of a fuzzy logic controller depends on its control rules and membership functions. Hence, it is very important to adjust these parameters to the process to be controlled. A method is presented for tuning fuzzy control rules by genetic algorithms to make the fuzzy logic control systems behave as closely as possible to the operator or expert behavior
A distributed pool architecture for genetic algorithms
Gautam Roy; Hyunyoung Lee; Jennifer L. Welch; Yuan Zhao; Vijitashwa Pandey; Deborah L. Thurston
2009-01-01
The genetic algorithm (GA) paradigm is a well-known heuristic for solving many problems in science and engineering. As problem sizes increase, a natural question is how to exploit advances in distributed and parallel computing to speed up the execution of GAs. This paper proposes a new distributed architecture for GAs, based on distributed storage of the individuals in a persistent
Predicting complex mineral structures using genetic algorithms.
Mohn, Chris E; Kob, Walter
2015-10-28
We show that symmetry-adapted genetic algorithms are capable of finding the ground state of a range of complex crystalline phases including layered- and incommensurate super-structures. This opens the way for the atomistic prediction of complex crystal structures of functional materials and mineral phases. PMID:26441052
Towards Automatic Image Enhancement Using Genetic Algorithms
C. Munteanu; A. Rosa; Superior Técnico
1999-01-01
This paper introduces a new automatic image enhancement technique based on real-coded Genetic Algorithms (GAs). The task of the GA is to adapt the parameters of a novel extension to a local enhancement technique similar to statistical scaling, as to enhance the contrast and detail in the image according to an objective fitness criterion. We compared our method with other
Evolving Networks: Using the Genetic Algorithm
Belew, Richard K.
hybrids of neuralnetwork learning algo rithms with evolutionary search procedures, simply because NatureEvolving Networks: Using the Genetic Algorithm with Connectionist Learning Richard K. Belew John Mc, 1990 #12; Belew, McInerney & Schraudolf: Evolving Networks i Abstract It is appealing to consider
Hybrid genetic algorithms for polypeptide energy minimization
Laurence D. Merkle; Robert L. Gaulke; Gary B. Lamont; George H. Gates Jr.; Ruth Pachter
1996-01-01
Efforts to predict polypeptide structures nearly always assume that the native conformation corresponds to the global minimum free energy state of the system. Given this assumption, a necessary step in solving the problem is the development of efficient global energy minimization techniques. We describe a hybrid genetic algorithm which incorporates efficient gradient-based minimization directly in the fitness evaluation, which is
Hybrid Genetic Algorithms for Feature Selection
Il-Seok Oh; Jin-Seon Lee; Byung-Ro Moon
2004-01-01
This paper proposes a novel hybrid genetic algorithm for feature selection. Local search operations are devised and embedded in hybrid GAs to fine-tune the search. The operations are parameterized in terms of their fine-tuning power, and their effectiveness and timing requirements are analyzed and compared. The hybridization technique produces two desirable effects: a significant improvement in the final performance and
HVAC optimisation studies: Sizing by genetic algorithm
J. A. Wright
1996-01-01
Previous research into the optimum sizing of hvac systems has focused on the use of direct search optimisation methods. Although these methods can find a solution, it is difficult for them to move discrete variables along nonlinear constraint boundaries and they often fail as a result. This paper describes the performance of a simple genetic algorithm search method when applied
MULTIOBJECTIVE PARALLEL GENETIC ALGORITHM FOR WASTE MINIMIZATION
In this research we have developed an efficient multiobjective parallel genetic algorithm (MOPGA) for waste minimization problems. This MOPGA integrates PGAPack (Levine, 1996) and NSGA-II (Deb, 2000) with novel modifications. PGAPack is a master-slave parallel implementation of a...
A hybrid neural-genetic multimodel parameter estimation algorithm
Vassilios Petridis; Emmanuel Paterakis; Athanasios Kehagias
1998-01-01
We introduce a hybrid neural-genetic multimodel parameter estimation algorithm. The algorithm is applied to structured system identification of nonlinear dynamical systems. The main components of the algorithm are: 1) a recurrent incremental credit assignment neural network which computes a credit function for each member of a generation of models; and 2) a genetic algorithm which uses the credit functions as
Genetic Algorithms for Multiple-Choice Problems
NASA Astrophysics Data System (ADS)
Aickelin, Uwe
2010-04-01
This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.
Feature Subset Selection Using Genetic Algorithm for Named Entity Recognition
Feature Subset Selection Using Genetic Algorithm for Named Entity Recognition Md. Hasanuzzaman1:sriparna.saha@gmail.com, asif.ekbal@gmail.com Abstract. In this paper, genetic algorithm (GA) is utilized to search: Genetic algorithm, Feature Selection, Maximum Entropy, Named Entity Recog- nition. 1 Introduction Named
Interactive Genetic Algorithms with Individual Fitness not Assigned by Human
Yao, Xin
to solve opti- mization problems with implicit or fuzzy indices. But human fatigue problem, resulting from Words: Optimization, genetic algorithm, interactive genetic algorithm, human fatigue, individual fitnessInteractive Genetic Algorithms with Individual Fitness not Assigned by Human Dunwei Gong (China
Genetic Algorithms applied to Problems of Forbidden Configurations
Fournier, John J.F.
Genetic Algorithms applied to Problems of Forbidden Configurations R.P. Anstee Miguel Raggi particular a Genetic Algorithm) for finding extremal matrices. We apply this technique to two forbidden and then proving the guess is indeed correct. The Genetic Algorithm was also helpful in finding the proof. Keywords
A Genetic CascadeCorrelation Learning Algorithm \\Lambda
George Mason University
A Genetic CascadeCorrelation Learning Algorithm \\Lambda Mitchell A. Potter Computer Science; however, in some applications gradient in formation may not be available. Biologically inspired genetic algorithms provide an alternative. Unfortunately, early attempts to use genetic algorithms to train connec
Comparing Heuristic Search Methods and Genetic Algorithms for Warehouse Scheduling
Whitley, Darrell
Comparing Heuristic Search Methods and Genetic Algorithms for Warehouse Scheduling L. D. Whitley, A. The techniques include a genetic algorithm, local search op erators, heuristic rules, systematic search and hybrid ap proaches. Initial results show a hybrid genetic algorithm to be superior to the other methods
Genetic Algorithms and Neural 11.1 INTRODUCTION
Whitley, Darrell
and neural networks are both inspired by computation in biological systems. A good deal of biological neural11 Genetic Algorithms and Neural Networks D. WHITLEY 11.1 INTRODUCTION Genetic algorithms. Genetic algorithms have been used in conjunction with neural networks in three major ways. First
Quantum-Inspired Genetic Algorithm or Quantum Genetic Algorithm: Which Is It?
NASA Astrophysics Data System (ADS)
Jones, Erika
2015-04-01
Our everyday work focuses on genetic algorithms (GAs) related to quantum computing where we call ``related'' algorithms those falling into one of two classes: (1) GAs run on classical computers but making use of quantum mechanical (QM) constructs and (2) GAs run on quantum hardware. Though convention has yet to be set with respect to usage of the accepted terms quantum-inspired genetic algorithm (QIGA) and quantum genetic algorithm (QGA), we find the two terms highly suitable respectively as labels for the aforementioned classes. With these specific definitions in mind, the difference between the QIGA and QGA is greater than might first be appreciated, particularly by those coming from a perspective emphasizing GA use as a general computational tool irrespective of QM aspects (1) suggested by QIGAs and (2) inherent in QGAs. We offer a theoretical standpoint highlighting key differences-both obvious, and more significantly, subtle-to be considered in general design of a QIGA versus that of a QGA.
Genetic algorithms for minimal source reconstructions
Lewis, P.S.; Mosher, J.C.
1993-12-01
Under-determined linear inverse problems arise in applications in which signals must be estimated from insufficient data. In these problems the number of potentially active sources is greater than the number of observations. In many situations, it is desirable to find a minimal source solution. This can be accomplished by minimizing a cost function that accounts from both the compatibility of the solution with the observations and for its ``sparseness``. Minimizing functions of this form can be a difficult optimization problem. Genetic algorithms are a relatively new and robust approach to the solution of difficult optimization problems, providing a global framework that is not dependent on local continuity or on explicit starting values. In this paper, the authors describe the use of genetic algorithms to find minimal source solutions, using as an example a simulation inspired by the reconstruction of neural currents in the human brain from magnetoencephalographic (MEG) measurements.
Application of Genetic Algorithms in Seismic Tomography
NASA Astrophysics Data System (ADS)
Soupios, Pantelis; Akca, Irfan; Mpogiatzis, Petros; Basokur, Ahmet; Papazachos, Constantinos
2010-05-01
In the earth sciences several inverse problems that require data fitting and parameter estimation are nonlinear and can involve a large number of unknown parameters. Consequently, the application of analytical inversion or optimization techniques may be quite restrictive. In practice, most analytical methods are local in nature and rely on a linearized form of the problem in question, adopting an iterative procedure using partial derivatives to improve an initial model. This approach can lead to a dependence of the final model solution on the starting model and is prone to entrapment in local misfit minima. Moreover, the calculation of derivatives can be computationally inefficient and create instabilities when numerical approximations are used. In contrast to these local minimization methods, global techniques that do not rely on partial derivatives, are independent of the form of the data misfit criterion, and are computationally robust. Such methods often use random processes to sample a selected wider span of the model space. In this situation, randomly generated models are assessed in terms of their data-fitting quality and the process may be stopped after a certain number of acceptable models is identified or continued until a satisfactory data fit is achieved. A new class of methods known as genetic algorithms achieves the aforementioned approximation through novel model representation and manipulations. Genetic algorithms (GAs) were originally developed in the field of artificial intelligence by John Holland more than 20 years ago, but even in this field it is less than a decade that the methodology has been more generally applied and only recently did the methodology attract the attention of the earth sciences community. Applications have been generally concentrated in geophysics and in particular seismology. As awareness of genetic algorithms grows there surely will be many more and varied applications to earth science problems. In the present work, the application of hybrid genetic algorithms in seismic tomography is examined and the efficiency of least squares and genetic methods as representative of the local and global optimization, respectively, is presented and evaluated. The robustness of both optimization methods has been tested and compared for the same source-receiver geometry and characteristics of the model structure (anomalies, etc.). A set of seismic refraction synthetic (noise free) data was used for modeling. Specifically, cross-well, down-hole and typical refraction studies using 24 geophones and 5 shoots were used to confirm the applicability of the genetic algorithms in seismic tomography. To solve the forward modeling and estimate the traveltimes, the revisited ray bending method was used supplemented by an approximate computation of the first Fresnel volume. The root mean square (rms) error as the misfit function was used and calculated for the entire random velocity model for each generation. After the end of each generation and based on the misfit of the individuals (velocity models), the selection, crossover and mutation (typical process steps of genetic algorithms) were selected continuing the evolution theory and coding the new generation. To optimize the computation time, since the whole procedure is quite time consuming, the Matlab Distributed Computing Environment (MDCE) was used in a multicore engine. During the tests, we noticed that the fast convergence that the algorithm initially exhibits (first 5 generations) is followed by progressively slower improvements of the reconstructed velocity models. Thus, to improve the final tomographic models, a hybrid genetic algorithm (GA) approach was adopted by combining the GAs with a local optimization method after several generations, on the basis of the convergence of the resulting models. This approach is shown to be efficient, as it directs the solution search towards a model region close to the global minimum solution.
Genetic algorithms in adaptive fuzzy control
NASA Technical Reports Server (NTRS)
Karr, C. Lucas; Harper, Tony R.
1992-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.
Evolutionary Computation: from Genetic Algorithms to Genetic Programming
Fernandez, Thomas
1 Evolutionary Computation: from Genetic Algorithms to Genetic Programming Ajith Abraham1 , Nadia Nedjah2 and Luiza de Macedo Mourelle3 1 School of Computer Science and Engineering Chung-Ang University 410, 2nd Engineering Building 221, Heukseok-dong, Dongjak-gu Seoul 156-756, Korea ajith
Microscale truss optimization using genetic algorithm
María Belén Prendes-Gero; Jean-Marc Drouet
2011-01-01
This paper describes the development of a genetic algorithm that is capable of optimizing the mass of micro-scale trusses.\\u000a Belonging to the group of periodic cellular materials, micro-scale trusses are characterized by the creation of a base cell\\u000a with a pattern that is repeated in space until a global structure is obtained. Investigation in this field has generally been\\u000a focused
Massive Multimodality, Deception, and Genetic Algorithms
David E. Goldberg; Kalyanmoy Deb; Jeffrey Horn
1992-01-01
This paper considers the use of genetic algorithms (GAs) for the solution of problems that are bothaverage-sense misleading (deceptive) and massively multimodal. An archetypical multimodal-deceptiveproblem, here called a bipolar deceptive problem, is defined and two generalized constructions of suchproblems are reviewed, one using reflected trap functions and one using low-order Walsh coefficients;sufficient conditions for bipolar deception are also reviewed. The
Environmental Optimization: Applications of Genetic Algorithms
Sue Ellen Haupt
The genetic algorithm (GA) has found wide acceptance in many fields, ranging from economics through engineering. In the environmental\\u000a sciences, some disciplines are using GAs regularly as a tool to solve typical problems; while in other areas, they have hardly\\u000a been assessed for use in research projects. The key to using GAs in environmental sciences is to pose the problem
Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories
NASA Technical Reports Server (NTRS)
Burchett, Bradley T.
2003-01-01
The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.
Path planning for autonomous UAV via vibrational genetic algorithm
Y. Volkan Pehlivanoglu; Oktay Baysal; Abdurrahman Hacioglu
2007-01-01
Purpose – It is aimed to provide an efficient algorithm for path planning in guidance of autonomous unmanned aerial vehicle (UAV) through 3D terrain environments. Design\\/methodology\\/approach – As a stochastic search method, vibrational genetic algorithm (VGA) is improved and used to accelerate the algorithm for path planning. Findings – Using VGA, an efficient path planning algorithm for autonomous UAV was
Optical flow optimization using parallel genetic algorithm
NASA Astrophysics Data System (ADS)
Zavala-Romero, Olmo; Botella, Guillermo; Meyer-Bäse, Anke; Meyer Base, Uwe
2011-06-01
A new approach to optimize the parameters of a gradient-based optical flow model using a parallel genetic algorithm (GA) is proposed. The main characteristics of the optical flow algorithm are its bio-inspiration and robustness against contrast, static patterns and noise, besides working consistently with several optical illusions where other algorithms fail. This model depends on many parameters which conform the number of channels, the orientations required, the length and shape of the kernel functions used in the convolution stage, among many more. The GA is used to find a set of parameters which improve the accuracy of the optical flow on inputs where the ground-truth data is available. This set of parameters helps to understand which of them are better suited for each type of inputs and can be used to estimate the parameters of the optical flow algorithm when used with videos that share similar characteristics. The proposed implementation takes into account the embarrassingly parallel nature of the GA and uses the OpenMP Application Programming Interface (API) to speedup the process of estimating an optimal set of parameters. The information obtained in this work can be used to dynamically reconfigure systems, with potential applications in robotics, medical imaging and tracking.
Selection of relevant features in a fuzzy genetic learning algorithm
Antonio González; Raúl Pérez
2001-01-01
Genetic algorithms offer a powerful search method for a variety of learning tasks, and there are different approaches in which they have been applied to learning processes. Structural learning algorithm on vague environment (SLAVE) is a genetic learning algorithm that uses the iterative approach to learn fuzzy rules. SLAVE can select the relevant features of the domain, but when working
Using genetic algorithms to optimize controller parameters for HVAC systems
W. Huang; H. N. Lam
1997-01-01
This paper presents an adaptive learning algorithm based on genetic algorithms (GA) for automatic tuning of proportional, integral and derivative (PID) controllers in Heating Ventilating and Air Conditioning (HVAC) systems to achieve optimal performance. Genetic algorithms, which are search procedures based on the mechanics of Darwin's natural selection, are employed since they have been proved to be robust and efficient
Parallel Genetic Algorithm for Alpha Spectra Fitting
NASA Astrophysics Data System (ADS)
García-Orellana, Carlos J.; Rubio-Montero, Pilar; González-Velasco, Horacio
2005-01-01
We present a performance study of alpha-particle spectra fitting using parallel Genetic Algorithm (GA). The method uses a two-step approach. In the first step we run parallel GA to find an initial solution for the second step, in which we use Levenberg-Marquardt (LM) method for a precise final fit. GA is a high resources-demanding method, so we use a Beowulf cluster for parallel simulation. The relationship between simulation time (and parallel efficiency) and processors number is studied using several alpha spectra, with the aim of obtaining a method to estimate the optimal processors number that must be used in a simulation.
Genetic algorithms for modelling and optimisation
NASA Astrophysics Data System (ADS)
McCall, John
2005-12-01
Genetic algorithms (GAs) are a heuristic search and optimisation technique inspired by natural evolution. They have been successfully applied to a wide range of real-world problems of significant complexity. This paper is intended as an introduction to GAs aimed at immunologists and mathematicians interested in immunology. We describe how to construct a GA and the main strands of GA theory before speculatively identifying possible applications of GAs to the study of immunology. An illustrative example of using a GA for a medical optimal control problem is provided. The paper also includes a brief account of the related area of artificial immune systems.
Modeling of Nonlinear Systems using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Hayashi, Kayoko; Yamamoto, Toru; Kawada, Kazuo
In this paper, a newly modeling system by using Genetic Algorithm (GA) is proposed. The GA is an evolutionary computational method that simulates the mechanisms of heredity or evolution of living things, and it is utilized in optimization and in searching for optimized solutions. Most process systems have nonlinearities, so it is necessary to anticipate exactly such systems. However, it is difficult to make a suitable model for nonlinear systems, because most nonlinear systems have a complex structure. Therefore the newly proposed method of modeling for nonlinear systems uses GA. Then, according to the newly proposed scheme, the optimal structure and parameters of the nonlinear model are automatically generated.
Genetic Algorithms in Optimization: Better than Random Search? \\Lambda
Amaral, José Nelson
Genetic Algorithms in Optimization: Better than Random Search? \\Lambda Jos'e Nelson Amaral, Ph'osGradua¸c~ao em Engenharia El'etrica Pontif'icia Universidade Cat'olica do Rio Grande do Sul 90619900 Porto of choice for individu als in Genetic Algorithms and that genetic op erators must be tailored to each
Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms
Gibbs, Trevor Howard
2011-08-08
since they modify several solutions simultaneously. All of these properties make genetic algorithms the logical choice to be the basis in answering the well location determination problem in a gas reservoir. LITERATURE SURVEY ?Genetic algorithms... PLACEMENT OPTIMIZATION IN GAS RESERVOIRS USING GENETIC ALGORITHMS A Thesis by TREVOR HOWARD GIBBS Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER...
Signal adaptive wavelet design using genetic algorithms
NASA Astrophysics Data System (ADS)
Jones, Eric; Runkle, Paul R.; Dasgupta, Nilanjan; Carin, Lawrence
2000-04-01
While discrete wavelet transforms offers a powerful combination of computational efficiency and compact representation for a broad range of signals, they are often designed without any prior knowledge of the signals under analysis. In this paper, we provide a methodology for constructing customized wavelet sand multi rate filterbanks through the application of a generalized cost function on available training data. In particular, we design wavelets that provide maximal discriminate between several signal classes, with the cost function directly tied to classification performance. Since the relationship between the filter coefficients and correct classification may be exceedingly complicated, the optimization is performed using a genetic algorithm. The multi rate filterbank is implemented in a lattice-type structure, known as lifting, which facilitates the incorporation of constraints on the search space. In addition to demonstrating the successful design of signal-adaptive wavelets, this paper validates the use of genetic algorithms as a powerful class of tools for complex system optimization. The method is applied to acoustic scattering data with classification performance evaluated in relation to both non-adaptive biorthogonal wavelets and signal-adaptive wavelets based on linear predictive constraints.
Application of genetic algorithm to steganalysis
NASA Astrophysics Data System (ADS)
Knapik, Timothy; Lo, Ephraim; Marsh, John A.
2006-05-01
We present a novel application of genetic algorithm (GA) to optimal feature set selection in supervised learning using support vector machine (SVM) for steganalysis. Steganalysis attempts to determine whether a cover object (in our case an image file) contains hidden information. This is a bivariate classification problem: the image either does or does not contain hidden data. Our SVM classifier uses a training set of images with known classification to "learn" how to classify images with unknown classification. The SVM uses a feature set, essentially a set of statistical quantities extracted from the image. The performance of the SVM classifier is heavily dependent on the feature set used. Too many features not only increase computation time but decrease performance, and too few features do not provide enough information for accurate classification. Our steganalysis technique uses entropic features that yield up to 240 features per image. The selection of an optimum feature set is a problem that lends itself well to genetic algorithm optimization. We describe this technique in detail and present a "GA optimized" feature set of 48 features that, for our application, optimizes the tradeoff between computation time and classification accuracy.
A New Challenge for Compression Algorithms: Genetic Sequences.
ERIC Educational Resources Information Center
Grumbach, Stephane; Tahi, Fariza
1994-01-01
Analyzes the properties of genetic sequences that cause the failure of classical algorithms used for data compression. A lossless algorithm, which compresses the information contained in DNA and RNA sequences by detecting regularities such as palindromes, is presented. This algorithm combines substitutional and statistical methods and appears to…
INVERSE DESIGN OF 2-D AIRFOIL VIA VIBRATIONAL GENETIC ALGORITHM
Y. Volkan PEHLIVANOGLU; Abdurrahman HACIOGLU
Within this study, it is aimed to provide an efficient algorithm for inverse design of 2-D airfoil in different flow conditions. For this purpose, as a stochastic search method, current vibrational genetic algorithm (VGA) is improved and used to accelerate the algorithm for inverse design. From the results obtained, it is concluded that VGA decreased the required time for optimal
A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem
Liu, Dong-sheng; Fan, Shu-jiang
2014-01-01
In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389
Multidisciplinary design optimization using genetic algorithms
NASA Technical Reports Server (NTRS)
Unal, Resit
1994-01-01
Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared with efficient gradient methods. Applicaiton of GA is underway for a cost optimization study for a launch-vehicle fuel-tank and structural design of a wing. The strengths and limitations of GA for launch vehicle design optimization is studied.
Spacecraft Attitude Maneuver Planning Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Kornfeld, Richard P.
2004-01-01
A key enabling technology that leads to greater spacecraft autonomy is the capability to autonomously and optimally slew the spacecraft from and to different attitudes while operating under a number of celestial and dynamic constraints. The task of finding an attitude trajectory that meets all the constraints is a formidable one, in particular for orbiting or fly-by spacecraft where the constraints and initial and final conditions are of time-varying nature. This approach for attitude path planning makes full use of a priori constraint knowledge and is computationally tractable enough to be executed onboard a spacecraft. The approach is based on incorporating the constraints into a cost function and using a Genetic Algorithm to iteratively search for and optimize the solution. This results in a directed random search that explores a large part of the solution space while maintaining the knowledge of good solutions from iteration to iteration. A solution obtained this way may be used as is or as an initial solution to initialize additional deterministic optimization algorithms. A number of representative case examples for time-fixed and time-varying conditions yielded search times that are typically on the order of minutes, thus demonstrating the viability of this method. This approach is applicable to all deep space and planet Earth missions requiring greater spacecraft autonomy, and greatly facilitates navigation and science observation planning.
Inversion for seismic anisotropy using genetic algorithms
Horne, S. Univ. of Edinburgh . Dept. of Geology and Geophysics); MacBeth, C. . Dept. of Geology and Geophysics)
1994-11-01
A general inversion scheme based on a genetic algorithm is developed to invert seismic observations for anisotropic parameters. The technique is applied to the inversion of shear-wave observations from two azimuthal VSP data sets from the Conoco test site in Oklahoma. Horizontal polarizations and time-delays are inverted for hexagonal and orthorhombic symmetries. The model solutions are consistent with previous studies using trial and error matching of full waveform synthetics. The shear-wave splitting observations suggest the presence of a shear-wave line singularity and are consistent with a dipping fracture system which is known to exist at the test site. Application of the inversion scheme prior to full waveform modeling demonstrates that a considerable saving in time is possible while retaining the same degree of accuracy.
A Parallel Genetic Algorithm for the Optimal Design of Multi-body Model Vehicle Suspensions
Jingjun Zhang; Guangyuan Liu; Ruizhen Gao; Kanghua Lou
2006-01-01
Based on an improved genetic algorithm, a parallel genetic algorithm is presented and the running environment is constituted in this paper. The parallel genetic algorithm of multi-body model vehicle suspension optimization is implemented establishing an interface between ADAMS software and the genetic algorithm. The results show that the parallel genetic algorithm developed in this paper is efficient.
James Kennedy; William M. Spears
1998-01-01
A multimodal problem generator was used to test three versions of a genetic algorithm and the binary particle swarm algorithm in a factorial time-series experiment. Specific strengths and weaknesses of the various algorithms were identified
Transonic Wing Shape Optimization Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)
2002-01-01
A method for aerodynamic shape optimization based on a genetic algorithm approach is demonstrated. The algorithm is coupled with a transonic full potential flow solver and is used to optimize the flow about transonic wings including multi-objective solutions that lead to the generation of pareto fronts. The results indicate that the genetic algorithm is easy to implement, flexible in application and extremely reliable.
Dominance Learning in Diploid Genetic Algorithms for Dynamic Optimization Problems
Yang, Shengxiang
Dominance Learning in Diploid Genetic Algorithms for Dynamic Optimization Problems Shengxiang Yang.yang@mcs.le.ac.uk ABSTRACT This paper proposes an adaptive dominance mechanism for diploidy genetic algorithms in dynamic environments. In this scheme, the genotype to phenotype mapping in each gene locus is controlled by a dominance
Plasma Xray Spectra Analysis Using Genetic Algorithms Igor E. Golovkin
Louis, Sushil J.
Plasma Xray Spectra Analysis Using Genetic Algorithms Igor E. Golovkin Department of Physics for plasma diagnostics. We use genetic algorithms to automatically analyze experi mental Xray line spectraray line spectra. 1 INTRODUCTION Xray spectroscopic analysis is a widely used method for hot dense plasma
Learning Weights for a Natural Language Grammar Using Genetic Algorithms
Ingo Schrder; Horia F. Pop; Wolfgang Menzel; Kilian A. Foth
2002-01-01
Abstract: Experiments in learning the weights of a natural language grammarby means of genetic algorithms are described. The results suggest that a manuallyprepared grammar can be improved, both in terms of quality and parsing time.Additionally, a good grammar can be derived from an almost random initial assignment.Key words: Constraint Dependency Grammar, Constraints Satisfaction, GeneticAlgorithms1
Automatic Design of Hierarchical Fuzzy Controllers Using Genetic Algorithms
Hoffmann, Frank
to hidden neurons in neural networks. The architecture of the hierarchical fuzzy controller is shown in Fig controlling a dynamic system. Besides adaptive fuzzy systems and neuro fuzzy systems, genetic algorithms (GAAutomatic Design of Hierarchical Fuzzy Controllers Using Genetic Algorithms Frank Ho mann, Gerd P
DARWIN: A Genetic Algorithm Language Arslan Arslan and Gktrk oluk
Ucoluk, Gokturk
DARWIN: A Genetic Algorithm Language Arslan Arslan and Göktürk Üçoluk Abstract This article describes the DARWIN Project, which is a Genetic Algorithm programming language and its C Cross code. The syntax of the DARWIN language and an implementational overview of the the cross
Clonal Selection based Genetic Algorithm for Workflow Service Selection
Ludwig, Simone
Clonal Selection based Genetic Algorithm for Workflow Service Selection Simone A. Ludwig North service selection of workflows is a very important aspect for service-oriented systems. The selection of services selected. In this paper, we propose an improved version of the standard genetic algorithm approach
Genetic Algorithms with Dynamic Niche Sharing for Multimodal Function Optimization
Brad L. Miller; Michael J. Shaw
1996-01-01
Genetic algorithms utilize populations of individualhypotheses that converge over time to a singleoptimum, even within a multimodal domain. This paperexamines methods that enable genetic algorithms to identifymultiple optima within multimodal domains by maintainingpopulation members within the niches defined bythe multiple optima. A new mechanism, Dynamic NicheSharing, is developed that is able to efficiently identifyand search multiple niches (peaks) in a
Genetic algorithm optimization for aerospace electromagnetic design and analysis
J. Michael Johnson; Yahya Rahmat-Samii
1996-01-01
This paper provides a tutorial overview of a new approach to optimization for aerospace electromagnetics known as the Genetic Algorithm. Genetic Algorithm (GA) optimizers are robust, stochastic search methods modeled on the concepts of natural selection and evolution. The relationship between traditional optimization techniques and GA is discussed and the details of GA optimization implementation are explored. The tutorial overview
Darwinian Evolution in Parallel Universes: A Parallel Genetic Algorithm for
Zhu, Mu
Darwinian Evolution in Parallel Universes: A Parallel Genetic Algorithm for Variable Selection Mu outcome of interest commonly arises in various industrial engineering applications. The genetic algorithm modification. Our idea is to run a number of GAs in parallel without allowing each GA to fully converge
Skill-based Resource Allocation using Genetic Algorithms and Ontologies
Levine, John
Skill-based Resource Allocation using Genetic Algorithms and Ontologies Kushan Nammuni1 , John the use of genetic algorithms, combined with an ontology, in producing optimal allocations of tutors to tutorials. The ontology is used to support partial matching; it helps determine which tutors' skills (i
Risk-Resilient Heuristics and Genetic Algorithms for
Hwang, Kai
heuristics, genetic algorithm, replication scheduling, risk resilience, NAS and PSA benchmarks, performance level from high to low. Obviously, the scheduling of jobs has to take the risk factor into accountRisk-Resilient Heuristics and Genetic Algorithms for Security-Assured Grid Job Scheduling Shanshan
How Genetic Algorithms Can Improve a Pacemaker Effciency
Dumas, Laurent
How Genetic Algorithms Can Improve a Pacemaker Effciency Laurent Dumas Laboratoire Jacques In this paper, we propose the use of Genetic Algorithms as a tool for improving a pacemaker efficiency induced in the sinus node, the natural pacemaker, then propagates through the atria and reaches
Genetic Algorithms and Artificial Life August 13,1998
Ruppin, Eytan
tool and an automated design method for Alife objects. In fact, GA's are the most widely used modelsGenetic Algorithms and Artificial Life Ziv Kedem August 13,1998 This is a summary of an article ''Genetic algorithms and Artificial Life'', published by Melanie Mitchell from Santa Fe Institute
High Frequency Foreign Exchange Trading Strategies Based on Genetic Algorithms
Hua Zhang; Ruoen Ren
2010-01-01
Foreign Exchange trading has emerged in recent times as a significant activity in many countries. Trading strategies and their parameters are heuristically or subjectively constructed. Recently, artificial intelligence techniques such as fuzzy logic, neural networks and genetic algorithms are used to solve various problems in trading. In this paper we used genetic algorithms to generate the most profitable trading strategy
INTEGRATED RETARDATION CONTROL USING NEURAL NETWORKS WITH GENETIC ALGORITHMS
Wolff, Krister
INTEGRATED RETARDATION CONTROL USING NEURAL NETWORKS WITH GENETIC ALGORITHMS Peter Lingman.lingman@volvo.com Abstract: Using the powerful techniques of neural networks and genetic algorithms, a brake system brakes in order to minimize wear cost of pad, disc, and tyres is investigated. The neural network
Genetic Algorithms for Quantum Circuit Design Evolving a Simpler Teleportation Circuit
Michigan, University of
Genetic Algorithms for Quantum Circuit Design Evolving a Simpler Teleportation Circuit Taro ever known. keyword: genetic algorithms, quantum teleportation, quantum computer, quantum computing-mentioned previous studies, we apply genetic algorithms to designing a quantum teleportation circuit. Quantum
Data quality measurement on categorical data using genetic algorithm
Vizhi, J Malar
2012-01-01
Data quality on categorical attribute is a difficult problem that has not received as much attention as numerical counterpart. Our basic idea is to employ association rule for the purpose of data quality measurement. Strong rule generation is an important area of data mining. Association rule mining problems can be considered as a multi objective problem rather than as a single objective one. The main area of concentration was the rules generated by association rule mining using genetic algorithm. The advantage of using genetic algorithm is to discover high level prediction rules is that they perform a global search and cope better with attribute interaction than the greedy rule induction algorithm often used in data mining. Genetic algorithm based approach utilizes the linkage between association rule and feature selection. In this paper, we put forward a Multi objective genetic algorithm approach for data quality on categorical attributes. The result shows that our approach is outperformed by the objectives...
Random Volumetric MRI Trajectories via Genetic Algorithms
Curtis, Andrew Thomas; Anand, Christopher Kumar
2008-01-01
A pseudorandom, velocity-insensitive, volumetric k-space sampling trajectory is designed for use with balanced steady-state magnetic resonance imaging. Individual arcs are designed independently and do not fit together in the way that multishot spiral, radial or echo-planar trajectories do. Previously, it was shown that second-order cone optimization problems can be defined for each arc independent of the others, that nulling of zeroth and higher moments can be encoded as constraints, and that individual arcs can be optimized in seconds. For use in steady-state imaging, sampling duty cycles are predicted to exceed 95 percent. Using such pseudorandom trajectories, aliasing caused by under-sampling manifests itself as incoherent noise. In this paper, a genetic algorithm (GA) is formulated and numerically evaluated. A large set of arcs is designed using previous methods, and the GA choses particular fit subsets of a given size, corresponding to a desired acquisition time. Numerical simulations of 1 second acquisitions show good detail and acceptable noise for large-volume imaging with 32 coils. PMID:18604305
Robot path planning using a genetic algorithm
NASA Technical Reports Server (NTRS)
Cleghorn, Timothy F.; Baffes, Paul T.; Wang, Liu
1988-01-01
Robot path planning can refer either to a mobile vehicle such as a Mars Rover, or to an end effector on an arm moving through a cluttered workspace. In both instances there may exist many solutions, some of which are better than others, either in terms of distance traversed, energy expended, or joint angle or reach capabilities. A path planning program has been developed based upon a genetic algorithm. This program assumes global knowledge of the terrain or workspace, and provides a family of good paths between the initial and final points. Initially, a set of valid random paths are constructed. Successive generations of valid paths are obtained using one of several possible reproduction strategies similar to those found in biological communities. A fitness function is defined to describe the goodness of the path, in this case including length, slope, and obstacle avoidance considerations. It was found that with some reproduction strategies, the average value of the fitness function improved for successive generations, and that by saving the best paths of each generation, one could quite rapidly obtain a collection of good candidate solutions.
Closed Loop System Identification with Genetic Algorithms
NASA Technical Reports Server (NTRS)
Whorton, Mark S.
2004-01-01
High performance control design for a flexible space structure is challenging since high fidelity plant models are di.cult to obtain a priori. Uncertainty in the control design models typically require a very robust, low performance control design which must be tuned on-orbit to achieve the required performance. Closed loop system identi.cation is often required to obtain a multivariable open loop plant model based on closed-loop response data. In order to provide an accurate initial plant model to guarantee convergence for standard local optimization methods, this paper presents a global parameter optimization method using genetic algorithms. A minimal representation of the state space dynamics is employed to mitigate the non-uniqueness and over-parameterization of general state space realizations. This control-relevant system identi.cation procedure stresses the joint nature of the system identi.cation and control design problem by seeking to obtain a model that minimizes the di.erence between the predicted and actual closed-loop performance.
Lunar Habitat Optimization Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
SanScoucie, M. P.; Hull, P. V.; Tinker, M. L.; Dozier, G. V.
2007-01-01
Long-duration surface missions to the Moon and Mars will require bases to accommodate habitats for the astronauts. Transporting the materials and equipment required to build the necessary habitats is costly and difficult. The materials chosen for the habitat walls play a direct role in protection against each of the mentioned hazards. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Clearly, an optimization method is warranted for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat wall design tool utilizing genetic algorithms (GAs) has been developed. GAs use a "survival of the fittest" philosophy where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multiobjective formulation of up-mass, heat loss, structural analysis, meteoroid impact protection, and radiation protection. This Technical Publication presents the research and development of this tool as well as a technique for finding the optimal GA search parameters.
Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous
NASA Technical Reports Server (NTRS)
Karr, C. L.; Freeman, L. M.; Meredith, D. L.
1990-01-01
The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.
Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.
2005-01-01
A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.
Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.
2004-01-01
A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.
Solving the Vehicle Routing Problem with Genetic Algorithms
Solving the Vehicle Routing Problem with Genetic Algorithms Ãslaug SÃ³ley BjarnadÃ³ttir April 2004 Algorithms are used to solve the Capacitated Vehicle Routing Problem. The problem involves optimising a fleet.1 The Vehicle Routing Problem . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.1 The Problem
Exogenous parameter selection in a real-valued genetic algorithm
Charles E. Kaiser; Gary B. Lamont; Laurence D. Merkle; George H. Gates; Ruth Pachter
1997-01-01
To evaluate the performance of a real valued genetic algorithm (GA) exploiting domain knowledge, we systematically evaluate the effect of exogenous parameters using analysis of variance. The GA platform used for this study is Genocop-III, a real valued, co evolutionary algorithm implementation for numerical optimization. We use the protein structure prediction (PSP) problem as our test domain. Nearly all PSP
Optimum spacing design of grillage systems using a genetic algorithm
M. P. Saka; A. Daloglu; F. Malhas
2000-01-01
In this study a genetic algorithm based method is developed for the optimum design of grillage systems. The algorithm not only selects the optimum sections for the grillage elements from a set of standard universal beam sections, but also finds the optimum spacing required for the grillage system. Deflection limitations and allowable stress constraints are considered in the formulation of
Adaptive Elitist-Population Based Genetic Algorithm for Multimodal Function
Huang, Jianwei
individuals, which may locate on different peaks, eventually converge to one peak due to genetic drift. Thus, standard EAs generally only end up with one solution. The genetic drift phenomenon is even more seriousAdaptive Elitist-Population Based Genetic Algorithm for Multimodal Function Optimization Kwong
Using Genetic Algorithms to optimize ACS-TSP Marcin L. Pilat and Tony White
White, Tony
-TSP algorithm as a Genetic Algorithm modification to ACS-TSP. The algorithm uses a GA to evolve a populationUsing Genetic Algorithms to optimize ACS-TSP Marcin L. Pilat and Tony White School of Computer,arpwhite}@scs.carleton.ca Abstract. We propose the addition of Genetic Algorithms to Ant Colony System (ACS) applied to improve
Optimization of computer-generated binary holograms using genetic algorithms
NASA Astrophysics Data System (ADS)
Cojoc, Dan; Alexandrescu, Adrian
1999-11-01
The aim of this paper is to compare genetic algorithms against direct point oriented coding in the design of binary phase Fourier holograms, computer generated. These are used as fan-out elements for free space optical interconnection. Genetic algorithms are optimization methods which model the natural process of genetic evolution. The configuration of the hologram is encoded to form a chromosome. To start the optimization, a population of different chromosomes randomly generated is considered. The chromosomes compete, mate and mutate until the best chromosome is obtained according to a cost function. After explaining the operators that are used by genetic algorithms, this paper presents two examples with 32 X 32 genes in a chromosome. The crossover type and the number of mutations are shown to be important factors which influence the convergence of the algorithm. GA is demonstrated to be a useful tool to design namely binary phase holograms of complicate structures.
Method of mechanism synthesis by hybrid genetic algorithm
O'Neil, Robert Anthony
1999-01-01
solutions allows mechanism designers to choose the mechanism that best fits the particular physical constraints of the system they are designing. The genetic algorithm was tailored to a particular mechanism problem by the choice of crossover and mutation...
Mobile transporter path planning using a genetic algorithm approach
NASA Technical Reports Server (NTRS)
Baffes, Paul; Wang, Lui
1988-01-01
The use of an optimization technique known as a genetic algorithm for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the Space Station which must be able to reach any point of the structure autonomously. Specific elements of the genetic algorithm are explored in both a theoretical and experimental sense. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research. However, trajectory planning problems are common in space systems and the genetic algorithm provides an attractive alternative to the classical techniques used to solve these problems.
Genetic Algorithm Based Damage Control For Shipboard Power Systems
Amba, Tushar
2010-07-14
Power system level. The proposed method used a constrained binary genetic algorithm to find an optimal network configuration. An optimal network configuration is a configuration which restores all of the de-energized loads that are possible...
Designing Teams of Unattended Ground Sensors Using Genetic Algorithms
Wu, Annie S.
Designing Teams of Unattended Ground Sensors Using Genetic Algorithms Ayse S. Yilmaz1 and Brian N sensors with varying capabilities. Detection percentage remains stable regardless of changes in the enemy
Designing Teams of Unattended Ground Sensors Using Genetic Algorithms
Wu, Annie S.
Designing Teams of Unattended Ground Sensors Using Genetic Algorithms Ayse S. Yilmaz 1 and Brian N configurations using sensors with varying capabilities. Detection percentage remains stable regardless of changes
Reliability assessment of electrical power systems using genetic algorithms
Samaan, Nader Amin Aziz
2004-11-15
The first part of this dissertation presents an innovative method for the assessment of generation system reliability. In this method, genetic algorithm (GA) is used as a search tool to truncate the probability state space and to track the most...
Cyclic Genetic Algorithm with Conditional Branching PredatorPrey Scenario
Parker, Gary B.
Cyclic Genetic Algorithm with Conditional Branching PredatorPrey Scenario Gary Parker Computer controller predator in a predatorprey scenario. Keywords: Evolutionary robotics, learning control, program the time needed initial program development, more importantly, means learning adaptive control changing
An improved localization algorithm based on genetic algorithm in wireless sensor networks.
Peng, Bo; Li, Lei
2015-04-01
Wireless sensor network (WSN) are widely used in many applications. A WSN is a wireless decentralized structure network comprised of nodes, which autonomously set up a network. The node localization that is to be aware of position of the node in the network is an essential part of many sensor network operations and applications. The existing localization algorithms can be classified into two categories: range-based and range-free. The range-based localization algorithm has requirements on hardware, thus is expensive to be implemented in practice. The range-free localization algorithm reduces the hardware cost. Because of the hardware limitations of WSN devices, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. However, these techniques usually have higher localization error compared to the range-based algorithms. DV-Hop is a typical range-free localization algorithm utilizing hop-distance estimation. In this paper, we propose an improved DV-Hop algorithm based on genetic algorithm. Simulation results show that our proposed algorithm improves the localization accuracy compared with previous algorithms. PMID:25852782
REPRESENTING RECTILINEAR STEINER TREES IN GENETIC ALGORITHMS
Julstrom, Bryant A.
of Computer Science St. Cloud State University 720 Fourth Avenue South St. Cloud, MN 56301 julstrom and with popula- tions seeded with a single chromosome that represented a short rectilinear Steiner tree. The algorithm identi- #12;ed much shorter trees using the weighted coding, and seeding the population improved
Evolving Temporal Association Rules with Genetic Algorithms
Hopgood, Adrian
algorithm to simultaneously search the rule space and temporal space. A methodology for validating more frequently in the days leading to a large sports event, or when an unforeseen event occurs association rules without exhaustively searching the itemset space and temporal space. The temporal rules
Evolving Distributed Algorithms with Genetic Programming: Election
Fernandez, Thomas
for it are swarming behaviors [33] in nature which have evolved over millions of years. Figure 2: Evolutionary behavior design. Evolutionary algorithms [3, 53] copy the natural evolution in order to solve complex of a program for any distributed system is basically the transformation of an expected behavior of a network
Segmentation of color images using genetic algorithm with image histogram
NASA Astrophysics Data System (ADS)
Sneha Latha, P.; Kumar, Pawan; Kahu, Samruddhi; Bhurchandi, Kishor M.
2015-02-01
This paper proposes a family of color image segmentation algorithms using genetic approach and color similarity threshold in terns of Just noticeable difference. Instead of segmenting and then optimizing, the proposed technique directly uses GA for optimized segmentation of color images. Application of GA on larger size color images is computationally heavy so they are applied on 4D-color image histogram table. The performance of the proposed algorithms is benchmarked on BSD dataset with color histogram based segmentation and Fuzzy C-means Algorithm using Probabilistic Rand Index (PRI). The proposed algorithms yield better analytical and visual results.
A hybrid of the genetic algorithm and concurrent simplex
Randolph, David Ethan
1995-01-01
A HYBRID OF THE GENETIC ALGORITHM AND CONCURRENT SIMPLEX A Thesis by DAVID ETHAN RANDOLPH Submitted to the Office of Graduate Studies of Texas ARM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May... 1995 Major Subject: Computer Science A HYBRID OF THE GENETIC ALGORITHM AND CONCURRENT SIMPLEX A Thesis DAVID ETHAN RANDOLPH Submitted to Texas AkM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE...
Genetic Algorithm Design in Urban Spatial Growth Modeling
Yu Zhuo; Wu Zhihua
2010-01-01
In order to reflect the dynamic, multi-objective and non-linear features and provide stronger decision-making support for urban spatial growth, this paper explores methods and technical route using genetic algorithm in urban spatial growth model, including encoding used for expressing urban growth individual, initial cluster setting for genetic algorithm operation beginning, fitness function design for evaluating individual strengths and weaknesses and
Arash Ghorbannia Delavar; M. Nejadkheirallah; M. Motalleb
2010-01-01
In this paper with studying of all parameters in grid environment a new scheduling algorithm for independent task is introduced according to Genetic Algorithm. This algorithm can be more efficient and more dependable than similar previous algorithms. The simulated results and reasons for reaching to better makespan and more efficiency in the grid environment. In the grids with high fault
Duan, Qian-Qian; Yang, Gen-Ke; Pan, Chang-Chun
2014-01-01
A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method. PMID:24772031
On The Use of Genetic Algorithm with Elitism in Robust and Nonparametric Multivariate Analysis
Chakraborty, Biman
1 On The Use of Genetic Algorithm with Elitism in Robust and Nonparametric Multivariate Analysis for such algorithms getting trapped in some local optimum. Here we propose genetic algorithm with elitism as a way
An Agent Inspired Reconfigurable Computing Implementation of a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Weir, John M.; Wells, B. Earl
2003-01-01
Many software systems have been successfully implemented using an agent paradigm which employs a number of independent entities that communicate with one another to achieve a common goal. The distributed nature of such a paradigm makes it an excellent candidate for use in high speed reconfigurable computing hardware environments such as those present in modem FPGA's. In this paper, a distributed genetic algorithm that can be applied to the agent based reconfigurable hardware model is introduced. The effectiveness of this new algorithm is evaluated by comparing the quality of the solutions found by the new algorithm with those found by traditional genetic algorithms. The performance of a reconfigurable hardware implementation of the new algorithm on an FPGA is compared to traditional single processor implementations.
Bayesian network structure learning using chaos hybrid genetic algorithm
NASA Astrophysics Data System (ADS)
Shen, Jiajie; Lin, Feng; Sun, Wei; Chang, KC
2012-06-01
A new Bayesian network (BN) learning method using a hybrid algorithm and chaos theory is proposed. The principles of mutation and crossover in genetic algorithm and the cloud-based adaptive inertia weight were incorporated into the proposed simple particle swarm optimization (sPSO) algorithm to achieve better diversity, and improve the convergence speed. By means of ergodicity and randomicity of chaos algorithm, the initial network structure population is generated by using chaotic mapping with uniform search under structure constraints. When the algorithm converges to a local minimal, a chaotic searching is started to skip the local minima and to identify a potentially better network structure. The experiment results show that this algorithm can be effectively used for BN structure learning.
Community detection based on modularity and an improved genetic algorithm
NASA Astrophysics Data System (ADS)
Shang, Ronghua; Bai, Jing; Jiao, Licheng; Jin, Chao
2013-03-01
Complex networks are widely applied in every aspect of human society, and community detection is a research hotspot in complex networks. Many algorithms use modularity as the objective function, which can simplify the algorithm. In this paper, a community detection method based on modularity and an improved genetic algorithm (MIGA) is put forward. MIGA takes the modularity Q as the objective function, which can simplify the algorithm, and uses prior information (the number of community structures), which makes the algorithm more targeted and improves the stability and accuracy of community detection. Meanwhile, MIGA takes the simulated annealing method as the local search method, which can improve the ability of local search by adjusting the parameters. Compared with the state-of-art algorithms, simulation results on computer-generated and four real-world networks reflect the effectiveness of MIGA.
Optimization of genomic selection training populations with a genetic algorithm
Technology Transfer Automated Retrieval System (TEKTRAN)
In this article, we derive a computationally efficient statistic to measure the reliability of estimates of genetic breeding values for a fixed set of genotypes based on a given training set of genotypes and phenotypes. We adopt a genetic algorithm scheme to find a training set of certain size from ...
The Automatic Generation of Mutation Operators for Genetic Algorithms
Woodward, John
for the comparison of human-designed and machine-designed mutation operators. In other words, we cannot meaningfullyThe Automatic Generation of Mutation Operators for Genetic Algorithms [Workshop on Evolutionary, SCOTLAND. jerry.swan@cs.stir.ac.uk ABSTRACT We automatically generate mutation operators for Genetic
Domain Knowledge for Genetic Algorithms Sushil J. Louis
Louis, Sushil J.
design problems; specifically, the structural design and optimization of trusses to ground our discussion the topology, geometry, and component properties of the structure. Preliminary results indicate that genetic in a genetic algorithm We chose the structural design and optimization of trusses as the application domain
A Genetic Algorithm with Multiple Reading Frames Terence Soule
Soule, Terence
A Genetic Algorithm with Multiple Reading Frames Terence Soule Department of Computer Science this compression is so extensive their genes are effectively longer than their DNA. In this paper a modification of a simple genetic al gorithm (GA) is introduced that uses mul tiple reading frames. It is shown that some
Feature Selection Methods: Genetic Algorithms vs. Greedylike Search
George Mason University
. The comparison is performed on three real world problems: texture recognition, breast cancer detection, and coalFeature Selection Methods: Genetic Algorithms vs. Greedylike Search Haleh Vafaie and Ibrahim F feature selection methods, the Importance Score (IS) which is based on a greedylike search and a genetic
Genetic programming can be used to automatically discover algorithms for
Spector, Lee
ABSTRACT Genetic programming can be used to automatically discover algorithms for quantum computers of genetic programming to quantum computation and vice versa. 1. Quantum Computing Quantum computers to be bound by the same limits of Turing computability, physicists argue that quantum computers can solve
Two Fast Tree-Creation Algorithms for Genetic Programming
George Mason University
commonly take the form of trees representing LISP s-expressions, and a typ- ical evolutionary run produces bloat, the tendency of GP trees to grow during the evolutionary process independent of any increase1 Two Fast Tree-Creation Algorithms for Genetic Programming Sean Luke Abstract--Genetic programming
Active flatness control of membrane structures using adaptive genetic algorithm
NASA Astrophysics Data System (ADS)
Wang, Xiaoyun; Zheng, Wanping; Hu, Yan-Ru
2007-04-01
Membrane structures are attracting attention as excellent candidates for lightweight large space structures, which can be utilized to improve the performance and reduce the cost of space exploration and earth observation missions. Membrane structures can be stowed to a small volume during launch and function as large structures after deployed. For many applications, maintaining surface accuracy of membranes is extremely important to achieve satisfactory performance, especially for membrane antennas and adaptive optics. Active flatness control is a vital technology to maintain surface accuracy of membrane structures. In this research, multiple shape memory alloy (SMA) actuators around the boundary of a rectangular membrane are used to apply tension forces to membrane structures to compensate wrinkle effects. The dynamics of membrane structures is nonlinear and computationally expensive, hence unfeasible to be used in real-time active flatness control. As a parallel direct searching method, genetic algorithm (GA) is used search optimal tension force combination on a high dimensional nonlinear surface. Due to increasing number of tension forces to search, the convergence is more difficult to attain. In order to increase responsiveness and convergence of genetic algorithm, an adaptive genetic algorithm (AGA) is proposed. Adaptive rules are incorporated in a modified genetic algorithm to regulate control parameters of genetic algorithm. Through numerical simulation and experimental studies, it is demonstrated that AGA can expedite its search process and prevent premature convergence.
A parallel genetic algorithm for the set partitioning problem
Levine, D.
1994-05-01
In this dissertation the author reports on his efforts to develop a parallel genetic algorithm and apply it to the solution of set partitioning problem -- a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. He developed a distributed steady-state genetic algorithm in conjunction with a specialized local search heuristic for solving the set partitioning problem. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. The authors found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulation found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, high-quality integer feasible solutions were found for problems with 36,699 and 43,749 integer variables, respectively. A notable limitation they found was the difficulty solving problems with many constraints.
Constrained minimization of smooth functions using a genetic algorithm
NASA Technical Reports Server (NTRS)
Moerder, Daniel D.; Pamadi, Bandu N.
1994-01-01
The use of genetic algorithms for minimization of differentiable functions that are subject to differentiable constraints is considered. A technique is demonstrated for converting the solution of the necessary conditions for a constrained minimum into an unconstrained function minimization. This technique is extended as a global constrained optimization algorithm. The theory is applied to calculating minimum-fuel ascent control settings for an energy state model of an aerospace plane.
Use of a genetic algorithm to analyze robust stability problems
Murdock, T.M.; Schmitendorf, W.E.; Forrest, S.
1990-01-01
This note resents a genetic algorithm technique for testing the stability of a characteristic polynomial whose coefficients are functions of unknown but bounded parameters. This technique is fast and can handle a large number of parametric uncertainties. We also use this method to determine robust stability margins for uncertain polynomials. Several benchmark examples are included to illustrate the two uses of the algorithm. 27 refs., 4 figs.
A pipelined FPGA implementation of an encryption algorithm based on genetic algorithm
NASA Astrophysics Data System (ADS)
Thirer, Nonel
2013-05-01
With the evolution of digital data storage and exchange, it is essential to protect the confidential information from every unauthorized access. High performance encryption algorithms were developed and implemented by software and hardware. Also many methods to attack the cipher text were developed. In the last years, the genetic algorithm has gained much interest in cryptanalysis of cipher texts and also in encryption ciphers. This paper analyses the possibility to use the genetic algorithm as a multiple key sequence generator for an AES (Advanced Encryption Standard) cryptographic system, and also to use a three stages pipeline (with four main blocks: Input data, AES Core, Key generator, Output data) to provide a fast encryption and storage/transmission of a large amount of data.
Cheating for Problem Solving: A Genetic Algorithm with Social Interactions
Lahoz-Beltra, Rafeal; Aickelin, Uwe
2010-01-01
We propose a variation of the standard genetic algorithm that incorporates social interaction between the individuals in the population. Our goal is to understand the evolutionary role of social systems and its possible application as a non-genetic new step in evolutionary algorithms. In biological populations, ie animals, even human beings and microorganisms, social interactions often affect the fitness of individuals. It is conceivable that the perturbation of the fitness via social interactions is an evolutionary strategy to avoid trapping into local optimum, thus avoiding a fast convergence of the population. We model the social interactions according to Game Theory. The population is, therefore, composed by cooperator and defector individuals whose interactions produce payoffs according to well known game models (prisoner's dilemma, chicken game, and others). Our results on Knapsack problems show, for some game models, a significant performance improvement as compared to a standard genetic algorithm.
A genetic algorithm approach in interface and surface structure optimization
Zhang, Jian
2010-05-16
The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the material structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.
Automatic page layout using genetic algorithms for electronic albuming
NASA Astrophysics Data System (ADS)
Geigel, Joe; Loui, Alexander C. P.
2000-12-01
In this paper, we describe a flexible system for automatic page layout that makes use of genetic algorithms for albuming applications. The system is divided into two modules, a page creator module which is responsible for distributing images amongst various album pages, and an image placement module which positions images on individual pages. Final page layouts are specified in a textual form using XML for printing or viewing over the Internet. The system makes use of genetic algorithms, a class of search and optimization algorithms that are based on the concepts of biological evolution, for generating solutions with fitness based on graphic design preferences supplied by the user. The genetic page layout algorithm has been incorporated into a web-based prototype system for interactive page layout over the Internet. The prototype system is built using client-server architecture and is implemented in java. The system described in this paper has demonstrated the feasibility of using genetic algorithms for automated page layout in albuming and web-based imaging applications. We believe that the system adequately proves the validity of the concept, providing creative layouts in a reasonable number of iterations. By optimizing the layout parameters of the fitness function, we hope to further improve the quality of the final layout in terms of user preference and computation speed.
The multi-niche crowding genetic algorithm: Analysis and applications
Cedeno, W.
1995-09-01
The ability of organisms to evolve and adapt to the environment has provided mother nature with a rich and diverse set of species. Only organisms well adapted to their environment can survive from one generation to the next, transferring on the traits, that made them successful, to their offspring. Competition for resources and the ever changing environment drives some species to extinction and at the same time others evolve to maintain the delicate balance in nature. In this disertation we present the multi-niche crowding genetic algorithm, a computational metaphor to the survival of species in ecological niches in the face of competition. The multi-niche crowding genetic algorithm maintains stable subpopulations of solutions in multiple niches in multimodal landscapes. The algorithm introduces the concept of crowding selection to promote mating among members with qirnilar traits while allowing many members of the population to participate in mating. The algorithm uses worst among most similar replacement policy to promote competition among members with similar traits while allowing competition among members of different niches as well. We present empirical and theoretical results for the success of the multiniche crowding genetic algorithm for multimodal function optimization. The properties of the algorithm using different parameters are examined. We test the performance of the algorithm on problems of DNA Mapping, Aquifer Management, and the File Design Problem. Applications that combine the use of heuristics and special operators to solve problems in the areas of combinatorial optimization, grouping, and multi-objective optimization. We conclude by presenting the advantages and disadvantages of the algorithm and describing avenues for future investigation to answer other questions raised by this study.
X. H. Shi; L. M. Wan; H. P. Lee; X. W. Yang; L. M. Wang; Y. C. Liang
2003-01-01
This paper presents an improved genetic algorithm with variable population-size (VPGA) inspired by the natural features of the variable size of the population. Based on the VPGA and the particle swarm optimization (PSO) algorithms, this paper also proposes a novel hybrid approach called PSO-GA based hybrid evolutionary algorithm (PGBHEA). Simulations show that both VPGA and PGBHEA are effective for the
The Ordered Clustered Travelling Salesman Problem: A Hybrid Genetic Algorithm
Ahmed, Zakir Hussain
2014-01-01
The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances. PMID:24701148
Selection Intensity in Genetic Algorithms with Generation Gaps
Cantu-Paz, E.
2000-01-19
This paper presents calculations of the selection intensity of common selection and replacement methods used in genetic algorithms (GAs) with generation gaps. The selection intensity measures the increase of the average fitness of the population after selection, and it can be used to predict the average fitness of the population at each iteration as well as the number of steps until the population converges to a unique solution. In addition, the theory explains the fast convergence of some algorithms with small generation gaps. The accuracy of the calculations was verified experimentally with a simple test function. The results of this study facilitate comparisons between different algorithms, and provide a tool to adjust the selection pressure, which is indispensable to obtain robust algorithms.
Using genetic algorithms to search for an optimal investment strategy
NASA Astrophysics Data System (ADS)
Mandere, Edward; Xi, Haowen
2007-10-01
In this experiment we used genetic algorithms to search for an investment strategy by dividing capital among different stocks with varying returns. The algorithm involves having a ``manager'' who divides his capital among various ``experts'' each of whom has a simple investment strategy. The expert strategies act like genes, experiencing mutation and crossover, in a selection process using previous returns as the fitness function. When algorithm was run with test data where the optimal strategy favored non-uniform investment in one stock it consistently beat a simple buy hold. However when the algorithm was run on actual stock data the system overwhelmingly stabilized at a population that closely resembled a simple buy hold portfolio, that is, evenly distribute the capital among all stocks.
Selection of relevant features in a fuzzy genetic learning algorithm.
Gonzalez, A; Perez, R
2001-01-01
Genetic algorithms offer a powerful search method for a variety of learning tasks, and there are different approaches in which they have been applied to learning processes. Structural learning algorithm on vague environment (SLAVE) is a genetic learning algorithm that uses the iterative approach to learn fuzzy rules. SLAVE can select the relevant features of the domain, but when working with large databases the search space is too large and the running time can sometimes be excessive. We propose to improve SLAVE by including a feature selection model in which the genetic algorithm works with individuals (representing individual rules) composed of two structures: one structure representing the relevance status of the involved variables in the rule, the other one representing the assignments variable/value. For this general representation, we study two alternatives depending on the information coded in the first structure. When compared with the initial algorithm, this new approach of SLAVE reduces the number of rules, simplifies the structure of the rules and improves the total accuracy. PMID:18244806
Ebtehaj, Isa; Bonakdari, Hossein
2014-01-01
The existence of sediments in wastewater greatly affects the performance of the sewer and wastewater transmission systems. Increased sedimentation in wastewater collection systems causes problems such as reduced transmission capacity and early combined sewer overflow. The article reviews the performance of the genetic algorithm (GA) and imperialist competitive algorithm (ICA) in minimizing the target function (mean square error of observed and predicted Froude number). To study the impact of bed load transport parameters, using four non-dimensional groups, six different models have been presented. Moreover, the roulette wheel selection method is used to select the parents. The ICA with root mean square error (RMSE) = 0.007, mean absolute percentage error (MAPE) = 3.5% show better results than GA (RMSE = 0.007, MAPE = 5.6%) for the selected model. All six models return better results than the GA. Also, the results of these two algorithms were compared with multi-layer perceptron and existing equations. PMID:25429460
A biased random-key genetic algorithm for data clustering.
Festa, P
2013-09-01
Cluster analysis aims at finding subsets (clusters) of a given set of entities, which are homogeneous and/or well separated. Starting from the 1990s, cluster analysis has been applied to several domains with numerous applications. It has emerged as one of the most exciting interdisciplinary fields, having benefited from concepts and theoretical results obtained by different scientific research communities, including genetics, biology, biochemistry, mathematics, and computer science. The last decade has brought several new algorithms, which are able to solve larger sized and real-world instances. We will give an overview of the main types of clustering and criteria for homogeneity or separation. Solution techniques are discussed, with special emphasis on the combinatorial optimization perspective, with the goal of providing conceptual insights and literature references to the broad community of clustering practitioners. A new biased random-key genetic algorithm is also described and compared with several efficient hybrid GRASP algorithms recently proposed to cluster biological data. PMID:23896381
Study of genetic direct search algorithms for function optimization
NASA Technical Reports Server (NTRS)
Zeigler, B. P.
1974-01-01
The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.
Search for overlapped communities by parallel genetic algorithms
Vincenza Carchiolo; Alessandro Longheu; Michele Malgeri; Giuseppe Mangioni
2009-12-07
In the last decade the broad scope of complex networks has led to a rapid progress. In this area a particular interest has the study of community structures. The analysis of this type of structure requires the formalization of the intuitive concept of community and the definition of indices of goodness for the obtained results. A lot of algorithms has been presented to reach this goal. In particular, an interesting problem is the search of overlapped communities and it is field seems very interesting a solution based on the use of genetic algorithms. The approach discusses in this paper is based on a parallel implementation of a genetic algorithm and shows the performance benefits of this solution.
A systematic study of genetic algorithms with genotype editing
Huang, C. F.; Rocha, L. M.
2004-01-01
This paper presents our systematic study on an RNA-editing computational model of Genetic Algorithms (GA). This model is constructed based on several genetic editing characteristics that are gleaned from the RNA editing system as observed in several organisms. We have expanded the traditional Genetic Algorithm with artificial editing mechanisms as proposed by [15]. The incorporation of editing mechanisms provides a means for artificial agents with genetic descriptions to gain greater phenotypic plasticity, which may be environmentally regulated. The systematic study of this RNA-editing model has shed some light into the evolutionary implications of RNA editing and how to select proper RNA editors for design of more robust GAS. The results will also show promising applications to complex real-world problems. We expect that the framework proposed will both facilitate determining the evolutionary role of RNA editing in biology, and advance the current state of research in Evolutionary Computation.
Stochastic search in structural optimization - Genetic algorithms and simulated annealing
NASA Technical Reports Server (NTRS)
Hajela, Prabhat
1993-01-01
An account is given of illustrative applications of genetic algorithms and simulated annealing methods in structural optimization. The advantages of such stochastic search methods over traditional mathematical programming strategies are emphasized; it is noted that these methods offer a significantly higher probability of locating the global optimum in a multimodal design space. Both genetic-search and simulated annealing can be effectively used in problems with a mix of continuous, discrete, and integer design variables.
WICT PROCEEDINGS, DECEMBER 2008 1 Definition Characterisation through Genetic Algorithms
Pace, Gordon J.
WICT PROCEEDINGS, DECEMBER 2008 1 Definition Characterisation through Genetic Algorithms Claudia Borg Dept. of Artificial Intelligence University of Malta claudia.borg@um.edu.mt Mike Rosner Dept. of Artificial Intelligence University of Malta mike.rosner@um.edu.mt Gordon J. Pace Dept. of Computer Science
Neural Networks and Genetic Algorithms for the Attitude Control Problem
Dimitris C. Dracopoulos; Antonia J. Jones
1995-01-01
A general adaptive control method using genetic algorithms and neural networks is proposed and applied to a highly nonlinear problem, the attitude control problem. Examples are given where the method successfully control a rigid body satellite with unknown dynamics, including an example where the satellite is subject to external forces trying to lead it into a chaotic motion.
Convergence Analysis of Canonical Genetic Algorithms GUNTER RUDOLPH
Rudolph, GÃ¼nter
of this de nition is used in this paper. Markov chains o er an appropriate model to analyze GAs and they have reproduc- tion applied to static optimization problems. It is proved by means of ho- mogeneous nite Markov to the schema theorem. Keywords: canonical genetic algorithm, global convergence, Markov chains, schema theorem
Design of electrically loaded wire antennas using genetic algorithms
Alona Boag; E. Michielssen; Raj Mittra
1996-01-01
A novel antenna design procedure based on genetic algorithm (GA) driven optimization is proposed and applied to the synthesis of wire antennas loaded with lumped components. Loading circuit parameters, locations of the loads along the antenna, as well as matching network parameters, are optimized simultaneously. A computational scheme based on the Sherman-Morrison-Woodbury formula for the fast evaluation of the antenna
Planning a collision avoidance model for ship using genetic algorithm
Xiao-Ming Zeng; M. Ito
2001-01-01
Using genetic algorithms to plan the safe path for ship in congested traffic situation, a new gene vector is proposed. The gene vector is composed of the position and speed of our ship, as well as a noise model. The noise model describes the influence on a maneuvering ships system of wind, sea waves and the other natural factors. To
Exploring Very Large State Spaces Using Genetic Algorithms
Rajamani, Sriram K.
Exploring Very Large State Spaces Using Genetic Algorithms Patrice Godefroid 1 and Sarfraz Khurshid this framework in conjunction with VeriSoft, a tool for exploring the state spaces of softÂ ware applications, thereby makÂ ing exhaustive stateÂspace exploration intractable. Several approaches have been proposed
Exploring Very Large State Spaces Using Genetic Algorithms
Khurshid, Sarfraz
Exploring Very Large State Spaces Using Genetic Algorithms Patrice Godefroid1 and Sarfraz Khurshid2 this frame- work in conjunction with VeriSoft, a tool for exploring the state spaces of software applications, thereby mak- ing exhaustive state-space exploration intractable. Several approaches have been proposed
A parallel genetic algorithm for the set partitioning problem
Levine, D.
1996-12-31
This paper describes a parallel genetic algorithm developed for the solution of the set partitioning problem- a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their own subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. We found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, high- quality integer feasible solutions were found for problems with 36, 699 and 43,749 integer variables, respectively. A notable limitation we found was the difficulty solving problems with many constraints.
Genetic algorithms in a distributed computing environment using PVM
Cronje, G.A.; Steeb, W.H. [Rand Afrikaans Univ., Auckland Park (South Africa)] [Rand Afrikaans Univ., Auckland Park (South Africa)
1997-04-01
The Parallel Virtual Machine (PVM) is a software system that enables a collection of heterogeneous computer systems to be used as a coherent and flexible concurrent computation resource. We show that genetic algorithms can be implemented using a Parallel Virtual Machine and C++. Problems with constraints are also discussed.
A fuzzy rule induction method using genetic algorithm
Toshio Tsuchiya; Tatsushi Maeda; Yukihiro Matsubara; Mitsuo Nagamachi
1996-01-01
Kansei engineering expert systems simulate human perception for the evaluation of product design. A procedure of inducing a fuzzy decision tree for the Kansei engineering system is described for the analysis of driving comfort of automobiles. A method is proposed in this study for inducing the tree based on a genetic algorithm. Linguistic fuzzy rules are acquired by tracing the
BP neural network optimization based on an improved genetic algorithm
Bo Yang; Xiao-Hong Su; Ya-Dong Wang
2002-01-01
An improved genetic algorithm based on evolutionarily stable strategy is proposed to optimize the initial weights of backpropagation (BP) network in this paper. The improvement of GA lies in the introducing of a new mutation operator under control of a stable factor, which is found to be a very simple and effective searching operator. The experimental results in BP neural
Multiple Vehicle Routing With Time Windows Using Genetic Algorithms
Louis, Sushil J.
.23% of the optimal on the rest. Keywords: Genetic Algorithms, merge crossover, vehicle routing problem with time win dows 1 INTRODUCTION In vehicle routing problems with time windows (VRPTW), a set of vehicles technique for obtaining optimal or near optimal solutions to the vehicle routing problems with time
ORIGINAL PAPER A self-organizing random immigrants genetic algorithm
Yang, Shengxiang
. In the proposed genetic algorithm, the replacement of an individual can affect other individuals in a chain reaction. The new individuals are preserved in a subpopulation which is defined by the number of individuals created in the current chain reaction. If the values of fitness are similar, as is the case
Truss topology optimization by a modified genetic algorithm
H. Kawamura; H. Ohmori; N. Kito
2002-01-01
This paper describes the use of a stochastic search procedure based on genetic algorithms for developing near-optimal topologies of load-bearing truss structures. Most existing cases these publications express the truss topology as a combination of members. These methods, however, have the disadvantage that the resulting topology may include needless members or those which overlap other members. In addition to these
Genetic algorithms and simulated annealing for robustness analysis
Xiaoyun Zhu; Yun Huang; John Doyle
1997-01-01
Genetic algorithms (GAs) and simulated annealing (SA) have been promoted as useful, general tools for nonlinear optimization. This paper explores their use in robustness analysis with real parameter variations, a known NP hard problem which would appear to be ideally suited to demonstrate the power of GAs and SA. Numerical experiment results show convincingly that they turn out to be
TUNING ROUGH CONTROLLERS BY GENETIC ALGORITHMS Teresa Chiu
Lin, Tsau Young
TUNING ROUGH CONTROLLERS BY GENETIC ALGORITHMS Teresa Chiu Department of Mathematics and Computer of intelligent control systems, called rough logic government, consists of a sequence of transformations rough sets, rough logic, and evolutionary computing. Rough logic government starts with a symbolic model
GENETIC ALGORITHMS AND OPTIMIZING CHEMICAL OXYGEN-IODINE LASERS
David L. Carroll
1996-01-01
This paper presents results from the first known application of the genetic algorithm (GA) technique for optimizing the performance of a laser system (chemical, solid-state, or gaseous). The effects of elitism, single point and uniform crossover, creep mutation, different random number seeds, population size, niching and the number of children per pair of parents on the performance of the GA
Frequency Relaying Based on Genetic Algorithm Using FPGAs
D. V. Coury; M. Oleskovicz; A. C. B. Delbem; E. V. Simoes; T. V. Silva; J. R. de Carvalho; D. Barbosa
2009-01-01
This work presents an accurate and precise genetic algorithm (GA) for frequency estimation of electrical power system (EPS) signals. The problem of estimating the frequency of a distorted electrical signal is modeled as an optimization problem. The advantages of GAs in this approach include the use of coding for a number of solutions which facilitates computer implementation, as well as
A genetic based algorithm for frequency relaying using FPGAs
D. V. Coury; M. Oleskovicz; A. C. B. Delbem; E. V. Simoes; T. V. Silva; J. R. de Carvalho; D. Barbosa
2009-01-01
This work presents an accurate and precise genetic algorithm (GA) for frequency estimation of electrical power system (EPS) signals. The problem of estimating the frequency of a distorted electrical signal is modeled as an optimization problem. The advantages of GAs in this approach include the use of coding for a number of solutions which facilitates computer implementation, as well as
Scheduling Observations of Agile Satellites with Combined Genetic Algorithm
Yuqing Li; Minqiang Xu; Rixin Wang
2007-01-01
This paper describes a combined genetic algorithm for selecting and scheduling tasks of agile earth observing satellites (AEOS). This kind of satellite has three degrees of freedom for acquiring images, and giving opportunities for a more efficient use of the satellite imaging capabilities. But the selection and scheduling of observations becomes significantly difficult, due to the larger search space for
A Genetic Algorithm for Designing Constellations with Low Error Floors
Valenti, Matthew C.
A Genetic Algorithm for Designing Constellations with Low Error Floors Matthew C. Valenti and Raghu-interleaved coded modulation with iterative decoding (BICM-ID) can be minimized for a particular constellation, but also their location in the signal space. Using this approach, new constellations of cardinality 16, 32
A Genetic Algorithm for Designing Constellations with Low Error Floors
Valenti, Matthew C.
A Genetic Algorithm for Designing Constellations with Low Error Floors Matthew C. Valenti1, Raghu C for Mapping Optimization 6 Technique for Constellation Optimization 7 Results 8 Valenti et al. ( Lane is determined by labeling map and choice of constellation Actual error floor can be approximated using EFF bound
Improving Digital Video Commercial Detectors With Genetic Algorithms
J. David Schaffer; Lalitha Agnihotri; Nevenka Dimitrova; Thomas Mcgee; Sylvie Jeannin
2002-01-01
The advent of digital video offers many opportunities to add features that enhance the viewing experience. One much-discussed feature is the possibility that commercials might be automatically detected in the video stream. We report on initial experiments with a class of commercial detection algorithms and show how their performance can be enhanced by applying genetic search to the optimization of
Computation of Molecular Electronic Structure by Genetic Algorithm
Rahul Sharma; Rajendra Saha; Subhajit Nandy; Shankar Prasad Bhattacharyya; Pinaki Chaudhury
2009-01-01
Several strategies based on Genetic Algorithms have been explored to locate the ground state energy and structure of atoms and molecules. A variational recipe in a finite basis has been invoked in one of the strategies leading to a matrix eigenvalue problem that has been solved by GA with simultaneous optimization of the basis. In the second approach, the molecular
Simple genetic algorithm parameter selection for protein structure prediction
L. D. Merkle; G. B. Lamont; R. Pachter
1995-01-01
Selection of run-time parameters is a critical step in the application of genetic algorithms (GAs). Numerous investigations have discussed parameter set selection, both theoretically and empirically. Theoretical work has focused on the choice of population size, while empirical studies cover a wide range of GA parameters. Theory suggests population sizes which increase exponentially with string length. The available experimental data
Mitchell, Melanie
straightforward. The GA developed here uses the texture of the prostate gland and its shape derived from manual. KEYWORDS Genetic algorithm, Medical image processing, Level set method, Segmentation, Prostate cancer. #12 and variability in the definition of tumor margins can result in suboptimal treatment of some patients
Dynamic Populations in Genetic Algorithms Zhanshan (Sam) Ma
Krings, Axel W.
Dynamic Populations in Genetic Algorithms Zhanshan (Sam) Ma University of Idaho Computer Science. Moscow, ID 83843, USA krings@cs.uidaho.edu ABSTRACT Biological populations are dynamic in both space in nature. In this paper, we explore the effects of dynamic (fluctuating) populations on the performance
EVOLVING HEXAPOD GAITS USING A CYCLIC GENETIC ALGORITHM
Parker, Gary B.
as possible. In addition, learning reduces the human engineering required to develop the intricaciesEVOLVING HEXAPOD GAITS USING A CYCLIC GENETIC ALGORITHM GARY B. PARKER, DAVID W. BRAUN, AND INGO with the differences between and within robots would greatly reduce engineering calculations and increase robot
Identification of Induction Machine Electrical Parameters Using Genetic Algorithms Optimization
Konstantinos Kampisios; Pericle Zanchetta; Chris Gerada; Andrew Trentin
2008-01-01
This paper introduces a new heuristic approach for identifying induction motor equivalent circuit parameters based on experimental transient measurements from a vector controlled induction motor (I.M.) drive and using an off line genetic algorithm (GA) routine with a linear machine model. The evaluation of the electrical motor parameters is achieved by minimizing the error between experimental responses (speed or current)
USING GENETIC ALGORITHMS TO DESIGN ENVIRONMENTALLY FRIENDLY PROCESSES
Genetic algorithm calculations are applied to the design of chemical processes to achieve improvements in environmental and economic performance. By finding the set of Pareto (i.e., non-dominated) solutions one can see how different objectives, such as environmental and economic ...
Machine Translation Method Using Inductive Learning with Genetic Algorithms
Machine Translation Method Using Inductive Learning with Genetic Algorithms Hiroshi Echizen out on machine translation. The rule-based machine translation (John and Harold, 1992) could not deal, example-based machine translations (Sato and Nagao, 1990; Akama and Ichikawa, 1979; Stanfill and Waltz
HARDWARE IMPLEMENTATION OF A PARALLELIZED GENETIC ALGORITHM FOR
Ricks, Kenneth G.
HARDWARE IMPLEMENTATION OF A PARALLELIZED GENETIC ALGORITHM FOR TASK SCHEDULING by VIJAY TIRUMALAI in Electrical Engineering in the Department of Electrical and Computer Engineering in the Graduate School of the requirements for the degree of Master of Science in Electrical Engineering. Accepted on behalf of the Faculty
Classification of ECG waveforms by using genetic algorithms
Tamer Olmez; Ziimray Dokur; Ertugrul Yazgan
1997-01-01
In this study, a restricted coulomb energy network trained by genetic algorithms (GARCE) is proposed for ECG (electrocardiogram) waveform detection. After the R peak of the QRS complex is detected, a window containing an ECG period is formed around the R peak. The significant frequency components of the discrete Fourier transform of the signal in this window are used to
Fuzzy Control of HVAC Systems Optimized by Genetic Algorithms
Rafael Alcalá; José Manuel Benítez; Jorge Casillas; Oscar Cordón; Raúl Pérez
2003-01-01
Abstract. This paper presents the use of genetic algorithms to develop smartly tuned fuzzy logic controllers dedicated to the control of heating, ventilating and air conditioning systems concerning energy performance and indoor comfort requirements. This problem has some specific restrictions that make it very particular and complex because of the large time requirements existing due to the need of considering
Genetic Algorithms with Self-Organizing Behaviour in Dynamic Environments
Yang, Shengxiang
change for several factors, like faults, machine degradation, environmental or climatic modifications. In recent years, researchers from the genetic algorithm (GA) commu- nity have developed several approaches of the problem are not fixed [24]. When changes occur in the problem, the so- lution given by the optimization
A Genetic Algorithm for Railway Scheduling , F. Barber2
Barber, Federico
is tested using real instances obtained from the Spanish Manager of Railway Infrastructure (ADIF by the Spanish Manager of Railway Infrastructure (ADIF). In addition, the heuristic technique described10 A Genetic Algorithm for Railway Scheduling Problems P. Tormos1 , A. Lova1 , F. Barber2 , L
Emotional Image and Musical Information Retrieval With Interactive Genetic Algorithm
Cho, Sung-Bae
, MEMBER, IEEE Invited Paper Several techniques in artificial intelligence have shown a great potential this shortcoming, we present a promising technique called interactive genetic algorithm (IGA), which performs opti approach allows us to design and search digital media not only explicitly ex- pressed, but also abstract
A Genetic Algorithm for Simultaneous Localization and Mapping Tom Duckett
Duckett, Tom
://www.aass.oru.se Abstract--- This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobileA Genetic Algorithm for Simultaneous Localization and Mapping Tom Duckett Centre for Applied data must be used for both mapping and localization. We can separate two major sources of uncertainty
Design optimization of electrical machines using genetic algorithms
G. F. Uler; O. A. Mohammed; Chang-Seop Koh
1995-01-01
The application of genetic algorithms (GAs) to the design optimization of electromagnetic devices is presented in detail. The method is demonstrated on a magnetizer by optimizing its pole face to obtain the desired magnetic flux density distribution. The shape of the pole face is constructed from the control points by means of uniform nonrational b-splines
Vibrational genetic algorithm as a new concept in airfoil design
Abdurrahman Hacio?lu; ?brahim Özkol
2002-01-01
We introduce the Vibration concept for real coded Genetic Algorithm and its implementation to inverse airfoil design, which decreases the number of CFD calculations. This concept assures efficient diversity in the population and consequently gives faster solution. We used the Vibration concept as vibrational mutation and vibrational crossover. For the mutational manner, a sinusoidal wave with random amplitude is introduced
Search for native conformations of organic molecules by genetic algorithms
Susanne Beiersdörfer; Jürgen Hesser; Jens Schmitt; Reinhard Männer; Andreas Schulz; Jürgen Wolfrum
1997-01-01
Recent results concerning the efficiency of genetic algorithms for structure prediction of organic molecules are presented. The main focus of this paper is on the efficiency of different problem specific crossover operators and repair heuristics. A fluorescent labeled nucleoside and an oligopeptide are used as application examples. The molecule structure adapted crossovers and repair heuristics turned out to increase the
NASA Astrophysics Data System (ADS)
Windarto, Indratno, S. W.; Nuraini, N.; Soewono, E.
2014-02-01
Genetic algorithm is an optimization method based on the principles of genetics and natural selection in life organisms. The algorithm begins by defining the optimization variables, defining the cost function (in a minimization problem) or the fitness function (in a maximization problem) and selecting genetic algorithm parameters. The main procedures in genetic algorithm are generating initial population, selecting some chromosomes (individual) as parent's individual, mating, and mutation. In this paper, binary and continuous genetic algorithms were implemented to estimate growth rate and carrying capacity parameter from poultry data cited from literature. For simplicity, all genetic algorithm parameters (selection rate and mutation rate) are set to be constant along implementation of the algorithm. It was found that by selecting suitable mutation rate, both algorithms can estimate these parameters well. Suitable range for mutation rate in continuous genetic algorithm is wider than the binary one.
Sampling protein conformations using segment libraries and a genetic algorithm
NASA Astrophysics Data System (ADS)
Gunn, John R.
1997-03-01
We present a new simulation algorithm for minimizing empirical contact potentials for a simplified model of protein structure. The model consists of backbone atoms only (including C?) with the ? and ? dihedral angles as the only degrees of freedom. In addition, ? and ? are restricted to a finite set of 532 discrete pairs of values, and the secondary structural elements are held fixed in ideal geometries. The potential function consists of a look-up table based on discretized inter-residue atomic distances. The minimization consists of two principal elements: the use of preselected lists of trial moves and the use of a genetic algorithm. The trial moves consist of substitutions of one or two complete loop regions, and the lists are in turn built up using preselected lists of randomly-generated three-residue segments. The genetic algorithm consists of mutation steps (namely, the loop replacements), as well as a hybridization step in which new structures are created by combining parts of two "parents'' and a selection step in which hybrid structures are introduced into the population. These methods are combined into a Monte Carlo simulated annealing algorithm which has the overall structure of a random walk on a restricted set of preselected conformations. The algorithm is tested using two types of simple model potential. The first uses global information derived from the radius of gyration and the rms deviation to drive the folding, whereas the second is based exclusively on distance-geometry constraints. The hierarchical algorithm significantly outperforms conventional Monte Carlo simulation for a set of test proteins in both cases, with the greatest advantage being for the largest molecule having 193 residues. When tested on a realistic potential function, the method consistently generates structures ranked lower than the crystal structure. The results also show that the improved efficiency of the hierarchical algorithm exceeds that which would be anticipated from tests on either of the two main elements used independently.
Design Synthesis of Microelectromechanical Systems Using Genetic Algorithms with Component-Based
Agogino, Alice M.
Design Synthesis of Microelectromechanical Systems Using Genetic Algorithms with Component-Based Genotype Representation Ying Zhang Systems Engineering Program University of California Berkeley, CA 94720 design synthesis system based on a multi-objective genetic algorithm (MOGA) has been developed
Julstrom, Bryant A.
-647-6/ 94/ 0003 $3.50 Given a set of points in the Euclidean plane, a rectilin- ear Steiner tree on those Steiner problem, genetic algorithms, seeding the pop- ulation. Abstract|A hybrid genetic algorithm
OPTIMIZATION OF TURBOMACHINERY AIRFOILS WITH A GENETIC/SEQUENTIAL QUADRATIC PROGRAMMING ALGORITHM
Dennis, Brian
OPTIMIZATION OF TURBOMACHINERY AIRFOILS WITH A GENETIC/SEQUENTIAL QUADRATIC PROGRAMMING ALGORITHM words: shape optimization, aerodynamic design, turbomachinery, aerodynamics, genetic algorithms-magneto- gasdynamic effects. In the case of a turbomachinery aerodynamics, sources of entropy production other than
COMPUTING AN ADAPTIVE MESH IN FLUID PROBLEMS USING NEURAL NETWORK AND GENETIC ALGORITHM Relaxation (GAAR) is presented in this paper to construct learning system that can find an Adaptive Mesh; genetic algorithm; adaptive mesh; finite element method #12;
Longest jobs first algorithm in solving job shop scheduling using adaptive genetic algorithm (GA)
NASA Astrophysics Data System (ADS)
Alizadeh Sahzabi, Vahid; Karimi, Iman; Alizadeh Sahzabi, Navid; Mamaani Barnaghi, Peiman
2012-01-01
In this paper, genetic algorithm was used to solve job shop scheduling problems. One example discussed in JSSP (Job Shop Scheduling Problem) and I described how we can solve such these problems by genetic algorithm. The goal in JSSP is to gain the shortest process time. Furthermore I proposed a method to obtain best performance on performing all jobs in shortest time. The method mainly, is according to Genetic algorithm (GA) and crossing over between parents always follows the rule which the longest process is at the first in the job queue. In the other word chromosomes is suggested to sorts based on the longest processes to shortest i.e. "longest job first" says firstly look which machine contains most processing time during its performing all its jobs and that is the bottleneck. Secondly, start sort those jobs which are belonging to that specific machine descending. Based on the achieved results," longest jobs first" is the optimized status in job shop scheduling problems. In our results the accuracy would grow up to 94.7% for total processing time and the method improved 4% the accuracy of performing all jobs in the presented example.
Longest jobs first algorithm in solving job shop scheduling using adaptive genetic algorithm (GA)
NASA Astrophysics Data System (ADS)
Alizadeh Sahzabi, Vahid; Karimi, Iman; Alizadeh Sahzabi, Navid; Mamaani Barnaghi, Peiman
2011-12-01
In this paper, genetic algorithm was used to solve job shop scheduling problems. One example discussed in JSSP (Job Shop Scheduling Problem) and I described how we can solve such these problems by genetic algorithm. The goal in JSSP is to gain the shortest process time. Furthermore I proposed a method to obtain best performance on performing all jobs in shortest time. The method mainly, is according to Genetic algorithm (GA) and crossing over between parents always follows the rule which the longest process is at the first in the job queue. In the other word chromosomes is suggested to sorts based on the longest processes to shortest i.e. "longest job first" says firstly look which machine contains most processing time during its performing all its jobs and that is the bottleneck. Secondly, start sort those jobs which are belonging to that specific machine descending. Based on the achieved results," longest jobs first" is the optimized status in job shop scheduling problems. In our results the accuracy would grow up to 94.7% for total processing time and the method improved 4% the accuracy of performing all jobs in the presented example.
Tolbert, Leon M.
-bridge converters in series as shown in Fig. 1 for a 7-level inverter. Each converter generates a square wave; therefore they have reduced harmonics compared to a square wave inverter. To reduce the harmonics furtherHarmonic Optimization of Multilevel Converters Using Genetic Algorithms Abstract-- In this paper
Optimal Design of Geodetic Network Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Vajedian, Sanaz; Bagheri, Hosein
2010-05-01
A geodetic network is a network which is measured exactly by techniques of terrestrial surveying based on measurement of angles and distances and can control stability of dams, towers and their around lands and can monitor deformation of surfaces. The main goals of an optimal geodetic network design process include finding proper location of control station (First order Design) as well as proper weight of observations (second order observation) in a way that satisfy all the criteria considered for quality of the network with itself is evaluated by the network's accuracy, reliability (internal and external), sensitivity and cost. The first-order design problem, can be dealt with as a numeric optimization problem. In this designing finding unknown coordinates of network stations is an important issue. For finding these unknown values, network geodetic observations that are angle and distance measurements must be entered in an adjustment method. In this regard, using inverse problem algorithms is needed. Inverse problem algorithms are methods to find optimal solutions for given problems and include classical and evolutionary computations. The classical approaches are analytical methods and are useful in finding the optimum solution of a continuous and differentiable function. Least squares (LS) method is one of the classical techniques that derive estimates for stochastic variables and their distribution parameters from observed samples. The evolutionary algorithms are adaptive procedures of optimization and search that find solutions to problems inspired by the mechanisms of natural evolution. These methods generate new points in the search space by applying operators to current points and statistically moving toward more optimal places in the search space. Genetic algorithm (GA) is an evolutionary algorithm considered in this paper. This algorithm starts with definition of initial population, and then the operators of selection, replication and variation are applied to obtain the solution of problem. In this research, the first step is to design a geodetic network and do the observations of the distances and angles between network's stations. The second step is to use the optimization algorithms to estimate unknown values of stations' coordinates, with regards to calculation equations of length and angle. The result indicates that The Genetic algorithms have been successfully employed for solving inverse problems in engineering disciplines. And it seems that many complex problems can be better solved using genetic algorithms than those of using conventional methods.
Distributed Query Plan Generation Using Multiobjective Genetic Algorithm
Panicker, Shina; Vijay Kumar, T. V.
2014-01-01
A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability. PMID:24963513
A novel pipeline based FPGA implementation of a genetic algorithm
NASA Astrophysics Data System (ADS)
Thirer, Nonel
2014-05-01
To solve problems when an analytical solution is not available, more and more bio-inspired computation techniques have been applied in the last years. Thus, an efficient algorithm is the Genetic Algorithm (GA), which imitates the biological evolution process, finding the solution by the mechanism of "natural selection", where the strong has higher chances to survive. A genetic algorithm is an iterative procedure which operates on a population of individuals called "chromosomes" or "possible solutions" (usually represented by a binary code). GA performs several processes with the population individuals to produce a new population, like in the biological evolution. To provide a high speed solution, pipelined based FPGA hardware implementations are used, with a nstages pipeline for a n-phases genetic algorithm. The FPGA pipeline implementations are constraints by the different execution time of each stage and by the FPGA chip resources. To minimize these difficulties, we propose a bio-inspired technique to modify the crossover step by using non identical twins. Thus two of the chosen chromosomes (parents) will build up two new chromosomes (children) not only one as in classical GA. We analyze the contribution of this method to reduce the execution time in the asynchronous and synchronous pipelines and also the possibility to a cheaper FPGA implementation, by using smaller populations. The full hardware architecture for a FPGA implementation to our target ALTERA development card is presented and analyzed.
Implementing Genetic Algorithms on Arduino Micro-Controllers
Alves, Nuno
2010-01-01
Since their conception in 1975, Genetic Algorithms have been an extremely popular approach to find exact or approximate solutions to optimization and search problems. Over the last years there has been an enhanced interest in the field with related techniques, such as grammatical evolution, being developed. Unfortunately, work on developing genetic optimizations for low-end embedded architectures hasn't embraced the same enthusiasm. This short paper tackles that situation by demonstrating how genetic algorithms can be implemented in Arduino Duemilanove, a 16 MHz open-source micro-controller, with limited computation power and storage resources. As part of this short paper, the libraries used in this implementation are released into the public domain under a GPL license.
Novel approach to aircraft silhouette recognition using genetic algorithms
NASA Astrophysics Data System (ADS)
Myler, Harley R.; Weeks, Arthur R.; Hooper-Giles, Jill L.
1992-07-01
An approach to aircraft silhouette recognition using a genetic algorithm for pattern analysis and search tasks and a bimorph shape classifier is presented. The bimorph classifier produces an assortment of shapes derived from a medial axis transform language (MAT) by establishing a set of genes, a chromosome, that portrays the genetic makeup of each shape produced. Each gene represents a unique shape feature for that object and each chromosome a unique object. The chromosomes are used to generate the shapes embodying the classification space. The genetic algorithm then performs a search on the space until the exemplar shape is found that matches an unknown aircraft. The outcome of the search is a chromosome that constitutes the aircraft shape characteristics. The chromosome may then be compared to that of known aircraft to determine the type of aircraft in question. The procedures and results of utilizing this classification system on various aircraft silhouettes are presented.
Material Design via Genetic Algorithms for Semiconductor Alloys and Superlattices
NASA Astrophysics Data System (ADS)
Kim, Kwiseon; Graf, Peter A.; Jones, Wesley B.
2005-06-01
We present an efficient and accurate method for designing materials for electronic applications. Our approach is to search an atomic configuration space by repeatedly applying a forward solver, guiding the search toward the optimal configuration using an evolutionary algorithm. We employ a hierarchical parallelism for the combined forward solver and the genetic algorithm. This enables the optimization process to run on many more processors than would otherwise be possible. We have optimized AlGaAs alloys for maximum bandgap and minimum bandgap for several given compositions. When combined with an efficient forward solver, this approach can be generalized to a wide range of applications in material design.
Multiple Magnetic Dipole Modeling Coupled with a Genetic Algorithm
NASA Astrophysics Data System (ADS)
Lientschnig, G.
2012-05-01
Magnetic field measurements of scientific spacecraft can be modelled successfully with the multiple magnetic dipole method. The existing GANEW software [1] uses a modified Gauss-Newton algorithm to find good magnetic dipole models. However, this deterministic approach relies on suitable guesses of the initial parameters which require a lot of expertise and time-consuming interaction of the user. Here, the use of probabilistic methods employing genetic algorithms is put forward. Stochastic methods like these are well- suited for providing good initial starting points for GANEW. Furthermore a computer software is reported upon that was successfully tested and used for a Cluster II satellite.
A Dedicated Genetic Algorithm for Localization of Moving Magnetic Objects.
Alimi, Roger; Weiss, Eyal; Ram-Cohen, Tsuriel; Geron, Nir; Yogev, Idan
2015-01-01
A dedicated Genetic Algorithm (GA) has been developed to localize the trajectory of ferromagnetic moving objects within a bounded perimeter. Localization of moving ferromagnetic objects is an important tool because it can be employed in situations when the object is obscured. This work is innovative for two main reasons: first, the GA has been tuned to provide an accurate and fast solution to the inverse magnetic field equations problem. Second, the algorithm has been successfully tested using real-life experimental data. Very accurate trajectory localization estimations were obtained over a wide range of scenarios. PMID:26393598
Genetic algorithms and their use in Geophysical Problems
Parker, Paul B.
1999-04-01
Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or ''fittest'' models from a ''population'' and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show that certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Optimal efficiency is usually achieved with smaller (< 50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (> 2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free parameters and with computationally expensive objective function calculations. More sophisticated techniques are presented for special problems. Niching and island model algorithms are introduced as methods to find multiple, distinct solutions to the nonunique problems that are typically seen in geophysics. Finally, hybrid algorithms are investigated as a way to improve the efficiency of the standard genetic algorithm.
An image restoration method based on genetic algorithm BP neural network
Qian Xiao; Weiren Shi; Xiaodong Xian; Xinxiang Yan
2008-01-01
A new image restoration method was presented and investigated based on genetic algorithm BP neural network. The method combined the characteristics of global optimization of genetic algorithm with local optimization of BP neural network. The mapping relationship between degenerated image and clear image was established by training genetic algorithm BP neural network. Experimental results show that this method has a
A Hybrid Genetic Algorithm for Shape Optimization of the Truss with Discrete Variables
Guo-fu Sun; Shu-cai Li; Chun-mei Zheng; Bo Zhang; Sheng-bo Hou
2009-01-01
A hybrid genetic algorithm is proposed to deal with the shape optimization of the truss based on relative difference quotient method and improved genetic algorithm. The advantages of the genetic algorithm in global optimization and the relative difference quotient method in local searching ability are both included in the HGA method. Numerical example of a 37-bar truss was given to
A Genetic Algorithm for Searching Shortest Lattice Vector of SVP Challenge
International Association for Cryptologic Research (IACR)
A Genetic Algorithm for Searching Shortest Lattice Vector of SVP Challenge Dan Ding1 , Guizhen Zhu2, China P. R. Abstract. In this paper, we propose a genetic algorithm for solving the shortest vector pruning. The experimental results show that the genetic algorithm runs rather good on the SVP challenge
Using Genetic Algorithms to Reorganize Superpeer Structure in Peer to Peer Networks
Arpinar, I. Budak
Using Genetic Algorithms to Reorganize Superpeer Structure in Peer to Peer Networks by Jaymin B a genetic algorithm for optimizing the superpeer structure of semantic peer to peer networks. Peer to peer the time in which they are answered. It will be shown that the genetic algorithm (GA) dramatically improves
Convex Onion Peeling Genetic Algorithm: An Efficient Solution to Map Labeling of Point-Feature
Bae, Wan
Convex Onion Peeling Genetic Algorithm: An Efficient Solution to Map Labeling of Point-Feature Wan-feature and develop a new genetic algorithm to solve this problem. We adopt a data struc- ture called convex onion peeling and utilize it in our pro- posed Convex Onion Peeling Genetic Algorithm (COPGA) to efficiently
Fast correspondence of unrectified stereo images using genetic algorithm and spline representation
Beau Tippetts; Dah Jye Lee; James Archibald
2010-01-01
Spline representations have been successfully used with a genetic algorithm to determine a disparity map for stereo image pairs. This paper describes work to modify the genetic spline algorithm to use a version of the genetic algorithm with small populations and few generations, previously referred to as \\
Contextual Genetic Algorithms: Evolving Developmental Rules
Rocha, Luis
by the biological system of RNA editing found in a variety of organisms. In biological systems, RNA editing on fuzzy set and evidence theories. 1. Introduction The essence of GA's lies on the separation computation and biological genetic systems, lies precisely on the connection between descriptions
RCQ-GA: RDF Chain Query Optimization Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Hogenboom, Alexander; Milea, Viorel; Frasincar, Flavius; Kaymak, Uzay
The application of Semantic Web technologies in an Electronic Commerce environment implies a need for good support tools. Fast query engines are needed for efficient querying of large amounts of data, usually represented using RDF. We focus on optimizing a special class of SPARQL queries, the so-called RDF chain queries. For this purpose, we devise a genetic algorithm called RCQ-GA that determines the order in which joins need to be performed for an efficient evaluation of RDF chain queries. The approach is benchmarked against a two-phase optimization algorithm, previously proposed in literature. The more complex a query is, the more RCQ-GA outperforms the benchmark in solution quality, execution time needed, and consistency of solution quality. When the algorithms are constrained by a time limit, the overall performance of RCQ-GA compared to the benchmark further improves.
An Airborne Conflict Resolution Approach Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Mondoloni, Stephane; Conway, Sheila
2001-01-01
An airborne conflict resolution approach is presented that is capable of providing flight plans forecast to be conflict-free with both area and traffic hazards. This approach is capable of meeting constraints on the flight plan such as required times of arrival (RTA) at a fix. The conflict resolution algorithm is based upon a genetic algorithm, and can thus seek conflict-free flight plans meeting broader flight planning objectives such as minimum time, fuel or total cost. The method has been applied to conflicts occurring 6 to 25 minutes in the future in climb, cruise and descent phases of flight. The conflict resolution approach separates the detection, trajectory generation and flight rules function from the resolution algorithm. The method is capable of supporting pilot-constructed resolutions, cooperative and non-cooperative maneuvers, and also providing conflict resolution on trajectories forecast by an onboard FMC.
Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization
NASA Technical Reports Server (NTRS)
Lohn, Jason D.; Kraus, William F.; Haith, Gary L.; Clancy, Daniel (Technical Monitor)
2002-01-01
We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these properties, setting up the additional population is trivial making implementation no more difficult than using a standard GA. Empirical results using a suite of two-objective test functions indicate that this CGA performs well at finding solutions on convex, nonconvex, discrete, and deceptive Pareto-optimal fronts, while giving respectable results on a nonuniform optimization. On a multimodal Pareto front, the CGA finds a solution that dominates solutions produced by eight other algorithms, yet the CGA has poor coverage across the Pareto front.
Threshold matrix for digital halftoning by genetic algorithm optimization
NASA Astrophysics Data System (ADS)
Alander, Jarmo T.; Mantere, Timo J.; Pyylampi, Tero
1998-10-01
Digital halftoning is used both in low and high resolution high quality printing technologies. Our method is designed to be mainly used for low resolution ink jet marking machines to produce both gray tone and color images. The main problem with digital halftoning is pink noise caused by the human eye's visual transfer function. To compensate for this the random dot patterns used are optimized to contain more blue than pink noise. Several such dot pattern generator threshold matrices have been created automatically by using genetic algorithm optimization, a non-deterministic global optimization method imitating natural evolution and genetics. A hybrid of genetic algorithm with a search method based on local backtracking was developed together with several fitness functions evaluating dot patterns for rectangular grids. By modifying the fitness function, a family of dot generators results, each with its particular statistical features. Several versions of genetic algorithms, backtracking and fitness functions were tested to find a reasonable combination. The generated threshold matrices have been tested by simulating a set of test images using the Khoros image processing system. Even though the work was focused on developing low resolution marking technology, the resulting family of dot generators can be applied also in other halftoning application areas including high resolution printing technology.
Scope of Gradient and Genetic Algorithms in Multivariable Function Optimization
NASA Technical Reports Server (NTRS)
Shaykhian, Gholam Ali; Sen, S. K.
2007-01-01
Global optimization of a multivariable function - constrained by bounds specified on each variable and also unconstrained - is an important problem with several real world applications. Deterministic methods such as the gradient algorithms as well as the randomized methods such as the genetic algorithms may be employed to solve these problems. In fact, there are optimization problems where a genetic algorithm/an evolutionary approach is preferable at least from the quality (accuracy) of the results point of view. From cost (complexity) point of view, both gradient and genetic approaches are usually polynomial-time; there are no serious differences in this regard, i.e., the computational complexity point of view. However, for certain types of problems, such as those with unacceptably erroneous numerical partial derivatives and those with physically amplified analytical partial derivatives whose numerical evaluation involves undesirable errors and/or is messy, a genetic (stochastic) approach should be a better choice. We have presented here the pros and cons of both the approaches so that the concerned reader/user can decide which approach is most suited for the problem at hand. Also for the function which is known in a tabular form, instead of an analytical form, as is often the case in an experimental environment, we attempt to provide an insight into the approaches focusing our attention toward accuracy. Such an insight will help one to decide which method, out of several available methods, should be employed to obtain the best (least error) output. *
Genetic algorithms for industrial ethernet network design
Nicolas Krommenacker; Eric Rondeau; Thieny Divoux
2002-01-01
The Ethernet network is increasingly being used for industrial communications which are strongly time-constrained. This kind of network is intrinsically nondeterministic and does not guarantee that communication end-to-end delays will be bounded. Nevertheless, from the observed traffic between industrial devices, some network topologies improving the availability and temporal performances can be designed. This paper describes the use of a genetic
Using genetic algorithm for Persian grammar induction
Mohsen Arabsorkhi; Hesham Faili; Mansoor Zolghadri Jahroumi
2009-01-01
Most of efficient computational approaches in NLP tasks are supervised methods which need annotated corpora. But the lack of supervised data in Persian encourages researchers to increase their interests and efforts on unsupervised and semi-supervised approaches. This paper presents a novel semi-supervised approach which called Genetic-based inside-outside (GIO), for Persian grammar inference for inducing a grammar model in a PCFG
NASA Astrophysics Data System (ADS)
Djuriši?, Aleksandra B.; Elazar, Jovan M.; Raki?, Aleksandar D.
1997-02-01
In this paper we propose parameter space size adjustment in genetic algorithms. The performance of these algorithms is compared with classical genetic algorithms in terms of solution quality, i.e. objective function or fitness final value and accuracy. The algorithms were tested on computer generated synthetic data closely resembling the optical constants of a metal. The algorithm with the best performance was used to determine the parameters of the model for optical constants of aluminum, gold and platinum.
Locomotive assignment problem with train precedence using genetic algorithm
NASA Astrophysics Data System (ADS)
Noori, Siamak; Ghannadpour, Seyed Farid
2012-07-01
This paper aims to study the locomotive assignment problem which is very important for railway companies, in view of high cost of operating locomotives. This problem is to determine the minimum cost assignment of homogeneous locomotives located in some central depots to a set of pre-scheduled trains in order to provide sufficient power to pull the trains from their origins to their destinations. These trains have different degrees of priority for servicing, and the high class of trains should be serviced earlier than others. This problem is modeled using vehicle routing and scheduling problem where trains representing the customers are supposed to be serviced in pre-specified hard/soft fuzzy time windows. A two-phase approach is used which, in the first phase, the multi-depot locomotive assignment is converted to a set of single depot problems, and after that, each single depot problem is solved heuristically by a hybrid genetic algorithm. In the genetic algorithm, various heuristics and efficient operators are used in the evolutionary search. The suggested algorithm is applied to solve the medium sized numerical example to check capabilities of the model and algorithm. Moreover, some of the results are compared with those solutions produced by branch-and-bound technique to determine validity and quality of the model. Results show that suggested approach is rather effective in respect of quality and time.
Adaptive process control using fuzzy logic and genetic algorithms
NASA Technical Reports Server (NTRS)
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Naturally selecting solutions: the use of genetic algorithms in bioinformatics.
Manning, Timmy; Sleator, Roy D; Walsh, Paul
2013-01-01
For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems. PMID:23222169
Optimum Actuator Selection with a Genetic Algorithm for Aircraft Control
NASA Technical Reports Server (NTRS)
Rogers, James L.
2004-01-01
The placement of actuators on a wing determines the control effectiveness of the airplane. One approach to placement maximizes the moments about the pitch, roll, and yaw axes, while minimizing the coupling. For example, the desired actuators produce a pure roll moment without at the same time causing much pitch or yaw. For a typical wing, there is a large set of candidate locations for placing actuators, resulting in a substantially larger number of combinations to examine in order to find an optimum placement satisfying the mission requirements and mission constraints. A genetic algorithm has been developed for finding the best placement for four actuators to produce an uncoupled pitch moment. The genetic algorithm has been extended to find the minimum number of actuators required to provide uncoupled pitch, roll, and yaw control. A simplified, untapered, unswept wing is the model for each application.
An edge detection technique using genetic algorithm-based optimization
Suchendra M. Bhandarkar; Yiqing Zhang; Walter D. Potter
1994-01-01
In this paper we present a genetic algorithm-based optimization technique for edge detection. The problem of edge detection is formulated as one of choosing a minimum cost edge configuration. The edge configurations are viewed as two-dimensional chromosomes with fitness values inversely proportional to their costs. The design of the crossover and the mutation operators in the context of the two-dimensional
DESIGN OF PEBBLE-BED REACTORS USING GENETIC ALGORITHMS
Hans D. Gougar; Abderrafi M. Ougouag; William K. Terry; Kostadin N. Ivanov
We present a conceptual design approach for high-temperature gas-cooled reactors using recirculating pebble-bed cores. The --design approach uses PEBBED, a reactor physics code specifically designed for equilibrium cycle analysis of pebble-bed reactors (PBRs), in conjunction with a genetic algorithm to obtain a core that maximizes a fitness value that is a function of user-specified parameters. The uniqueness of the asymptotic
RCQ-GA: RDF Chain Query Optimization Using Genetic Algorithms
Alexander Hogenboom; Viorel Milea; Flavius Frasincar; Uzay Kaymak
2009-01-01
The application of Semantic Web technologies in an Electronic Commerce environment implies a need for good support tools.\\u000a Fast query engines are needed for efficient querying of large amounts of data, usually represented using RDF. We focus on\\u000a optimizing a special class of SPARQL queries, the so-called RDF chain queries. For this purpose, we devise a genetic algorithm\\u000a called RCQ-GA
A quantum genetic algorithm with quantum crossover and mutation operations
NASA Astrophysics Data System (ADS)
SaiToh, Akira; Rahimi, Robabeh; Nakahara, Mikio
2013-11-01
In the context of evolutionary quantum computing in the literal meaning, a quantum crossover operation has not been introduced so far. Here, we introduce a novel quantum genetic algorithm that has a quantum crossover procedure performing crossovers among all chromosomes in parallel for each generation. A complexity analysis shows that a quadratic speedup is achieved over its classical counterpart in the dominant factor of the run time to handle each generation.
First flights of genetic-algorithm Kitty Hawk
Goldberg, D.E.
1994-12-31
The design of complex systems requires an effective methodology of invention. This paper considers the methodology of the Wright brothers in inventing the powered airplane and suggests how successes in the design of genetic algorithms have come at the hands of a Wright-brothers-like approach. Recent reliable subquadratic results in solving hard problems with nontraditional GAs and predictions of the limits of simple GAs are presented as two accomplishments achieved in this manner.
Investigation of range extension with a genetic algorithm
Austin, A. S., LLNL
1998-03-04
Range optimization is one of the tasks associated with the development of cost- effective, stand-off, air-to-surface munitions systems. The search for the optimal input parameters that will result in the maximum achievable range often employ conventional Monte Carlo techniques. Monte Carlo approaches can be time-consuming, costly, and insensitive to mutually dependent parameters and epistatic parameter effects. An alternative search and optimization technique is available in genetic algorithms. In the experiments discussed in this report, a simplified platform motion simulator was the fitness function for a genetic algorithm. The parameters to be optimized were the inputs to this motion generator and the simulator`s output (terminal range) was the fitness measure. The parameters of interest were initial launch altitude, initial launch speed, wing angle-of-attack, and engine ignition time. The parameter values the GA produced were validated by Monte Carlo investigations employing a full-scale six-degree-of-freedom (6 DOF) simulation. The best results produced by Monte Carlo processes using values based on the GA derived parameters were within - 1% of the ranges generated by the simplified model using the evolved parameter values. This report has five sections. Section 2 discusses the motivation for the range extension investigation and reviews the surrogate flight model developed as a fitness function for the genetic algorithm tool. Section 3 details the representation and implementation of the task within the genetic algorithm framework. Section 4 discusses the results. Section 5 concludes the report with a summary and suggestions for further research.
Decision support for irrigation project planning using a genetic algorithm
Sheng-Feng Kuo; Gary P. Merkley; Chen-Wuing Liu
2000-01-01
This work presents a model based on on-farm irrigation scheduling and the simple genetic algorithm optimization (GA) method for decision support in irrigation project planning. The proposed model is applied to an irrigation project located in Delta, Utah of 394.6ha in area, for optimizing economic profits, simulating the water demand, crop yields, and estimating the related crop area percentages with
Genetic Algorithms with Lego Mindstorms and Matlab Frank Klassner1
Klassner, Frank
Genetic Algorithms with Lego Mindstorms and Matlab Frank Klassner1 James C Peyton-Jones2 Kurt.klassner,james.peyton-jones,kurt.lehmer}@villanova.edu Abstract This paper presents a case study in combining Lego MindstormsTM NXT with Matlab/Simulink to help-level understanding of AI techniques. We present a case study combining Lego MindstormsTM NXT [13] with Matlab
Optimal design of passive linear suspension using genetic algorithm
R Alkhatib; G Nakhaie Jazar; M. F Golnaraghi
2004-01-01
In this paper the genetic algorithm (GA) method is applied to the optimization problem of a linear one-degree-of-freedom (1-DOF) vibration isolator mount and the method is extended to the optimization of a linear quarter car suspension model. A novel criterion for selecting optimal suspension parameters is presented. An optimal relationship between the root mean square (RMS) of the absolute acceleration
Inverse band structure method via genetic algorithm for nanostructures
NASA Astrophysics Data System (ADS)
Kim, Kwiseon; Jones, Wesley B.; Zunger, Alex
2004-03-01
With the increased efficiency and accuracy of electronic structure methods, the inverse problem of material design has been tackled in e.g. Refs [1,2]. The inverse problems are solved by optimization, repeatedly applying the forward solving method while scanning the configuration space. We have implemented the inverse method for semiconductor alloys (iaga) by optimization using a genetic algorithm and PGAPack [3]. The forward solver employed is the parallel folded spectrum electronic structure method (PESCAN) with LDA-based empirical pseudopotentials, which has been run on up to million atom supercells. Hierarchical parallelism is adopted for using the parallel forward solver and the parallel genetic algorithm. Examples of inverse band structure results on AlGaAs alloys and superlattices will be presented. The approach is adaptable to a wide range of applications when combined with the efficient forward solvers. [1] A. Franceschetti and A. Zunger, Nature 402, 60 (1999). [2] G. H. Johannesson, et al., Phys. Rev. Lett. 88, 255506 (2002). [3] D. Levine, PGAPack: Parallel Genetic Algorithm Library (1998), T. Cwik and G. Klimeck, Proc. of 1st NASA/DoD Workshop on Evolvable Hardware, IEEE (1999).
MAC protocol for ad hoc networks using a genetic algorithm.
Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L; Reyna, Alberto
2014-01-01
The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339
Wireless LAN Load-Balancing with Genetic Algorithms
NASA Astrophysics Data System (ADS)
Scully, Ted; Brown, Kenneth N.
In recent years IEEE 802.11 wireless local area networks (WLANs) have become increasingly popular. Consequently, there has also been a surge in the number of end-users. The IEEE 802.11 standards do not provide any mechanism for load distribution and as a result user quality of service (QoS) degrades significantly in congested networks where large numbers of users tend to congregate in the same area. The objective of this paper is to provide load balancing techniques that optimise network throughput in areas of user congestion, thereby improving user QoS. Specifically, we develop micro-genetic and standard genetic algorithm approaches for the WLAN load balancing problem, and we analyse their strengths and weaknesses. We also compare the performance of these algorithms with schemes currently in use in IEEE 802.11 WLANs. The results demonstrate that the proposed genetic algorithms give a significant improvement in performance over current techniques. We also show that this improvement is achieved without penalising any class of user.
MAC Protocol for Ad Hoc Networks Using a Genetic Algorithm
Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L.
2014-01-01
The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339
Genetic Algorithm Optimizes Q-LAW Control Parameters
NASA Technical Reports Server (NTRS)
Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard
2008-01-01
A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.
A Hybrid Metaheuristic for Biclustering Based on Scatter Search and Genetic Algorithms
NASA Astrophysics Data System (ADS)
Nepomuceno, Juan A.; Troncoso, Alicia; Aguilar–Ruiz, Jesús S.
In this paper a hybrid metaheuristic for biclustering based on Scatter Search and Genetic Algorithms is presented. A general scheme of Scatter Search has been used to obtain high-quality biclusters, but a way of generating the initial population and a method of combination based on Genetic Algorithms have been chosen. Experimental results from yeast cell cycle and human B-cell lymphoma are reported. Finally, the performance of the proposed hybrid algorithm is compared with a genetic algorithm recently published.
NASA Astrophysics Data System (ADS)
Huang, Xiaobiao; Safranek, James
2014-09-01
Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.
Automatic Data Filter Customization Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Mandrake, Lukas
2013-01-01
This work predicts whether a retrieval algorithm will usefully determine CO2 concentration from an input spectrum of GOSAT (Greenhouse Gases Observing Satellite). This was done to eliminate needless runtime on atmospheric soundings that would never yield useful results. A space of 50 dimensions was examined for predictive power on the final CO2 results. Retrieval algorithms are frequently expensive to run, and wasted effort defeats requirements and expends needless resources. This algorithm could be used to help predict and filter unneeded runs in any computationally expensive regime. Traditional methods such as the Fischer discriminant analysis and decision trees can attempt to predict whether a sounding will be properly processed. However, this work sought to detect a subsection of the dimensional space that can be simply filtered out to eliminate unwanted runs. LDAs (linear discriminant analyses) and other systems examine the entire data and judge a "best fit," giving equal weight to complex and problematic regions as well as simple, clear-cut regions. In this implementation, a genetic space of "left" and "right" thresholds outside of which all data are rejected was defined. These left/right pairs are created for each of the 50 input dimensions. A genetic algorithm then runs through countless potential filter settings using a JPL computer cluster, optimizing the tossed-out data s yield (proper vs. improper run removal) and number of points tossed. This solution is robust to an arbitrary decision boundary within the data and avoids the global optimization problem of whole-dataset fitting using LDA or decision trees. It filters out runs that would not have produced useful CO2 values to save needless computation. This would be an algorithmic preprocessing improvement to any computationally expensive system.
Using Bayesian Network Inference Algorithms to Recover Molecular Genetic Regulatory Networks
Babu, M. Madan
biological system. The sampled data is then passed to an inference algorithm to evaluate the algorithmUsing Bayesian Network Inference Algorithms to Recover Molecular Genetic Regulatory Networks Jing of bioinformatics the use of network inference algorithms to predict causal models of molecular networks from
Eliciting spatial statistics from geological experts using genetic algorithms
NASA Astrophysics Data System (ADS)
Walker, Matthew; Curtis, Andrew
2014-07-01
A new method to obtain the statistics of a geostatistical model is introduced. The method elicits the statistical information from a geological expert directly, by iteratively updating a population of vectors of statistics, based on the expert's subjective opinion of the corresponding geological simulations. Thus, it does not require the expert to have knowledge of the mathematical and statistical details of the model. The process uses a genetic algorithm to generate new vectors. We demonstrate the methodology for a particular geostatistical model used to model rock pore-space, which simulates the spatial distribution of matrix and pores over a 2-D grid, using multipoint statistics specified by conditional probabilities. Experts were asked to use the algorithm to estimate the statistics of a given target pore-space image with known statistics; thus, their numerical rates of convergence could be calculated. Convergence was measured for all experts, showing that the algorithm can be used to find appropriate probabilities given the expert's subjective input. However, considerable and apparently irreducible residual misfit was found between the true statistics and the estimates of statistics obtained by the experts, with the root-mean-square error on the conditional probabilities typically >0.1. This is interpreted as the limit of the experts' abilities to distinguish between realizations of different spatial statistics using the algorithm. More accurate discrimination is therefore likely to require complementary elicitation techniques or sources of information independent of expert opinion.
An Introduction to Genetic Algorithms and to Their Use in Information Retrieval.
ERIC Educational Resources Information Center
Jones, Gareth; And Others
1994-01-01
Genetic algorithms, a class of nondeterministic algorithms in which the role of chance makes the precise nature of a solution impossible to guarantee, seem to be well suited to combinatorial-optimization problems in information retrieval. Provides an introduction to techniques and characteristics of genetic algorithms and illustrates their…
A NEW MULTI-FREQUENCY VIBRATIONAL GENETIC ALGORITHM IN RADAR CROSS SECTION MINIMIZATION PROBLEMS
Y. Volkan PEHLIVANOGLU
Within this study, it is aimed to provide an efficient stochastic algorithm for different optimization problems. For this purpose, as a search method, multi frequency vibrational genetic algorithm (m-VGA) is improved and used to accelerate the genetic algorithm for radar cross section minimization problem. From the results obtained, it is concluded that m-VGA decreased the required time for the minimized
Tree-structured vector quantization design using adaptive genetic algorithms
NASA Astrophysics Data System (ADS)
Xu, Yong; Liu, Yiwen; Chen, Hexin; Dai, Yisong
1998-08-01
Recently, vector quantization (VQ) has received considerable attention, and has become an effective tool for image compression because of its high compression ratio and simple decoding process. In order to reduce the computational complexity of searching and archiving, tree search can be used in codebook generation which is a major problem of VQ. The Codebook can be generated by a clustering algorithm that selects the most significant vectors of a training set in order to minimize the coding error when all the training set vectors are encoded. Genetic algorithm (GA), a global search method with high robustness, is very effective at finding optimal or near optimal solution to some complex and nonlinear problems. This paper presents a new technique for design a tree-structured vector quantizer using adaptive genetic algorithm. The difference between adaptive GA (AGA) and standard GA is that the probabilities of crossover and mutation of the former are varied depending on fitness values of solutions, thus prove the performance. Experimental results have shown that applying AGA to clustering can accurately locate the clustering centers. In this paper, AGA is used in tree-structured VQ to generate very node codebook. It is proved theoretically and experimentally that the reconstructed images generated by this method have high visual qualities.
Sobel, E.; Lange, K.; O`Connell, J.R.
1996-12-31
Haplotyping is the logical process of inferring gene flow in a pedigree based on phenotyping results at a small number of genetic loci. This paper formalizes the haplotyping problem and suggests four algorithms for haplotype reconstruction. These algorithms range from exhaustive enumeration of all haplotype vectors to combinatorial optimization by simulated annealing. Application of the algorithms to published genetic analyses shows that manual haplotyping is often erroneous. Haplotyping is employed in screening pedigrees for phenotyping errors and in positional cloning of disease genes from conserved haplotypes in population isolates. 26 refs., 6 figs., 3 tabs.
A test sheet generating algorithm based on intelligent genetic algorithm and hierarchical planning
NASA Astrophysics Data System (ADS)
Gu, Peipei; Niu, Zhendong; Chen, Xuting; Chen, Wei
2013-03-01
In recent years, computer-based testing has become an effective method to evaluate students' overall learning progress so that appropriate guiding strategies can be recommended. Research has been done to develop intelligent test assembling systems which can automatically generate test sheets based on given parameters of test items. A good multisubject test sheet depends on not only the quality of the test items but also the construction of the sheet. Effective and efficient construction of test sheets according to multiple subjects and criteria is a challenging problem. In this paper, a multi-subject test sheet generation problem is formulated and a test sheet generating approach based on intelligent genetic algorithm and hierarchical planning (GAHP) is proposed to tackle this problem. The proposed approach utilizes hierarchical planning to simplify the multi-subject testing problem and adopts genetic algorithm to process the layered criteria, enabling the construction of good test sheets according to multiple test item requirements. Experiments are conducted and the results show that the proposed approach is capable of effectively generating multi-subject test sheets that meet specified requirements and achieve good performance.
Multilayer Traffic Network Optimized by Multiobjective Genetic Clustering Algorithm
NASA Astrophysics Data System (ADS)
Wen, Feng; Gen, Mitsuo; Yu, Xinjie
This paper introduces a multilayer traffic network model and traffic network clustering method for solving the route selection problem (RSP) in car navigation system (CNS). The purpose of the proposed method is to reduce the computation time of route selection substantially with acceptable loss of accuracy by preprocessing the large size traffic network into new network form. The proposed approach further preprocesses the traffic network than the traditional hierarchical network method by clustering method. The traffic network clustering considers two criteria. We specify a genetic clustering algorithm for traffic network clustering and use NSGA-II for calculating the multiple objective Pareto optimal set. The proposed method can overcome the size limitations when solving route selection in CNS. Solutions provided by the proposed algorithm are compared with the optimal solutions to analyze and quantify the loss of accuracy.
Genetic algorithms for the construction of D-optimal designs
Heredia-Langner, Alejandro; Carlyle, W M.; Montgomery, D C.; Borror, Connie M.; Runger, George C.
2003-01-01
Computer-generated designs are useful for situations where standard factorial, fractional factorial or response surface designs cannot be easily employed. Alphabetically-optimal designs are the most widely used type of computer-generated designs, and of these, the D-optimal (or D-efficient) class of designs are extremely popular. D-optimal designs are usually constructed by algorithms that sequentially add and delete points from a potential design based using a candidate set of points spaced over the region of interest. We present a technique to generate D-efficient designs using genetic algorithms (GA). This approach eliminates the need to explicitly consider a candidate set of experimental points and it can handle highly constrained regions while maintaining a level of performance comparable to more traditional design construction techniques.
A Genetic Algorithm for Solving the Generalized Vehicle Routing Problem
NASA Astrophysics Data System (ADS)
Pop, P. C.; Matei, O.; Sitar, C. Pop; Chira, C.
The generalized vehicle routing problem is a variant of the well-known vehicle routing problem in which the nodes of a graph are partitioned into a given number of node sets (clusters) and the objective is to find the minimum-cost delivery or collection of routes, subject to capacity restrictions, from a given depot to the number of predefined clusters passing through one node from each clusters. We present an effective metaheuristic algorithm for the problem based on genetic algorithms. The proposed metaheuristic is competitive with other heuristics published to date in both solution quality and computation time. Computational results for benchmarks problems are reported and the results point out that GA is an appropriate method to explore the search space of this complex problem and leads to good solutions in a short amount of time.
Application of genetic algorithms to tuning fuzzy control systems
NASA Technical Reports Server (NTRS)
Espy, Todd; Vombrack, Endre; Aldridge, Jack
1993-01-01
Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.
A genetic algorithm based method for docking flexible molecules
Judson, R.S. [Sandia National Labs., Livermore, CA (United States); Jaeger, E.P.; Treasurywala, A.M. [Sterling-Winthrop Inc., Collegeville, PA (United States)
1993-11-01
The authors describe a computational method for docking flexible molecules into protein binding sites. The method uses a genetic algorithm (GA) to search the combined conformation/orientation space of the molecule to find low energy conformation. Several techniques are described that increase the efficiency of the basic search method. These include the use of several interacting GA subpopulations or niches; the use of a growing algorithm that initially docks only a small part of the molecule; and the use of gradient minimization during the search. To illustrate the method, they dock Cbz-GlyP-Leu-Leu (ZGLL) into thermolysin. This system was chosen because a well refined crystal structure is available and because another docking method had previously been tested on this system. Their method is able to find conformations that lie physically close to and in some cases lower in energy than the crystal conformation in reasonable periods of time on readily available hardware.
Genetic Algorithms: A New Method for Neutron Beam Spectral Characterization
David W. Freeman
2000-06-04
A revolutionary new concept for solving the neutron spectrum unfolding problem using genetic algorithms (GAs) has recently been introduced. GAs are part of a new field of evolutionary solution techniques that mimic living systems with computer-simulated chromosome solutions that mate, mutate, and evolve to create improved solutions. The original motivation for the research was to improve spectral characterization of neutron beams associated with boron neutron capture therapy (BNCT). The GA unfolding technique has been successfully applied to problems with moderate energy resolution (up to 47 energy groups). Initial research indicates that the GA unfolding technique may well be superior to popular unfolding methods in common use. Research now under way at Kansas State University is focused on optimizing the unfolding algorithm and expanding its energy resolution to unfold detailed beam spectra based on multiple foil measurements. Indications are that the final code will significantly outperform current, state-of-the-art codes in use by the scientific community.
A Parallel Genetic Algorithm for Automated Electronic Circuit Design
NASA Technical Reports Server (NTRS)
Long, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris
2000-01-01
Parallelized versions of genetic algorithms (GAs) are popular primarily for three reasons: the GA is an inherently parallel algorithm, typical GA applications are very compute intensive, and powerful computing platforms, especially Beowulf-style computing clusters, are becoming more affordable and easier to implement. In addition, the low communication bandwidth required allows the use of inexpensive networking hardware such as standard office ethernet. In this paper we describe a parallel GA and its use in automated high-level circuit design. Genetic algorithms are a type of trial-and-error search technique that are guided by principles of Darwinian evolution. Just as the genetic material of two living organisms can intermix to produce offspring that are better adapted to their environment, GAs expose genetic material, frequently strings of 1s and Os, to the forces of artificial evolution: selection, mutation, recombination, etc. GAs start with a pool of randomly-generated candidate solutions which are then tested and scored with respect to their utility. Solutions are then bred by probabilistically selecting high quality parents and recombining their genetic representations to produce offspring solutions. Offspring are typically subjected to a small amount of random mutation. After a pool of offspring is produced, this process iterates until a satisfactory solution is found or an iteration limit is reached. Genetic algorithms have been applied to a wide variety of problems in many fields, including chemistry, biology, and many engineering disciplines. There are many styles of parallelism used in implementing parallel GAs. One such method is called the master-slave or processor farm approach. In this technique, slave nodes are used solely to compute fitness evaluations (the most time consuming part). The master processor collects fitness scores from the nodes and performs the genetic operators (selection, reproduction, variation, etc.). Because of dependency issues in the GA, it is possible to have idle processors. However, as long as the load at each processing node is similar, the processors are kept busy nearly all of the time. In applying GAs to circuit design, a suitable genetic representation 'is that of a circuit-construction program. We discuss one such circuit-construction programming language and show how evolution can generate useful analog circuit designs. This language has the desirable property that virtually all sets of combinations of primitives result in valid circuit graphs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. Using a parallel genetic algorithm and circuit simulation software, we present experimental results as applied to three analog filter and two amplifier design tasks. For example, a figure shows an 85 dB amplifier design evolved by our system, and another figure shows the performance of that circuit (gain and frequency response). In all tasks, our system is able to generate circuits that achieve the target specifications.
Design Space Exploration of incompletely specified Embedded Systems by Genetic Algorithms
Huss, Sorin A.
Design Space Exploration of incompletely specified Embedded Systems by Genetic Algorithms Stephan design space exploration algorithm, which jointly determines a complete set of Pareto optimal for new modules in a single optimization run. This design space exploration method is based
Dynamic and fault tolerant three-dimensional cellular genetic algorithms
Al Naqi, Asmaa
2012-11-29
In the area of artificial intelligence, the development of Evolutionary Algorithms (EAs) has been very active, especially in the last decade. These algorithms started to evolve when scientists from various regions of the ...
An Evolvement-based Genetic Algorithm for Computer-aided Molecular Docking
NASA Astrophysics Data System (ADS)
Ling, Kang; Xiaoyu, Zhao; Xi, Chen; Xicheng, Wang
2010-05-01
Species dynamics model is introduced into the genetic algorithm to reflect the true state of evolution. An adaptive evolution algorithm is developed. In the algorithm, an adaptive strategy is used to overcome the difficulty of confirming the crossover and mutation probabilities. Small population strategy and optimal strategy ensure the diversity of the populations. Numerical results show that introducing species dynamics model can improve the efficiency of the algorithm. Based on the genetic algorithm, a new molecular docking program is developed. Docking result indicates that the algorithm can effectively solve the molecular docking problem.
Genetic Algorithm based route planner for large urban street networks
Suranga Chandima Nanayakkara; Dipti Srinivasan; Lai Wei Lup; Xavier German; Elizabeth Taylor; S. H. Ong
2007-01-01
Finding the shortest path from a given source to a given destination is a well known and widely applicable problem. Most of the work done in the area have used static route planning algorithms such as A*, Dijkstra's, Bellman-Ford algorithm etc. Although these algorithms are said to be optimum, they are not capable of dealing with certain real life scenarios.
Full design of fuzzy controllers using genetic algorithms
NASA Technical Reports Server (NTRS)
Homaifar, Abdollah; Mccormick, ED
1992-01-01
This paper examines the applicability of genetic algorithms (GA) in the complete design of fuzzy logic controllers. While GA has been used before in the development of rule sets or high performance membership functions, the interdependence between these two components dictates that they should be designed together simultaneously. GA is fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. We show the application of this new method to the development of a cart controller.
A new chromatic dispersion compensation method based on genetic algorithm
NASA Astrophysics Data System (ADS)
Liu, Chun-wu; Qin, Jiang-yi; Huang, Zhi-ping; Zhang, Yi-meng
2013-08-01
In the 40Gbps high-speed optical fiber communication system, chromatic dispersion of optical signal brings about to generation of inter-symbol interface which influences the quality of optical fiber communication. In order to solve the above questions in the 40Gbps differential quarter phase-shift keying (DQPSK) optical fiber communication system, a new method of chromatic dispersion compensation based on genetic algorithm is proposed according to the demodulation of DQPSK optical signal and the trait of chromatic dispersion. Result shows that the system's receiving sensitivity has been enhanced up to six orders of magnitude.
Full design of fuzzy controllers using genetic algorithms
NASA Technical Reports Server (NTRS)
Homaifar, Abdollah; Mccormick, ED
1992-01-01
This paper examines the applicability of genetic algorithms in the complete design of fuzzy logic controllers. While GA has been used before in the development of rule sets or high performance membership functions, the interdependence between these two components dictates that they should be designed together simultaneously. GA is fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. We show the application of this new method to the development of a cart controller.
Optimization of reinforced soil embankments by genetic algorithm
NASA Astrophysics Data System (ADS)
Ponterosso, P.; Fox, D. St. J.
2000-04-01
A Genetic Algorithm (GA) is described, which produces solutions to the cost optimization problem of reinforcement layout for reinforced soil slopes. These solutions incorporate different types of reinforcement within a single slope. The GA described is implemented with the aim of optimizing the cost of materials for the preliminary layout of reinforced soil embankments. The slope design method chosen is the U.K. Department of Transport HA 68/94 Design Methods for the Reinforcement of Highway Slopes by Reinforced Soil and Soil Nailing Techniques. The results confirm that there is a role for the GA in optimization of reinforced soil design.
Application of Genetic Algorithms in Nonlinear Heat Conduction Problems
Khan, Waqar A.
2014-01-01
Genetic algorithms are employed to optimize dimensionless temperature in nonlinear heat conduction problems. Three common geometries are selected for the analysis and the concept of minimum entropy generation is used to determine the optimum temperatures under the same constraints. The thermal conductivity is assumed to vary linearly with temperature while internal heat generation is assumed to be uniform. The dimensionless governing equations are obtained for each selected geometry and the dimensionless temperature distributions are obtained using MATLAB. It is observed that GA gives the minimum dimensionless temperature in each selected geometry. PMID:24695517
Optimal brushless DC motor design using genetic algorithms
NASA Astrophysics Data System (ADS)
Rahideh, A.; Korakianitis, T.; Ruiz, P.; Keeble, T.; Rothman, M. T.
2010-11-01
This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using a genetic algorithm. Characteristics of the motor are expressed as functions of motor geometries. The objective function is a combination of losses, volume and cost to be minimized simultaneously. Electrical and mechanical requirements (i.e. voltage, torque and speed) and other limitations (e.g. upper and lower limits of the motor geometries) are cast into constraints of the optimization problem. One sample case is used to illustrate the design and optimization technique.
An Evolved Wavelet Library Based on Genetic Algorithm
Vaithiyanathan, D.; Seshasayanan, R.; Kunaraj, K.; Keerthiga, J.
2014-01-01
As the size of the images being captured increases, there is a need for a robust algorithm for image compression which satiates the bandwidth limitation of the transmitted channels and preserves the image resolution without considerable loss in the image quality. Many conventional image compression algorithms use wavelet transform which can significantly reduce the number of bits needed to represent a pixel and the process of quantization and thresholding further increases the compression. In this paper the authors evolve two sets of wavelet filter coefficients using genetic algorithm (GA), one for the whole image portion except the edge areas and the other for the portions near the edges in the image (i.e., global and local filters). Images are initially separated into several groups based on their frequency content, edges, and textures and the wavelet filter coefficients are evolved separately for each group. As there is a possibility of the GA settling in local maximum, we introduce a new shuffling operator to prevent the GA from this effect. The GA used to evolve filter coefficients primarily focuses on maximizing the peak signal to noise ratio (PSNR). The evolved filter coefficients by the proposed method outperform the existing methods by a 0.31?dB improvement in the average PSNR and a 0.39?dB improvement in the maximum PSNR. PMID:25405225
Integrating GIS and genetic algorithms for automating land partitioning
NASA Astrophysics Data System (ADS)
Demetriou, Demetris; See, Linda; Stillwell, John
2014-08-01
Land consolidation is considered to be the most effective land management planning approach for controlling land fragmentation and hence improving agricultural efficiency. Land partitioning is a basic process of land consolidation that involves the subdivision of land into smaller sub-spaces subject to a number of constraints. This paper explains the development of a module called LandParcelS (Land Parcelling System) that integrates geographical information systems and a genetic algorithm to automate the land partitioning process by designing and optimising land parcels in terms of their shape, size and value. This new module has been applied to two land blocks that are part of a larger case study area in Cyprus. Partitioning is carried out by guiding a Thiessen polygon process within ArcGIS and it is treated as a multiobjective problem. The results suggest that a step forward has been made in solving this complex spatial problem, although further research is needed to improve the algorithm. The contribution of this research extends land partitioning and space partitioning in general, since these approaches may have relevance to other spatial processes that involve single or multi-objective problems that could be solved in the future by spatial evolutionary algorithms.
Experience with a Genetic Algorithm Implemented on a Multiprocessor Computer
NASA Technical Reports Server (NTRS)
Plassman, Gerald E.; Sobieszczanski-Sobieski, Jaroslaw
2000-01-01
Numerical experiments were conducted to find out the extent to which a Genetic Algorithm (GA) may benefit from a multiprocessor implementation, considering, on one hand, that analyses of individual designs in a population are independent of each other so that they may be executed concurrently on separate processors, and, on the other hand, that there are some operations in a GA that cannot be so distributed. The algorithm experimented with was based on a gaussian distribution rather than bit exchange in the GA reproductive mechanism, and the test case was a hub frame structure of up to 1080 design variables. The experimentation engaging up to 128 processors confirmed expectations of radical elapsed time reductions comparing to a conventional single processor implementation. It also demonstrated that the time spent in the non-distributable parts of the algorithm and the attendant cross-processor communication may have a very detrimental effect on the efficient utilization of the multiprocessor machine and on the number of processors that can be used effectively in a concurrent manner. Three techniques were devised and tested to mitigate that effect, resulting in efficiency increasing to exceed 99 percent.
NASA Astrophysics Data System (ADS)
Que, Dashun; Li, Gang; Yue, Peng
2007-12-01
An adaptive optimization watermarking algorithm based on Genetic Algorithm (GA) and discrete wavelet transform (DWT) is proposed in this paper. The core of this algorithm is the fitness function optimization model for digital watermarking based on GA. The embedding intensity for digital watermarking can be modified adaptively, and the algorithm can effectively ensure the imperceptibility of watermarking while the robustness is ensured. The optimization model research may provide a new idea for anti-coalition attacks of digital watermarking algorithm. The paper has fulfilled many experiments, including the embedding and extracting experiments of watermarking, the influence experiments by the weighting factor, the experiments of embedding same watermarking to the different cover image, the experiments of embedding different watermarking to the same cover image, the comparative analysis experiments between this optimization algorithm and human visual system (HVS) algorithm and etc. The simulation results and the further analysis show the effectiveness and advantage of the new algorithm, which also has versatility and expandability. And meanwhile it has better ability of anti-coalition attacks. Moreover, the robustness and security of watermarking algorithm are improved by scrambling transformation and chaotic encryption while preprocessing the watermarking.
Genetic algorithm in the structural design of Cooke triplet lenses
NASA Astrophysics Data System (ADS)
Hazra, Lakshminarayan; Banerjee, Saswatee
1999-08-01
This paper is in tune with our efforts to develop a systematic method for multicomponent lens design. Our aim is to find a suitable starting point in the final configuration space, so that popular local search methods like damped least squares (DLS) may directly lead to a useful solution. For 'ab initio' design problems, a thin lens layout specifying the powers of the individual components and the intercomponent separations are worked out analytically. Requirements of central aberration targets for the individual components in order to satisfy the prespecified primary aberration targets for the overall system are then determined by nonlinear optimization. The next step involves structural design of the individual components by optimization techniques. This general method may be adapted for the design of triplets and their derivatives. However, for the thin lens design of a Cooke triplet composed of three airspaced singlets, the two steps of optimization mentioned above may be combined into a single optimization procedure. The optimum configuration for each of the single set, catering to the required Gaussian specification and primary aberration targets for the Cooke triplet, are determined by an application of genetic algorithm (GA). Our implementation of this algorithm is based on simulations of some complex tools of natural evolution, like selection, crossover and mutation. Our version of GA may or may not converge to a unique optimum, depending on some of the algorithm specific parameter values. With our algorithm, practically useful solutions are always available, although convergence to a global optimum can not be guaranteed. This is perfectly in keeping with our need to allow 'floating' of aberration targets in the subproblem level. Some numerical results dealing with our preliminary investigations on this problem are presented.
Genetic algorithms in astronomy and astrophysics Vinesh Rajpaul1,2
Masci, Frank
Genetic algorithms in astronomy and astrophysics Vinesh Rajpaul1,2 1 Astrophysics, Cosmology-mail: vinesh.rajpaul@uct.ac.za Abstract. Genetic algorithms (GAs) emulate the process of biological evolution errorsRecombination Figure 1. Schematic to illustrate the workings of a simple binary-coded genetic
A New Multiobjective Genetic Algorithm for Route Selection
NASA Astrophysics Data System (ADS)
Wen, Feng; Gen, Mitsuo; Yu, Xinjie
In the area of Intelligent Transport Systems, the multiobjective route selection problem (mRSP) becomes an important key problem that needs to be solved in car navigation system (CNS). In this paper, we propose an effective route selection approach for solving mRSP while minimizing driving distance, driving time and driving cost simultaneously. A new multiobjective genetic algorithm (moGA) called distance Pareto Genetic Algorithm (dpGA) is presented to effectively solve mRSP. The mechanism of the proposed dpGA guarantees good convergence toward the Pareto-optimal front and gives sufficient emphasis on the diversity feature. The fitness function used in dpGA is based on two kinds of distance values, i.e. Pareto distance and crowding distance. Finally, we demonstrate the applicability and evaluate the efficiency of the proposed solution approach by using numerical experiments with the real digital road map data. The experimental results show the effectiveness of the proposed solution approach.
Segmentation of thermographic images of hands using a genetic algorithm
NASA Astrophysics Data System (ADS)
Ghosh, Payel; Mitchell, Melanie; Gold, Judith
2010-01-01
This paper presents a new technique for segmenting thermographic images using a genetic algorithm (GA). The individuals of the GA also known as chromosomes consist of a sequence of parameters of a level set function. Each chromosome represents a unique segmenting contour. An initial population of segmenting contours is generated based on the learned variation of the level set parameters from training images. Each segmenting contour (an individual) is evaluated for its fitness based on the texture of the region it encloses. The fittest individuals are allowed to propagate to future generations of the GA run using selection, crossover and mutation. The dataset consists of thermographic images of hands of patients suffering from upper extremity musculo-skeletal disorders (UEMSD). Thermographic images are acquired to study the skin temperature as a surrogate for the amount of blood flow in the hands of these patients. Since entire hands are not visible on these images, segmentation of the outline of the hands on these images is typically performed by a human. In this paper several different methods have been tried for segmenting thermographic images: Gabor-wavelet-based texture segmentation method, the level set method of segmentation and our GA which we termed LSGA because it combines level sets with genetic algorithms. The results show a comparative evaluation of the segmentation performed by all the methods. We conclude that LSGA successfully segments entire hands on images in which hands are only partially visible.
Optimal robust motion controller design using multiobjective genetic algorithm.
Sarjaš, Andrej; Sve?ko, Rajko; Chowdhury, Amor
2014-01-01
This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm-differential evolution. PMID:24987749
Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm
Sve?ko, Rajko
2014-01-01
This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749
Learning lung nodule similarity using a genetic algorithm
NASA Astrophysics Data System (ADS)
Seitz, Kerry A., Jr.; Giuca, Anne-Marie; Furst, Jacob; Raicu, Daniela
2012-03-01
The effectiveness and efficiency of content-based image retrieval (CBIR) can be improved by determining an optimal combination of image features to use in determining similarity between images. This combination of features can be optimized using a genetic algorithm (GA). Although several studies have used genetic algorithms to refine image features and similarity measures in CBIR, the present study is the first to apply these techniques to medical image retrieval. By implementing a GA to test different combinations of image features for pulmonary nodules in CT scans, the set of image features was reduced to 29 features from a total of 63 extracted features. The performance of the CBIR system was assessed by calculating the average precision across all query nodules. The precision values obtained using the GA-reduced set of features were significantly higher than those found using all 63 image features. Using radiologist-annotated malignancy ratings as ground truth resulted in an average precision of 85.95% after 3 images retrieved per query nodule when using the feature set identified by the GA. Using computer-predicted malignancy ratings as ground truth resulted in an average precision of 86.91% after 3 images retrieved. The results suggest that in the absence of radiologist semantic ratings, using computer-predicted malignancy as ground truth is a valid substitute given the closeness of the two precision values.
Internal Lattice Reconfiguration for Diversity Tuning in Cellular Genetic Algorithms
Morales-Reyes, Alicia; Erdogan, Ahmet T.
2012-01-01
Cellular Genetic Algorithms (cGAs) have attracted the attention of researchers due to their high performance, ease of implementation and massive parallelism. Maintaining an adequate balance between exploitative and explorative search is essential when studying evolutionary optimization techniques. In this respect, cGAs inherently possess a number of structural configuration parameters that are able to sustain diversity during evolution. In this study, the internal reconfiguration of the lattice is proposed to constantly or adaptively control the exploration-exploitation trade-off. Genetic operators are characterized in their simplest form since algorithmic performance is assessed on implemented reconfiguration mechanisms. Moreover, internal reconfiguration allows the adjacency of individuals to be maintained. Hence, any improvement in performance is only a consequence of topological changes. Two local selection methods presenting opposite selection pressures are used in order to evaluate the influence of the proposed techniques. Problems ranging from continuous to real world and combinatorial are tackled. Empirical results are supported statistically in terms of efficiency and efficacy. PMID:22859973
Feature Subset Selection, Class Separability, and Genetic Algorithms
Cantu-Paz, E
2004-01-21
The performance of classification algorithms in machine learning is affected by the features used to describe the labeled examples presented to the inducers. Therefore, the problem of feature subset selection has received considerable attention. Genetic approaches to this problem usually follow the wrapper approach: treat the inducer as a black box that is used to evaluate candidate feature subsets. The evaluations might take a considerable time and the traditional approach might be unpractical for large data sets. This paper describes a hybrid of a simple genetic algorithm and a method based on class separability applied to the selection of feature subsets for classification problems. The proposed hybrid was compared against each of its components and two other feature selection wrappers that are used widely. The objective of this paper is to determine if the proposed hybrid presents advantages over the other methods in terms of accuracy or speed in this problem. The experiments used a Naive Bayes classifier and public-domain and artificial data sets. The experiments suggest that the hybrid usually finds compact feature subsets that give the most accurate results, while beating the execution time of the other wrappers.
Actuator Placement Via Genetic Algorithm for Aircraft Morphing
NASA Technical Reports Server (NTRS)
Crossley, William A.; Cook, Andrea M.
2001-01-01
This research continued work that began under the support of NASA Grant NAG1-2119. The focus of this effort was to continue investigations of Genetic Algorithm (GA) approaches that could be used to solve an actuator placement problem by treating this as a discrete optimization problem. In these efforts, the actuators are assumed to be "smart" devices that change the aerodynamic shape of an aircraft wing to alter the flow past the wing, and, as a result, provide aerodynamic moments that could provide flight control. The earlier work investigated issued for the problem statement, developed the appropriate actuator modeling, recognized the importance of symmetry for this problem, modified the aerodynamic analysis routine for more efficient use with the genetic algorithm, and began a problem size study to measure the impact of increasing problem complexity. The research discussed in this final summary further investigated the problem statement to provide a "combined moment" problem statement to simultaneously address roll, pitch and yaw. Investigations of problem size using this new problem statement provided insight into performance of the GA as the number of possible actuator locations increased. Where previous investigations utilized a simple wing model to develop the GA approach for actuator placement, this research culminated with application of the GA approach to a high-altitude unmanned aerial vehicle concept to demonstrate that the approach is valid for an aircraft configuration.
Solving for the RC4 stream cipher state register using a genetic algorithm
Benjamin Ferriman; Charlie Obimbo
2014-01-01
The RC4 stream cipher has shown to be quite resilient to cryptanalysis for the 26 years it has been around. The algorithm is still one of the most widely used methods of encryption over the Internet today being implemented through the Secure Socket Layer and Transport Layer Security protocols. Genetic algorithms are a sub-class of evolutionary algorithms that have been
Hybrid Genetic Algorithms for Minimization of a Polypeptide Specific Energy Model
Laurence D. Merkle; Gary B. Lamont; George H. Gates Jr.; Ruth Pachter
1996-01-01
A hybrid genetic algorithm for polypeptide structure prediction is proposed which incorporates efficient gradient-based minimization directly in the fitness evaluation. Fitness is based on a polypeptide specific potential energy model. The algorithm includes a replacement frequency parameter which specifies the probability with which an individual is replaced by its minimized counterpart. Thus, the algorithm can implement either Baldwinian, Lamarckian, or
Y. Volkan Pehlivanoglu
A new optimization algorithm called multi-frequency vibrational genetic algorithm (mVGA) that can be used to solve the path planning problems of autonomous unmanned aerial vehicles (UAVs) is significantly improved. The algorithm emphasizes a new mutation application strategy and diversity variety such as the global random and the local random diversity. Clustering method and Voronoi diagram concepts are used within the
Damage detection by an adaptive real-parameter simulated annealing genetic algorithm
Rong-Song He; Shun-Fa Hwang
2006-01-01
An effective algorithm, which combined an adaptive real-parameter genetic algorithm with simulated annealing, is proposed to detect damage occurrence in beam-type structures. The proposed algorithm uses the displacements of static response and natural frequencies of modal analysis, which are obtained by finite element software ANSYS. There are three different kinds of beam structures to verify the performance of the proposed
Optimal design of electric machine using genetic algorithms coupled with direct method
Oh, Y.H.; Chung, T.K. (Chung Ang Univ., Seoul (Korea, Republic of). Dept. of Electrical Engineering); Kim, M.K. (Samsung Electronics Co., Ltd., Kyunggi-do (Korea, Republic of). FA Research Inst.); Jung, H.K. (Seoul National Univ. (Korea, Republic of). School of Electrical Engineering)
1999-05-01
This paper discusses the development of a new optimization algorithm for DC motor design. In principle, the new algorithm utilizes a mixed method that consists of genetic algorithms in conjunction with direct search method. The genetic algorithms are used for locating the global optimum region while the direct search method is used to achieve objective function convergence. In order to validate the effectiveness, the new algorithm has been applied to an actual DC motor. Field and torque characteristics of the DC motor are computed using finite element method and the principle of virtual work, respectively.
Human emotion detector based on genetic algorithm using lip features
NASA Astrophysics Data System (ADS)
Brown, Terrence; Fetanat, Gholamreza; Homaifar, Abdollah; Tsou, Brian; Mendoza-Schrock, Olga
2010-04-01
We predicted human emotion using a Genetic Algorithm (GA) based lip feature extractor from facial images to classify all seven universal emotions of fear, happiness, dislike, surprise, anger, sadness and neutrality. First, we isolated the mouth from the input images using special methods, such as Region of Interest (ROI) acquisition, grayscaling, histogram equalization, filtering, and edge detection. Next, the GA determined the optimal or near optimal ellipse parameters that circumvent and separate the mouth into upper and lower lips. The two ellipses then went through fitness calculation and were followed by training using a database of Japanese women's faces expressing all seven emotions. Finally, our proposed algorithm was tested using a published database consisting of emotions from several persons. The final results were then presented in confusion matrices. Our results showed an accuracy that varies from 20% to 60% for each of the seven emotions. The errors were mainly due to inaccuracies in the classification, and also due to the different expressions in the given emotion database. Detailed analysis of these errors pointed to the limitation of detecting emotion based on the lip features alone. Similar work [1] has been done in the literature for emotion detection in only one person, we have successfully extended our GA based solution to include several subjects.
Primary chromatic aberration elimination via optimization work with genetic algorithm
NASA Astrophysics Data System (ADS)
Wu, Bo-Wen; Liu, Tung-Kuan; Fang, Yi-Chin; Chou, Jyh-Horng; Tsai, Hsien-Lin; Chang, En-Hao
2008-09-01
Chromatic Aberration plays a part in modern optical systems, especially in digitalized and smart optical systems. Much effort has been devoted to eliminating specific chromatic aberration in order to match the demand for advanced digitalized optical products. Basically, the elimination of axial chromatic and lateral color aberration of an optical lens and system depends on the selection of optical glass. According to reports from glass companies all over the world, the number of various newly developed optical glasses in the market exceeds three hundred. However, due to the complexity of a practical optical system, optical designers have so far had difficulty in finding the right solution to eliminate small axial and lateral chromatic aberration except by the Damped Least Squares (DLS) method, which is limited in so far as the DLS method has not yet managed to find a better optical system configuration. In the present research, genetic algorithms are used to replace traditional DLS so as to eliminate axial and lateral chromatic, by combining the theories of geometric optics in Tessar type lenses and a technique involving Binary/Real Encoding, Multiple Dynamic Crossover and Random Gene Mutation to find a much better configuration for optical glasses. By implementing the algorithms outlined in this paper, satisfactory results can be achieved in eliminating axial and lateral color aberration.
Machine based optimization using genetic algorithms in a storage ring
NASA Astrophysics Data System (ADS)
Tian, K.; Safranek, J.; Yan, Y.
2014-02-01
The genetic algorithm (GA) has been a popular technique in optimizing the design of particle accelerators. As a population based algorithm, GA requires a large number of evaluations of the objective functions, which can be time consuming. One can benefit from parallel computing with significantly reduced computing time when fulfilling the function evaluation by a numerical machine model in simulation codes. Indeed, this is the most common approach in GA applications. In this paper, instead of applying GA in the conventional numerical calculations as described above, we present a successful experimental demonstration of implementing GA in real machine based optimization. We conduct the minimization of the average vertical beam size of the SPEAR3 storage ring using GA. Beam loss rate is chosen as the sole objective function because it is inversely proportional to the vertical beam size and can be measured instantaneously in SPEAR3. The decision variables are the strengths of SPEAR3 skew quadrupoles, by varying which we can change both the betatron coupling and the vertical dispersion while searching for the minimum beam size. The results in this paper can shed light on new applications of GAs in the particle accelerator community, for example, optimizing the luminosity of a high energy collider or the injection efficiency of a diffraction limited storage ring in real time.
Shape optimization of noise barriers using genetic algorithms
NASA Astrophysics Data System (ADS)
Duhamel, D.
2006-10-01
This article presents a method to find optimal shapes for noise barriers by coupling a boundary element solution of the sound pressure around the barrier and an optimization process by genetic algorithms to minimize the sound pressure level in a domain behind the barrier. The objective is not to provide geometries with immediate practical applications but to estimate the improvement that could be obtained if noise barriers with improved shapes were used instead of the traditional barriers built today. The method supposes given source and receiver positions and the calculation provides an optimal shape for the barrier to reduce the sound pressure at receiver points over a specified frequency band. Different examples are presented to estimate the influence of the source and receiver positions, of the frequencies and the influence of the size of the barrier. The main conclusion is an estimate of the potential improvement of noise barriers efficiency by using better geometries.
Application of a genetic algorithm to wind turbine design
Selig, M.S.; Coverstone-Carroll, V.L.
1995-09-01
This paper presents an optimization procedure for stall-regulated horizontal-axis wind-turbines. A hybrid approach is used that combines the advantages of a genetic algorithm and an inverse design method. This method is used to determine the optimum blade pitch and blade chord and twist distributions that maximize the annual energy production. To illustrate the method, a family of 25 wind turbines was designed to examine the sensitivity of annual energy production to changes in the rotor blade length and peak rotor power. Trends are revealed that should aid in the design of new rotors for existing turbines. In the second application, a series of five wind turbines was designed to determine the benefits of specifically tailoring wind turbine blades for the average wind speed at a particular site. The results have important practical implications related to rotors designed for the Midwest versus those where the average wind speed may be greater.
Application of a genetic algorithm to wind turbine design
Selig, M.S.; Coverstone-Carroll, V.L.
1996-03-01
This paper presents an optimization method for stall-regulated horizontal-axis wind turbines. A hybrid approach is used that combines the advantages of a genetic algorithm with an inverse design method. This method is used to determine the optimum blade pitch and blade chord and twist distributions that maximize the annual energy production. To illustrate the method, a family of 25 wind turbines was designed to examine the sensitivity of annual energy product to changes in the rotor blade length and peak rotor power. Trends are revealed that should aid in the design of new rotors for existing turbines. In the second application, five wind turbines were designed to determine the benefits of specifically tailoring wind turbine blades for the average wind speed at a particular site. The results have important practical implications related to rotors designed for the Midwestern US versus those where the average wind speed may be greater.
Alien Genetic Algorithm for Exploration of Search Space
NASA Astrophysics Data System (ADS)
Patel, Narendra; Padhiyar, Nitin
2010-10-01
Genetic Algorithm (GA) is a widely accepted population based stochastic optimization technique used for single and multi objective optimization problems. Various versions of modifications in GA have been proposed in last three decades mainly addressing two issues, namely increasing convergence rate and increasing probability of global minima. While both these. While addressing the first issue, GA tends to converge to a local optima and addressing the second issue corresponds the large computational efforts. Thus, to reduce the contradictory effects of these two aspects, we propose a modification in GA by adding an alien member in the population at every generation. Addition of an Alien member in the current population at every generation increases the probability of obtaining global minima at the same time maintaining higher convergence rate. With two test cases, we have demonstrated the efficacy of the proposed GA by comparing with the conventional GA.
Evolutionary Design of Rule Changing Artificial Society Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Wu, Yun; Kanoh, Hitoshi
Socioeconomic phenomena, cultural progress and political organization have recently been studied by creating artificial societies consisting of simulated agents. In this paper we propose a new method to design action rules of agents in artificial society that can realize given requests using genetic algorithms (GAs). In this paper we propose an efficient method for designing the action rules of agents that will constitute an artificial society that meets a specified demand by using a GAs. In the proposed method, each chromosome in the GA population represents a candidate set of action rules and the number of rule iterations. While a conventional method applies distinct rules in order of precedence, the present method applies a set of rules repeatedly for a certain period. The present method is aiming at both firm evolution of agent population and continuous action by that. Experimental results using the artificial society proved that the present method can generate artificial society which fills a demand in high probability.
Population Induced Instabilities in Genetic Algorithms for Constrained Optimization
NASA Astrophysics Data System (ADS)
Vlachos, D. S.; Parousis-Orthodoxou, K. J.
2013-02-01
Evolutionary computation techniques, like genetic algorithms, have received a lot of attention as optimization techniques but, although they exhibit a very promising potential in curing the problem, they have not produced a significant breakthrough in the area of systematic treatment of constraints. There are two mainly ways of handling the constraints: the first is to produce an infeasibility measure and add it to the general cost function (the well known penalty methods) and the other is to modify the mutation and crossover operation in a way that they only produce feasible members. Both methods have their drawbacks and are strongly correlated to the problem that they are applied. In this work, we propose a different treatment of the constraints: we induce instabilities in the evolving population, in a way that infeasible solution cannot survive as they are. Preliminary results are presented in a set of well known from the literature constrained optimization problems.
Automatic radiometric normalization with genetic algorithms and a Kriging model
NASA Astrophysics Data System (ADS)
Liu, Shou-Heng; Lin, Ching-Weei; Chen, Yie-Ruey; Tseng, Chih-Ming
2012-06-01
An automatic procedure of radiometric normalization is proposed for multi-temporal satellite image correction, with a modified genetic algorithm (GA) regression method and a spatially variant normalization model using the Kriging interpolation. The proposed procedure was tested on a synthetic altered image and an image pair from FORMOSAT-2; the results show that the GA method is more robust than the conventional PCA methods in high-resolution imaging, and that different regression-error evaluation models have different sensitivities to the linear regression parameters. A statistical comparison demonstrates that 1-km sampling spacing is able to successfully achieve the parameter spatial variation. Error validation on FORMOSAT-2 image pair shows it is a decent combination of radiometric normalization with GA estimation and a spatially variant parameter normalization model.
Strawberry Maturity Neural Network Detectng System Based on Genetic Algorithm
NASA Astrophysics Data System (ADS)
Xu, Liming
The quick and non-detective detection of agriculture product is one of the measures to increase the precision and productivity of harvesting and grading. Having analyzed H frequency of different maturities in different light intensities, the results show that H frequency for the same maturity has little influence in different light intensities; Under the same light intensity, three strawberry maturities are changing in order. After having confirmed the H frequency section to distinguish the different strawberry maturity, the triplelayer feed-forward neural network system to detect strawberry maturity was designed by using genetic algorithm. The test results show that the detecting precision ratio is 91.7%, it takes 160ms to distinguish one strawberry. Therefore, the online non-detective detecting the strawberry maturity could be realized.
Clustering online social network communities using genetic algorithms
Hajeer, Mustafa H; Dasgupta, Dipankar; Sanyal, Sugata
2013-01-01
To analyze the activities in an Online Social network (OSN), we introduce the concept of "Node of Attraction" (NoA) which represents the most active node in a network community. This NoA is identified as the origin/initiator of a post/communication which attracted other nodes and formed a cluster at any point in time. In this research, a genetic algorithm (GA) is used as a data mining method where the main objective is to determine clusters of network communities in a given OSN dataset. This approach is efficient in handling different type of discussion topics in our studied OSN - comments, emails, chat expressions, etc. and can form clusters according to one or more topics. We believe that this work can be useful in finding the source for spread of this GA-based clustering of online interactions and reports some results of experiments with real-world data and demonstrates the performance of proposed approach.
An Intelligent Model for Pairs Trading Using Genetic Algorithms
Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An
2015-01-01
Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice.
Genetic Algorithm Optimization of a Cost Competitive Hybrid Rocket Booster
NASA Technical Reports Server (NTRS)
Story, George
2015-01-01
Performance, reliability and cost have always been drivers in the rocket business. Hybrid rockets have been late entries into the launch business due to substantial early development work on liquid rockets and solid rockets. Slowly the technology readiness level of hybrids has been increasing due to various large scale testing and flight tests of hybrid rockets. One remaining issue is the cost of hybrids versus the existing launch propulsion systems. This paper will review the known state-of-the-art hybrid development work to date and incorporate it into a genetic algorithm to optimize the configuration based on various parameters. A cost module will be incorporated to the code based on the weights of the components. The design will be optimized on meeting the performance requirements at the lowest cost.
Hierarchical Stochastic Simulation Algorithm for SBML Models of Genetic Circuits
Watanabe, Leandro H.; Myers, Chris J.
2014-01-01
This paper describes a hierarchical stochastic simulation algorithm, which has been implemented within iBioSim, a tool used to model, analyze, and visualize genetic circuits. Many biological analysis tools flatten out hierarchy before simulation, but there are many disadvantages associated with this approach. First, the memory required to represent the model can quickly expand in the process. Second, the flattening process is computationally expensive. Finally, when modeling a dynamic cellular population within iBioSim, inlining the hierarchy of the model is inefficient since models must grow dynamically over time. This paper discusses a new approach to handle hierarchy on the fly to make the tool faster and more memory-efficient. This approach yields significant performance improvements as compared to the former flat analysis method. PMID:25506588
Genetic Algorithms and Nucleation in VIH-AIDS transition.
NASA Astrophysics Data System (ADS)
Barranon, Armando
2003-03-01
VIH to AIDS transition has been modeled via a genetic algorithm that uses boom-boom principle and where population evolution is simulated with a cellular automaton based on SIR model. VIH to AIDS transition is signed by nucleation of infected cells and low probability of infection are obtained for different mutation rates in agreement with clinical results. A power law is obtained with a critical exponent close to the critical exponent of cubic, spherical percolation, colossal magnetic resonance, Ising Model and liquid-gas phase transition in heavy ion collisions. Computations were carried out at UAM-A Supercomputing Lab and author acknowledges financial support from Division of CBI at UAM-A.
Maximizing microbial perchlorate degradation using a genetic algorithm: consortia optimization.
Kucharzyk, Katarzyna H; Soule, Terence; Hess, Thomas F
2013-09-01
Microorganisms in consortia perform many tasks more effectively than individual organisms and in addition grow more rapidly and in greater abundance. In this work, experimental datasets were assembled consisting of all possible selected combinations of perchlorate reducing strains of microorganisms and their perchlorate degradation rates were evaluated. A genetic algorithm (GA) methodology was successfully applied to define sets of microbial strains to achieve maximum rates of perchlorate degradation. Over the course of twenty generations of optimization using a GA, we saw a statistically significant 2.06 and 4.08-fold increase in average perchlorate degradation rates by consortia constructed using solely the perchlorate reducing bacteria (PRB) and by consortia consisting of PRB and accompanying organisms that did not degrade perchlorate, respectively. The comparison of kinetic rates constant in two types of microbial consortia additionally showed marked increases. PMID:23229741
Optimization and implementation of piezoelectric radiators using the genetic algorithm.
Bai, Mingsian R; Huang, Chinghong
2003-06-01
Very thin and small (45 mm x 35 mm x 0.35 mm) piezoelectric radiators have been developed in this research. The system is modeled by using the energy method in conjunction with the assumed-modes method. Electrical system, mechanical system, and acoustic loading have all been accounted for during the modeling stage. On the basis of the simulation model, the genetic algorithm (GA) is employed to optimize the overall configurations for a low resonance frequency and a large gain. The resulting designs are then implemented and evaluated experimentally. Performance indices for the experimental evaluation include the frequency response, the directional response, the sensitivity, and the efficiency. It is found in the experimental results that the piezoelectric radiators are able to produce comparable acoustical output with significantly less electrical input than the voice-coil panel speakers. PMID:12822792
Chiral metamaterial design using optimized pixelated inclusions with genetic algorithm
NASA Astrophysics Data System (ADS)
Akturk, Cemal; Karaaslan, Muharrem; Ozdemir, Ersin; Ozkaner, Vedat; Dincer, Furkan; Bakir, Mehmet; Ozer, Zafer
2015-03-01
Chiral metamaterials have been a research area for many researchers due to their polarization rotation properties on electromagnetic waves. However, most of the proposed chiral metamaterials are designed depending on experience or time-consuming inefficient simulations. A method is investigated for designing a chiral metamaterial with a strong and natural chirality admittance by optimizing a grid of metallic pixels through both sides of a dielectric sheet placed perpendicular to the incident wave by using a genetic algorithm (GA) technique based on finite element method solver. The effective medium parameters are obtained by using constitutive equations and S parameters. The proposed methodology is very efficient for designing a chiral metamaterial with the desired effective medium parameters. By using GA-based topology, it is proven that a chiral metamaterial can be designed and manufactured more easily and with a low cost.
Broadband omnidirectional antireflection coatings optimized by genetic algorithm.
Poxson, David J; Schubert, Martin F; Mont, Frank W; Schubert, E F; Kim, Jong Kyu
2009-03-15
An optimized graded-refractive-index (GRIN) antireflection (AR) coating with broadband and omnidirectional characteristics--as desired for solar cell applications--designed by a genetic algorithm is presented. The optimized three-layer GRIN AR coating consists of a dense TiO2 and two nanoporous SiO2 layers fabricated using oblique-angle deposition. The normal incidence reflectance of the three-layer GRIN AR coating averaged between 400 and 700 nm is 3.9%, which is 37% lower than that of a conventional single-layer Si3N4 coating. Furthermore, measured reflection over the 410-740 nm range and wide incident angles 40 degrees -80 degrees is reduced by 73% in comparison with the single-layer Si3N4 coating, clearly showing enhanced omnidirectionality and broadband characteristics of the optimized three-layer GRIN AR coating. PMID:19282913
An adaptive genetic algorithm for crystal structure prediction
Wu, Shunqing; Ji, Min; Wang, Cai-Zhuang; Nguyen, Manh Cuong; Zhao, Xin; Umemoto, K.; Wentzcovitch, R. M.; Ho, Kai-Ming
2013-10-31
We present a genetic algorithm (GA) for structural search that combines the speed of structure exploration by classical potentials with the accuracy of density functional theory (DFT) calculations in an adaptive and iterative way. This strategy increases the efficiency of the DFT-based GA by several orders of magnitude. This gain allows a considerable increase in the size and complexity of systems that can be studied by first principles. The performance of the method is illustrated by successful structure identifications of complex binary and ternary intermetallic compounds with 36 and 54 atoms per cell, respectively. The discovery of a multi-TPa Mg-silicate phase with unit cell containing up to 56 atoms is also reported. Such a phase is likely to be an essential component of terrestrial exoplanetary mantles.
Investigation for digital speckle correlation method based on improved genetic algorithm
NASA Astrophysics Data System (ADS)
Zhang, Ting; Chen, Haoyuan; Yang, Xiankun; Zheng, Xitao
2011-10-01
A digital speckle correlation method (DSCM) to measure surface displacements and strains have been developed. The correlate search algorithm plays an important role in DSCM. The traditional correlate search algorithms depend on initial value selection seriously. For this defect, this paper presents DSCM which uses genetic algorithm (GA) to measure surface deformation. In order to overcome the premature convergence of tradition genetic algorithm, this paper utilizes multi-parent crossover and adaptive mutation probability to perform genetic operation. The simulation demonstrates that improved GA increases the speed and the precision of search. With the application of DSCM in measuring deformation of composite laminates, the test reveals an agreement with the anticipated result.
Cloud identification using genetic algorithms and massively parallel computation
NASA Technical Reports Server (NTRS)
Buckles, Bill P.; Petry, Frederick E.
1996-01-01
As a Guest Computational Investigator under the NASA administered component of the High Performance Computing and Communication Program, we implemented a massively parallel genetic algorithm on the MasPar SIMD computer. Experiments were conducted using Earth Science data in the domains of meteorology and oceanography. Results obtained in these domains are competitive with, and in most cases better than, similar problems solved using other methods. In the meteorological domain, we chose to identify clouds using AVHRR spectral data. Four cloud speciations were used although most researchers settle for three. Results were remarkedly consistent across all tests (91% accuracy). Refinements of this method may lead to more timely and complete information for Global Circulation Models (GCMS) that are prevalent in weather forecasting and global environment studies. In the oceanographic domain, we chose to identify ocean currents from a spectrometer having similar characteristics to AVHRR. Here the results were mixed (60% to 80% accuracy). Given that one is willing to run the experiment several times (say 10), then it is acceptable to claim the higher accuracy rating. This problem has never been successfully automated. Therefore, these results are encouraging even though less impressive than the cloud experiment. Successful conclusion of an automated ocean current detection system would impact coastal fishing, naval tactics, and the study of micro-climates. Finally we contributed to the basic knowledge of GA (genetic algorithm) behavior in parallel environments. We developed better knowledge of the use of subpopulations in the context of shared breeding pools and the migration of individuals. Rigorous experiments were conducted based on quantifiable performance criteria. While much of the work confirmed current wisdom, for the first time we were able to submit conclusive evidence. The software developed under this grant was placed in the public domain. An extensive user's manual was written and distributed nationwide to scientists whose work might benefit from its availability. Several papers, including two journal articles, were produced.
NASA Astrophysics Data System (ADS)
Djurisic, Aleksandra B.; Elazar, Jovan M.; Rakic, A. D.
1997-11-01
The concept of parameter-space size adjustment is proposed in order to enable successful application of genetic algorithms to continuous optimization problems. Performance of genetic algorithms with six different combinations of selection and reproduction mechanisms, with and without parameter-space size adjustment, were severely tested on eleven multiminima test functions. An algorithm with the best performance was employed for the determination of the model parameters of the optical constants of Pt, Ni and Cr.
Aerodynamic Shape Optimization Using A Real-Number-Encoded Genetic Algorithm
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.
2001-01-01
A new method for aerodynamic shape optimization using a genetic algorithm with real number encoding is presented. The algorithm is used to optimize three different problems, a simple hill climbing problem, a quasi-one-dimensional nozzle problem using an Euler equation solver and a three-dimensional transonic wing problem using a nonlinear potential solver. Results indicate that the genetic algorithm is easy to implement and extremely reliable, being relatively insensitive to design space noise.
MECHANISTIC-BASED GENETIC ALGORITHM SEARCH ON A BEOWULF CLUSTER OF LINUX PCS
Hoffman, Forrest M.
MECHANISTIC-BASED GENETIC ALGORITHM SEARCH ON A BEOWULF CLUSTER OF LINUX PCS Jin-Ping Gwo), Beowulf Linux cluster. ABSTRACT A simple genetic algorithm (SGA) was implemented on a cluster of Linux PCs to environmental researchers and engineers. The Beowulf computer was built out of surplus personal computers at Oak
Cláudio M. N. A. Pereira; Celso M. F. Lapa
2003-01-01
This work extends the research related to genetic algorithms (GA) in core design optimization problems, which basic investigations were presented in previous work. Here we explore the use of the Island Genetic Algorithm (IGA), a coarse-grained parallel GA model, comparing its performance to that obtained by the application of a traditional non-parallel GA. The optimization problem consists on adjusting several
Genetic algorithm neural network model based on Coke Oven Gas Collector Pressure system
Ruiping Bai; Hongxing Li
2008-01-01
Coke-oven gas collector pressure control system is important in coke oven system. In this paper we identify the model of coke oven gas collectors pressure system and combine with genetic arithmetic and BP artificial neural network, a new model is presented in this article. Because of the limitation of BP algorithm, the genetic algorithm is used to find the fitness
AERODYNAMIC AND AEROACOUSTIC OPTIMIZATION OF AIRFOI LS VIA A PARALLEL GENETIC ALGORITHM
Brian R. Jones; William A. Crossley; Anastasios S. Lyrintzis
1998-01-01
A parallel genetic algorithm (GA) was used to generate, in a single run, a family of aerodynamically efficient, low-noise rotor blade designs representing th e Pareto optimal set. The n-branch tournament, uniform crossover genetic algorithm operates on twenty design variables, which constitute the control points for a spline representing the airfoil surface. The GA takes advantage of available computer resources
A Hybrid Approach to Vehicle Routing Using Neural Networks and Genetic Algorithms
Jean-yves Potvin; Danny Dubé; Christian Robillard
1996-01-01
A competitive neural network model and a genetic algorithm are used to improve the initialization and construction phase of a parallel insertion heuristic for the vehicle routing problem with time windows. The neural network identifies seed customers that are suitably distributed over the entire geographic area during the initialization phase, and the genetic algorithm finds good parameter settings in the
Constructive Induction and Genetic Algorithms for Learning Concepts with Complex Interaction
Shafti, Leila
of ap- plying Genetic Algorithms as a global search strategy for these methods and present MFE2/GA1 Genetic Algorithm's operators and compare MFE2/GA with greedy-based methods. We also performed experiments MFE2/GA is a modification to our previous method intro- duced in[27] Permission to make digital
2003-01-01
In this article, load frequency control (LFC) of a two area hydro system is studied. Optimum gain settings of different types of controllers are obtained using the genetic algorithm (GA) for a two area hydro power system. A brief sketch of the genetic algorithm (GA) is presented and its strategy as a method of control system design is discussed. Results
Huixin Tian; Zhizhong Mao; Shu Wang; Kun Li
2006-01-01
In this paper, a new soft sensor model of LF molten steel temperature is introduced after analyzing the process of LF refining and the factors of influencing molten steel temperature. A genetic algorithm combined with improved back-propagation (BP) neural network is used in this model. Genetic algorithm is used to optimize weight and bias values of BP network. The simulation
Shouyu Chen; Yu Guo; Dagang Wang
2006-01-01
On the ground of fuzzy optimum selection of BP neural network model, the paper introduces genetic algorithm to the model and presents an intelligent decision-making model based on fuzzy optimum selection of BP neural network model and mixed with genetic algorithm. A case proved that the intelligent decision-making model is efficient and robust in determining network topologic structure, accelerating convergence
Structure-Based Ligand Design by a Build-Up Approach and Genetic Algorithm
Caflisch, Amedeo
Structure-Based Ligand Design by a Build-Up Approach and Genetic Algorithm Search in Conformational: 19561970, 2001 Keywords: ligand docking; structure-based design; genetic algorithm; implicit solvation; peptidomimetics Introduction Computer-aided structure-based ligand design is a multidisciplinary and challenging
New knowledge-based genetic algorithm for excavator boom structural optimization
NASA Astrophysics Data System (ADS)
Hua, Haiyan; Lin, Shuwen
2014-03-01
Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.
VOLUMETRIC REPRESENTATION OF A CLOUD OF POINTS WITH BLOBBIES USING A GENETIC ALGORITHM
Egli, Richard
VOLUMETRIC REPRESENTATION OF A CLOUD OF POINTS WITH BLOBBIES USING A GENETIC ALGORITHM Chaumont N is an aggregate of blobbies, computed using a genetic algorithm. The choice of this model is motivated a bunny and a cat. * The research of the second author was supported in part by a grant from the Natural
A Comparative Study of Immune System Based Genetic Algorithms in Dynamic Environments
Yang, Shengxiang
A Comparative Study of Immune System Based Genetic Algorithms in Dynamic Environments Shengxiang. This paper investigates several GAs inspired by the ideas of biological immune system and transformation to the immune system based genetic algorithm to deal with dynamic environments. Using a series of systematically
Using genetic algorithms to select and create features for pattern classification. Technical report
Chang, E.I.; Lippmann, R.P.
1991-03-11
Genetic algorithms were used to select and create features and to select reference exemplar patterns for machine vision and speech pattern classification tasks. On a 15-feature machine-vision inspection task, it was found that genetic algorithms performed no better than conventional approaches to feature selection but required much more computation. For a speech recognition task, genetic algorithms required no more computation time than traditional approaches but reduced the number of features required by a factor of five (from 153 to 33 features). On a difficult artificial machine-vision task, genetic algorithms were able to create new features (polynomial functions of the original features) that reduced classification error rates from 10 to almost 0 percent. Neural net and nearest-neighbor classifiers were unable to provide such low error rates using only the original features. Genetic algorithms were also used to reduce the number of reference exemplar patterns and to select the value of k for a k-nearest-neighbor classifier. On a .338 training pattern vowel recognition problem with 10 classes, genetic algorithms simultaneously reduced the number of stored exemplars from 338 to 63 and selected k without significantly decreasing classification accuracy. In all applications, genetic algorithms were easy to apply and found good solutions in many fewer trials than would be required by an exhaustive search. Run times were long but not unreasonable. These results suggest that genetic algorithms may soon be practical for pattern classification problems as faster serial and parallel computers are developed.
Combining CaseBased Memory with Genetic Algorithm Search for Competent Game AI
Louis, Sushil J.
extracted during human gameplay. This paper describes the design of a caseinjected Genetic AlgorithmCombining CaseBased Memory with Genetic Algorithm Search for Competent Game AI Sushil J Louis and Chris Miles Evolutionary Computing Systems Laboratory Department of Computer Science and Engineering
Combining Case-Based Memory with Genetic Algorithm Search for Competent Game AI
Louis, Sushil J.
extracted during human game-play. This paper describes the design of a case-injected Genetic AlgorithmCombining Case-Based Memory with Genetic Algorithm Search for Competent Game AI Sushil J Louis and Chris Miles Evolutionary Computing Systems Laboratory Department of Computer Science and Engineering
Using the genetic algorithm to find snake-in-the-box codes
Walter D. Potter; Robert W. Robinson; John A. Miller; Krys Kochut; D. Z. Redys
1994-01-01
Genetic Algorithms are heuristic search schemes based on a model of Darwinian evolution. Although not guaranteed to findthe optimal solution, genetic algorithms have been shown to be effective at finding near optimal and, in some cases, optimal solutions to combinatorially explosive problems. Finding a maximal length snake, a list of vertices satisfy- ing specific constraints, in an n -dimensional hypercube,
A novel approach for synthetic aperture radar image processing based on Genetic Algorithm
M. Emre Aydemir; T. Gunel; Ism Erer; S. Kurnaz
2003-01-01
In this study, an evolutionary computing algorithm is utilized for data preparation and analysis of synthetic aperture radar (SAR) imagery for planetary geology. Since its invention by J.H. Holland in the 1990s, the Genetic Algorithm (GA) has already gained popularity in a wide range of engineering applications. The genetic approach is used for processing of SAR imagery to find a
A Genetic Algorithm Search & Optimization Technique for the Spectroscopic Determination of
Louis, Sushil J.
A Genetic Algorithm Search & Optimization Technique for the Spectroscopic Determination and their parameterization. In this connection, we present the implementation of a Niched Pareto Genetic Algorithm technique employed to determine time-histories of core-averaged Te and Ne in Ar-doped ICF implosions. ·The next step
Genetic Algorithms based Parameter Identification of Yeast Fed-Batch Cultivation
Borissova, Daniela
Genetic Algorithms based Parameter Identification of Yeast Fed-Batch Cultivation Maria Angelova of a fermentation process. Altogether eight realizations of genetic algorithms have been presented - four of simple the others. 1 INTRODUCTION Fermentation processes (FP) are widely used in different branches of industry, i
MEMS Design Synthesis: Integrating Case-based Reasoning and Multi-objective Genetic Algorithms
Agogino, Alice M.
MEMS Design Synthesis: Integrating Case-based Reasoning and Multi-objective Genetic Algorithms-Electro-Mechanical Systems (MEMS) design tool that uses a multi-objective genetic algorithm (MOGA) to synthesize and optimize conceptual designs. CBR utilizes previously successful MEMS designs and sub-assemblies as building blocks
Real-Coded Genetic Algorithm for Rule-Based Flood Control Reservoir Management
Fi-John Chang; Li Chen
1998-01-01
Genetic algorithms (GAs) have been fairly successful in a diverse range of optimization problems, providing an efficient and robust way for guiding a search even in a complex system and in the absence of domain knowledge. In this paper, two types of genetic algorithms, real-coded and binary-coded, are examined for function optimization and applied to the optimization of a flood
Lindner, Douglas K.
Design Optimization of Power Electronics Circuits using Genetic Algorithms A Boost PFC Converter with a developed software tool for designing a low-cost boost power factor correction (PFC) front-end converter.15 kW unit are presented. Index Terms -- Design, optimization, genetic algorithm, boost, PFC, EMI. I
Efficient hybrid distributed genetic algorithms for wind turbine positioning in large wind farms
Hou-Sheng Huang
2009-01-01
An efficient hybrid distributed genetic algorithm is proposed to determine the proper number and locations of wind turbines in large wind farms. The objective of this optimal process is to find a solution that maximizes the annual profit obtained from a wind farm. It is well-known that genetic algorithms are good for global searches, but are weak for local searches.
C. M. Kishtawal; Falguni Patadia; Randhir Singh; Sujit Basu; M. S. Narayanan; P. C. Joshi
2005-01-01
An automatic method for intensity estimation of tropical cyclones using multi-channel observations from TRMM Microwave Imager (TMI) is developed using a non-linear data fitting approach called Genetic Algorithm. The intensity estimation technique SIEGA (Storm Intensity Estimation using Genetic Algorithm) uses only 9 simple statistical variables based on TMI observations and does not require any subjective input except the center of
András Tompos; József L. Margitfalvi; Erno ý Tfirst; Lajos Végvári
2006-01-01
In this study two catalyst library optimization methods, the Holographic Research Strategy (HRS) and the Genetic Algorithm (GA) were compared based on their ability to find the optimum compositions in a given multi-dimensional experimental space. Results obtained in three different case studies were used to investigate both the rate and the certainty of the optimum search. In these case studies
Designing teams of unattended ground sensors using genetic algorithms
NASA Astrophysics Data System (ADS)
Yilmaz, Ayse S.; McQuay, Brian N.; Wu, Annie S.; Sciortino, John C., Jr.
2004-04-01
Improvements in sensor capabilities have driven the need for automated sensor allocation and management systems. Such systems provide a penalty-free test environment and valuable input to human operators by offering candidate solutions. These abilities lead, in turn, to savings in manpower and time. Determining an optimal team of cooperating sensors for military operations is a challenging task. There is a tradeoff between the desire to decrease the cost and the need to increase the sensing capabilities of a sensor suite. This work focuses on unattended ground sensor networks consisting of teams of small, inexpensive sensors. Given a possible configuration of enemy radar, our goal isto generate sensor suites that monitor as many enemy radar as possible while minimizing cost. In previous work, we have shown that genetic algorithms (GAs) can be used to evolve successful teams of sensors for this problem. This work extends our previous work in two ways: we use an improved simulator containing a more accurate model of radar and sensor capabilities for out fitness evaluations and we introduce two new genetic operators, insertion and deletion, that are expected to improve the GA's fine tuning abilities. Empirical results show that our GA approach produces near optimal results under a variety of enemy radar configurations using sensors with varying capabilities. Detection percentage remains stable regardless of changes in the enemy radar placements.
Rajeev Motwani; Prabhakax Raghavan
1995-01-01
The last decade has witnessed a tremendous growth in the area of randomized algorithms.During this period, randomized algorithms went from being a tool in computational number theory to finding widespread application in many types of algorithms. Two benefits of randomization have spearheaded this growth: simplicity and speed. For many applications, a randomized algorithm is the simplest algorithm available, or the
Genetic algorithms applied to nonlinear and complex domains
Barash, D; Woodin, A E
1999-06-01
The dissertation, titled ''Genetic Algorithms Applied to Nonlinear and Complex Domains'', describes and then applies a new class of powerful search algorithms (GAS) to certain domains. GAS are capable of solving complex and nonlinear problems where many parameters interact to produce a ''final'' result such as the optimization of the laser pulse in the interaction of an atom with an intense laser field. GAS can very efficiently locate the global maximum by searching parameter space in problems which are unsuitable for a search using traditional methods. In particular, the dissertation contains new scientific findings in two areas. First, the dissertation examines the interaction of an ultra-intense short laser pulse with atoms. GAS are used to find the optimal frequency for stabilizing atoms in the ionization process. This leads to a new theoretical formulation, to explain what is happening during the ionization process and how the electron is responding to finite (real-life) laser pulse shapes. It is shown that the dynamics of the process can be very sensitive to the ramp of the pulse at high frequencies. The new theory which is formulated, also uses a novel concept (known as the (t,t') method) to numerically solve the time-dependent Schrodinger equation Second, the dissertation also examines the use of GAS in modeling decision making problems. It compares GAS with traditional techniques to solve a class of problems known as Markov Decision Processes. The conclusion of the dissertation should give a clear idea of where GAS are applicable, especially in the physical sciences, in problems which are nonlinear and complex, i.e. difficult to analyze by other means.
Genetic algorithms applied to nonlinear and complex domains
Barash, D; Woodin, A E
1999-06-01
The dissertation, titled ''Genetic Algorithms Applied to Nonlinear and Complex Domains'', describes and then applies a new class of powerful search algorithms (GAS) to certain domains. GAS are capable of solving complex and nonlinear problems where many parameters interact to produce a final result such as the optimization of the laser pulse in the interaction of an atom with an intense laser field. GAS can very efficiently locate the global maximum by searching parameter space in problems which are unsuitable for a search using traditional methods. In particular, the dissertation contains new scientific findings in two areas. First, the dissertation examines the interaction of an ultra-intense short laser pulse with atoms. GAS are used to find the optimal frequency for stabilizing atoms in the ionization process. This leads to a new theoretical formulation, to explain what is happening during the ionization process and how the electron is responding to finite (real-life) laser pulse shapes. It is shown that the dynamics of the process can be very sensitive to the ramp of the pulse at high frequencies. The new theory which is formulated, also uses a novel concept (known as the (t,t') method) to numerically solve the time-dependent Schrodinger equation Second, the dissertation also examines the use of GAS in modeling decision making problems. It compares GAS with traditional techniques to solve a class of problems known as Markov Decision Processes. The conclusion of the dissertation should give a clear idea of where GAS are applicable, especially in the physical sciences, in problems which are nonlinear and complex, i.e. difficult to analyze by other means.
A genetic algorithm for optical flow estimation Marco Tagliasacchi *
Tagliasacchi, Marco
; Motion estimation 1. Introduction We refer to the optical flow as the velocity field which warps one algorithms [10] exploit locally adaptive parametric motion models to drive the optical flow estimation
GenMin: An enhanced genetic algorithm for global optimization
NASA Astrophysics Data System (ADS)
Tsoulos, Ioannis G.; Lagaris, I. E.
2008-06-01
A new method that employs grammatical evolution and a stopping rule for finding the global minimum of a continuous multidimensional, multimodal function is considered. The genetic algorithm used is a hybrid genetic algorithm in conjunction with a local search procedure. We list results from numerical experiments with a series of test functions and we compare with other established global optimization methods. The accompanying software accepts objective functions coded either in Fortran 77 or in C++. Program summaryProgram title: GenMin Catalogue identifier: AEAR_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAR_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 35 810 No. of bytes in distributed program, including test data, etc.: 436 613 Distribution format: tar.gz Programming language: GNU-C++, GNU-C, GNU Fortran 77 Computer: The tool is designed to be portable in all systems running the GNU C++ compiler Operating system: The tool is designed to be portable in all systems running the GNU C++ compiler RAM: 200 KB Word size: 32 bits Classification: 4.9 Nature of problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a nonlinear system of equations via optimization, employing a least squares type of objective, one may encounter many local minima that do not correspond to solutions (i.e. they are far from zero). Solution method: Grammatical evolution and a stopping rule. Running time: Depending on the objective function. The test example given takes only a few seconds to run.
Cláudio M. N. A. Pereira; Celso M. F. Lapa
2003-01-01
In this work, we focus the application of an Island Genetic Algorithm (IGA), a coarse-grained parallel genetic algorithm (PGA) model, to a Nuclear Power Plant (NPP) Auxiliary Feedwater System (AFWS) surveillance tests policy optimization. Here, the main objective is to outline, by means of comparisons, the advantages of the IGA over the simple (non-parallel) genetic algorithm (GA), which has been
Hirsh, Haym
optimizer. Moreover, the results suggest that the modification makes the genetic algorithm less sensitiveIn Proceedings of the 1997 International Conference on Genetic Algorithms Using CaseBased Learning to Improve GeneticAlgorithmBased Design Optimization Khaled Rasheed Computer Science Department Rutgers
Optimization on robot arm machining by using genetic algorithms
NASA Astrophysics Data System (ADS)
Liu, Tung-Kuan; Chen, Chiu-Hung; Tsai, Shang-En
2007-12-01
In this study, an optimization problem on the robot arm machining is formulated and solved by using genetic algorithms (GAs). The proposed approach adopts direct kinematics model and utilizes GA's global search ability to find the optimum solution. The direct kinematics equations of the robot arm are formulated and can be used to compute the end-effector coordinates. Based on these, the objective of optimum machining along a set of points can be evolutionarily evaluated with the distance between machining points and end-effector positions. Besides, a 3D CAD application, CATIA, is used to build up the 3D models of the robot arm, work-pieces and their components. A simulated experiment in CATIA is used to verify the computation results first and a practical control on the robot arm through the RS232 port is also performed. From the results, this approach is proved to be robust and can be suitable for most machining needs when robot arms are adopted as the machining tools.
Improved satellite image compression and reconstruction via genetic algorithms
NASA Astrophysics Data System (ADS)
Babb, Brendan; Moore, Frank; Peterson, Michael; Lamont, Gary
2008-10-01
A wide variety of signal and image processing applications, including the US Federal Bureau of Investigation's fingerprint compression standard [3] and the JPEG-2000 image compression standard [26], utilize wavelets. This paper describes new research that demonstrates how a genetic algorithm (GA) may be used to evolve transforms that outperform wavelets for satellite image compression and reconstruction under conditions subject to quantization error. The new approach builds upon prior work by simultaneously evolving real-valued coefficients representing matched forward and inverse transform pairs at each of three levels of a multi-resolution analysis (MRA) transform. The training data for this investigation consists of actual satellite photographs of strategic urban areas. Test results show that a dramatic reduction in the error present in reconstructed satellite images may be achieved without sacrificing the compression capabilities of the forward transform. The transforms evolved during this research outperform previous start-of-the-art solutions, which optimized coefficients for the reconstruction transform only. These transforms also outperform wavelets, reducing error by more than 0.76 dB at a quantization level of 64. In addition, transforms trained using representative satellite images do not perform quite as well when subsequently tested against images from other classes (such as fingerprints or portraits). This result suggests that the GA developed for this research is automatically learning to exploit specific attributes common to the class of images represented in the training population.
Topology-changing shape optimization with the genetic algorithm
NASA Astrophysics Data System (ADS)
Lamberson, Steven E., Jr.
The goal is to take a traditional shape optimization problem statement and modify it slightly to allow for prescribed changes in topology. This modification enables greater flexibility in the choice of parameters for the topology optimization problem, while improving the direct physical relevance of the results. This modification involves changing the optimization problem statement from a nonlinear programming problem into a form of mixed-discrete nonlinear programing problem. The present work demonstrates one possible way of using the Genetic Algorithm (GA) to solve such a problem, including the use of "masking bits" and a new modification to the bit-string affinity (BSA) termination criterion specifically designed for problems with "masking bits." A simple ten-bar truss problem proves the utility of the modified BSA for this type of problem. A more complicated two dimensional bracket problem is solved using both the proposed approach and a more traditional topology optimization approach (Solid Isotropic Microstructure with Penalization or SIMP) to enable comparison. The proposed approach is able to solve problems with both local and global constraints, which is something traditional methods cannot do. The proposed approach has a significantly higher computational burden --- on the order of 100 times larger than SIMP, although the proposed approach is able to offset this with parallel computing.
Toward Developing Genetic Algorithms to Aid in Critical Infrastructure Modeling
Not Available
2007-05-01
Today’s society relies upon an array of complex national and international infrastructure networks such as transportation, telecommunication, financial and energy. Understanding these interdependencies is necessary in order to protect our critical infrastructure. The Critical Infrastructure Modeling System, CIMS©, examines the interrelationships between infrastructure networks. CIMS© development is sponsored by the National Security Division at the Idaho National Laboratory (INL) in its ongoing mission for providing critical infrastructure protection and preparedness. A genetic algorithm (GA) is an optimization technique based on Darwin’s theory of evolution. A GA can be coupled with CIMS© to search for optimum ways to protect infrastructure assets. This includes identifying optimum assets to enforce or protect, testing the addition of or change to infrastructure before implementation, or finding the optimum response to an emergency for response planning. This paper describes the addition of a GA to infrastructure modeling for infrastructure planning. It first introduces the CIMS© infrastructure modeling software used as the modeling engine to support the GA. Next, the GA techniques and parameters are defined. Then a test scenario illustrates the integration with CIMS© and the preliminary results.
Prediction of plasma processes using neural network and genetic algorithm
NASA Astrophysics Data System (ADS)
Kim, Byungwhan; Bae, Jungki
2005-10-01
Using genetic algorithm (GA) and backpropagation neural network (BPNN), computer models of plasma processes were constructed. The GA was applied to optimize five training factors simultaneously. The presented technique was evaluated with plasma etch data, characterized by a statistical experimental design. The etching was conducted in an inductively coupled plasma etch system. The etch outputs to model include aluminum (Al) etch rate, Al selectivity, silica profile angle, and DC bias. GA-BPNN models demonstrated improved predictions of more than 20% for all etch outputs but the DC bias. This indicates that a simultaneous optimization of training factors is more effective in improving the prediction performance of BPNN model than a sequential optimization of individual training factor. Compared to GA-BPNN models constructed in a previous training set, the presented models also yielded a much improved prediction of more than 35% for all etch outputs. The proven improvement indicates that the presented training set is more effective to improve GA-BPNN models.
Suboptimum binary phase code search using a genetic algorithm
NASA Astrophysics Data System (ADS)
Mora, Jorge L.; Flores, Benjamin C.; Kreinovich, Vladik
1994-09-01
Optimum binary phase codes of length L are characterized by an autocorrelation function R((tau) ) with uniform sidelobes of level 1/L with respect to the main lobe. These optimum binary codes are called Barker codes. Binary phase codes that exhibit minimum peak sidelobes above 1/L are called suboptimum. A genetic algorithm is implemented to conduct the search for optimum and suboptimum binary codes of a given length L. In this approach, several different fitness functions are considered. These fitness functions are based on sidelobe level (SLL) and generalized entropy measures. To verify that these are reasonable fitness functions, they are first applied to sequence lengths for which optimum codes are known to exist. It is shown that if L is such that a Barker code exists, and S is a generalized entropy measure, then the Barker codes are the only ones that give the minimum value for S. It is also shown that the proposed binary phase code search is efficient for large values of L.
Development of hybrid genetic algorithms for product line designs.
Balakrishnan, P V Sundar; Gupta, Rakesh; Jacob, Varghese S
2004-02-01
In this paper, we investigate the efficacy of artificial intelligence (AI) based meta-heuristic techniques namely genetic algorithms (GAs), for the product line design problem. This work extends previously developed methods for the single product design problem. We conduct a large scale simulation study to determine the effectiveness of such an AI based technique for providing good solutions and bench mark the performance of this against the current dominant approach of beam search (BS). We investigate the potential advantages of pursuing the avenue of developing hybrid models and then implement and study such hybrid models using two very distinct approaches: namely, seeding the initial GA population with the BS solution, and employing the BS solution as part of the GA operator's process. We go on to examine the impact of two alternate string representation formats on the quality of the solutions obtained by the above proposed techniques. We also explicitly investigate a critical managerial factor of attribute importance in terms of its impact on the solutions obtained by the alternate modeling procedures. The alternate techniques are then evaluated, using statistical analysis of variance, on a fairy large number of data sets, as to the quality of the solutions obtained with respect to the state-of-the-art benchmark and in terms of their ability to provide multiple, unique product line options. PMID:15372718
Inner Random Restart Genetic Algorithm for Practical Delivery Schedule Optimization
NASA Astrophysics Data System (ADS)
Sakurai, Yoshitaka; Takada, Kouhei; Onoyama, Takashi; Tsukamoto, Natsuki; Tsuruta, Setsuo
A delivery route optimization that improves the efficiency of real time delivery or a distribution network requires solving several tens to hundreds but less than 2 thousands cities Traveling Salesman Problems (TSP) within interactive response time (less than about 3 second), with expert-level accuracy (less than about 3% of error rate). Further, to make things more difficult, the optimization is subjects to special requirements or preferences of each various delivery sites, persons, or societies. To meet these requirements, an Inner Random Restart Genetic Algorithm (Irr-GA) is proposed and developed. This method combines meta-heuristics such as random restart and GA having different types of simple heuristics. Such simple heuristics are 2-opt and NI (Nearest Insertion) methods, each applied for gene operations. The proposed method is hierarchical structured, integrating meta-heuristics and heuristics both of which are multiple but simple. This method is elaborated so that field experts as well as field engineers can easily understand to make the solution or method easily customized and extended according to customers' needs or taste. Comparison based on the experimental results and consideration proved that the method meets the above requirements more than other methods judging from not only optimality but also simplicity, flexibility, and expandability in order for this method to be practically used.
The Adaptive Analysis of Visual Cognition using Genetic Algorithms
Cook, Robert G.; Qadri, Muhammad A. J.
2014-01-01
Two experiments used a novel, open-ended, and adaptive test procedure to examine visual cognition in animals. Using a genetic algorithm, a pigeon was tested repeatedly from a variety of different initial conditions for its solution to an intermediate brightness search task. On each trial, the animal had to accurately locate and peck a target element of intermediate brightness from among a variable number of surrounding darker and lighter distractor elements. Displays were generated from six parametric variables, or genes (distractor number, element size, shape, spacing, target brightness, distractor brightness). Display composition changed over time, or evolved, as a function of the bird’s differential accuracy within the population of values for each gene. Testing three randomized initial conditions and one set of controlled initial conditions, element size and number of distractors were identified as the most important factors controlling search accuracy, with distractor brightness, element shape, and spacing making secondary contributions. The resulting changes in this multidimensional stimulus space suggested the existence of a set of conditions that the bird repeatedly converged upon regardless of initial conditions. This psychological “attractor” represents the cumulative action of the cognitive operations used by the pigeon in solving and performing this search task. The results are discussed regarding their implications for visual cognition in pigeons and the usefulness of adaptive, subject-driven experimentation for investigating human and animal cognition more generally. PMID:24000905
Constrained genetic algorithms for optimizing multi-use reservoir operation
NASA Astrophysics Data System (ADS)
Chang, Li-Chiu; Chang, Fi-John; Wang, Kuo-Wei; Dai, Shin-Yi
2010-08-01
To derive an optimal strategy for reservoir operations to assist the decision-making process, we propose a methodology that incorporates the constrained genetic algorithm (CGA) where the ecological base flow requirements are considered as constraints to water release of reservoir operation when optimizing the 10-day reservoir storage. Furthermore, a number of penalty functions designed for different types of constraints are integrated into reservoir operational objectives to form the fitness function. To validate the applicability of this proposed methodology for reservoir operations, the Shih-Men Reservoir and its downstream water demands are used as a case study. By implementing the proposed CGA in optimizing the operational performance of the Shih-Men Reservoir for the last 20 years, we find this method provides much better performance in terms of a small generalized shortage index (GSI) for human water demands and greater ecological base flows for most of the years than historical operations do. We demonstrate the CGA approach can significantly improve the efficiency and effectiveness of water supply capability to both human and ecological base flow requirements and thus optimize reservoir operations for multiple water users. The CGA can be a powerful tool in searching for the optimal strategy for multi-use reservoir operations in water resources management.
Shape: automatic conformation prediction of carbohydrates using a genetic algorithm
2009-01-01
Background Detailed experimental three dimensional structures of carbohydrates are often difficult to acquire. Molecular modelling and computational conformation prediction are therefore commonly used tools for three dimensional structure studies. Modelling procedures generally require significant training and computing resources, which is often impractical for most experimental chemists and biologists. Shape has been developed to improve the availability of modelling in this field. Results The Shape software package has been developed for simplicity of use and conformation prediction performance. A trivial user interface coupled to an efficient genetic algorithm conformation search makes it a powerful tool for automated modelling. Carbohydrates up to a few hundred atoms in size can be investigated on common computer hardware. It has been shown to perform well for the prediction of over four hundred bioactive oligosaccharides, as well as compare favourably with previously published studies on carbohydrate conformation prediction. Conclusion The Shape fully automated conformation prediction can be used by scientists who lack significant modelling training, and performs well on computing hardware such as laptops and desktops. It can also be deployed on computer clusters for increased capacity. The prediction accuracy under the default settings is good, as it agrees well with experimental data and previously published conformation prediction studies. This software is available both as open source and under commercial licenses. PMID:20298520
Horizontal axis wind turbine systems: optimization using genetic algorithms
NASA Astrophysics Data System (ADS)
Diveux, T.; Sebastian, P.; Bernard, D.; Puiggali, J. R.; Grandidier, J. Y.
2001-10-01
A method for the optimization of a grid-connected wind turbine system is presented. The behaviour of the system components is coupled in a non-linear way, and optimization must take into account technical and economical aspects of the complete system design. The annual electrical energy cost is estimated using a cost model for the wind turbine rotor, nacelle and tower and an energy output model based on the performance envelopes of the power coefficient of the rotor, CP, on the Weibull parameters k and c and on the power law coefficient of the wind profile. In this study the site is defined with these three parameters and the extreme wind speed Vmax. The model parameters vary within a range of possible values. Other elements of the project (foundation, grid connection, financing cost, etc.) are taken into account through coefficients. The optimal values of the parameters are determined using genetic algorithms, which appear to be efficient for such a problem. These optimal values were found to be very different for a Mediterranean site and a northern European site using our numerical model. Optimal wind turbines at the Mediterranean sites considered in this article have an excellent profitability compared with reference northern European wind turbines. Most of the existing wind turbines appear to be well designed for northern European sites but not for Mediterranean sites.
Improvement of unsupervised texture classification based on genetic algorithms
NASA Astrophysics Data System (ADS)
Okumura, Hiroshi; Togami, Yuuki; Arai, Kohei
2004-11-01
At the previous conference, the authors are proposed a new unsupervised texture classification method based on the genetic algorithms (GA). In the method, the GA are employed to determine location and size of the typical textures in the target image. The proposed method consists of the following procedures: 1) the determination of the number of classification category; 2) each chromosome used in the GA consists of coordinates of center pixel of each training area candidate and those size; 3) 50 chromosomes are generated using random number; 4) fitness of each chromosome is calculated; the fitness is the product of the Classification Reliability in the Mixed Texture Cases (CRMTC) and the Stability of NZMV against Scanning Field of View Size (SNSFS); 5) in the selection operation in the GA, the elite preservation strategy is employed; 6) in the crossover operation, multi point crossover is employed and two parent chromosomes are selected by the roulette strategy; 7) in mutation operation, the locuses where the bit inverting occurs are decided by a mutation rate; 8) go to the procedure 4. However, this method has not been automated because it requires not only target image but also the number of categories for classification. In this paper, we describe some improvement for implementation of automated texture classification. Some experiments are conducted to evaluate classification capability of the proposed method by using images from Brodatz's photo album and actual airborne multispectral scanner. The experimental results show that the proposed method can select appropriate texture samples and can provide reasonable classification results.
Optimal design of passive linear suspension using genetic algorithm
NASA Astrophysics Data System (ADS)
Alkhatib, R.; Nakhaie Jazar, G.; Golnaraghi, M. F.
2004-08-01
In this paper the genetic algorithm (GA) method is applied to the optimization problem of a linear one-degree-of-freedom (1-DOF) vibration isolator mount and the method is extended to the optimization of a linear quarter car suspension model. A novel criterion for selecting optimal suspension parameters is presented. An optimal relationship between the root mean square (RMS) of the absolute acceleration and the RMS of the relative displacement is found. Although the systems are linear, it is difficult to find such optimal relation analytically. The optimum solution is obtained numerically by utilizing GA and employing a cost function that seeks minimizing absolute acceleration RMS sensitivity to changes in relative displacement RMS. The combination of RMS and absolute acceleration sensitivity minimization produces optimal suspension that is robust to broadband frequency excitation. The GA method increases the probability of finding the global optimum solution and avoids convergence to a local minimum which is a drawback of gradient-based methods. Given allowable mount relative displacement (working space), designers can use the results to specify the optimal mount and suspension. The cost function employed can be extended to optimize multi-DOF (MDOF) and non-linear vibrating mechanical systems in frequency domain. Applying the method to a linear quarter car model illustrates the applicability of the method to MDOF systems. An example is given to demonstrate the optimality of the solution obtained by the GA technique.
Genetic Local Search Algorithm for Optimization Design of Diffractive Optical Elements
NASA Astrophysics Data System (ADS)
Zhou, Guangya; Chen, Yixin; Wang, Zongguang; Song, Hongwei
1999-07-01
We propose a genetic local search algorithm (GLSA) for the optimization design of diffractive optical elements (DOE s). This hybrid algorithm incorporates advantages of both genetic algorithm (GA) and local search techniques. It appears better able to locate the global minimum compared with a canonical GA. Sample cases investigated here include the optimization design of binary-phase Dammann gratings, continuous surface-relief grating array generators, and a uniform top-hat focal plane intensity profile generator. Two GLSA s whose incorporated local search techniques are the hill-climbing method and the simulated annealing algorithm are investigated. Numerical experimental results demonstrate that the proposed algorithm is highly efficient and robust. DOE s that have high diffraction efficiency and excellent uniformity can be achieved by use of the algorithm we propose.
An induction motor servo drive using sliding-mode controller with genetic algorithm
Faa-Jeng Lin; Wen-Der Chou
2003-01-01
An adaptive sliding-mode controller based on real-time genetic algorithms (GAs) is developed for an induction motor (IM) servo drive in this paper. First, an adaptive sliding-mode controller with an integral-operation switching surface is investigated, in which a simple adaptive algorithm is utilized to estimate the bound of uncertainties. Since the adaptation gain in the adaptive algorithm is fixed, if the
Identifying damage in spherical laminate shells by using a hybrid real-parameter genetic algorithm
Rong-Song He; Shun-Fa Hwang
2007-01-01
A hybrid algorithm combining an adaptive real-parameter genetic algorithm with simulated annealing is proposed to detect damage in a simply supported equal-sided sector of a spherical laminate shell. The proposed algorithm uses the data of natural frequencies and mode shapes. A single damage example and a two-damage example are investigated. To simulate the error on the measured data, three error
In-Space Radiator Shape Optimization using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Hull, Patrick V.; Kittredge, Ken; Tinker, Michael; SanSoucie, Michael
2006-01-01
Future space exploration missions will require the development of more advanced in-space radiators. These radiators should be highly efficient and lightweight, deployable heat rejection systems. Typical radiators for in-space heat mitigation commonly comprise a substantial portion of the total vehicle mass. A small mass savings of even 5-10% can greatly improve vehicle performance. The objective of this paper is to present the development of detailed tools for the analysis and design of in-space radiators using evolutionary computation techniques. The optimality criterion is defined as a two-dimensional radiator with a shape demonstrating the smallest mass for the greatest overall heat transfer, thus the end result is a set of highly functional radiator designs. This cross-disciplinary work combines topology optimization and thermal analysis design by means of a genetic algorithm The proposed design tool consists of the following steps; design parameterization based on the exterior boundary of the radiator, objective function definition (mass minimization and heat loss maximization), objective function evaluation via finite element analysis (thermal radiation analysis) and optimization based on evolutionary algorithms. The radiator design problem is defined as follows: the input force is a driving temperature and the output reaction is heat loss. Appropriate modeling of the space environment is added to capture its effect on the radiator. The design parameters chosen for this radiator shape optimization problem fall into two classes, variable height along the width of the radiator and a spline curve defining the -material boundary of the radiator. The implementation of multiple design parameter schemes allows the user to have more confidence in the radiator optimization tool upon demonstration of convergence between the two design parameter schemes. This tool easily allows the user to manipulate the driving temperature regions thus permitting detailed design of in-space radiators for unique situations. Preliminary results indicate an optimized shape following that of the temperature distribution regions in the "cooler" portions of the radiator. The results closely follow the expected radiator shape.
NASA Astrophysics Data System (ADS)
Qin, Zujun; Zhou, Xiaojun; Li, Fadan; Wu, Haocheng; Zou, Zili
2008-01-01
In this paper, we proposed a novel numerical algorithm for nth-order cascaded Raman fiber lasers (CRFLs) with the combination of genetic algorithm (GA) and shooting method. Although shooting method possesses fast speed in solving nonlinear two-point boundary-value ordinary differential equations, calculating process may diverge if it is directly applied in the coupled equations of CRFLs when arbitrarily guessed initial values are out of the domain of convergence. To overcome the problem, genetic algorithm which has rather strong searching ability in global space is firstly employed to search for the initial value in convergent domain for each Stokes power; and then, the task of finding the more accurate initial values is finished by shooting method instead of GA whose searching ability is weak in local region. As an example, a sixth-order Ge-doped CRFL has been simulated by the novel algorithm. Calculated results show that the new method can effectively and quickly solve the coupled equations of the CRFL without the problem of divergence.
in silico protein recombination: a genetic algorithm applied to template and alignment selection in
Moreira, Bruno Contreras
in silico protein recombination: a genetic algorithm applied to template and alignment selectionLachlan (1986) Nature, 319: 199-203. #12;in silico protein recombination algorithm Contreras-More : 593-608.ira, Fitzjohn and Bates (2003) J Mol Biol, 328 #12;in silico protein recombination: performance d
An Efficient Solution Method for Weber Problems with Barriers based on Genetic Algorithms
Klamroth, Kathrin
An Efficient Solution Method for Weber Problems with Barriers based on Genetic Algorithms M by the Weiszfeld algorithm in case of the Weber objective function and Euclidean distances. A solution method the classical Weber objective function and hence seek to minimize the sum of the weighted distances between
Handwriting Biometric Hash Attack: A Genetic Algorithm with User Interaction for Raw Data
Paris-Sud XI, Université de
of biometric identification and verification systems based on fingerprints, iris, voice etc. were introducedHandwriting Biometric Hash Attack: A Genetic Algorithm with User Interaction for Raw Data}@fh-brandenburg.de, dirk.franke@st.ovgu.de, jana.dittmann@iti.cs.uni-magdeburg.de Abstract. Biometric Hash algorithms, also
Quad Search and Hybrid Genetic Algorithms Darrell Whitley, Deon Garrett, and JeanPaul Watson
Whitley, Darrell
Search using a Gray code representation con verges after at most 2L+ 2 evaluations on classesQuad Search and Hybrid Genetic Algorithms Darrell Whitley, Deon Garrett, and JeanPaul Watson constructed an algorithm we call Quad Search. Quad Search converges to a local optimum on unimodal 1D
Quad Search and Hybrid Genetic Algorithms Darrell Whitley, Deon Garrett, and Jean-Paul Watson
Whitley, Darrell
Search using a Gray code representation con- verges after at most ¡ evaluations on classesQuad Search and Hybrid Genetic Algorithms Darrell Whitley, Deon Garrett, and Jean-Paul Watson constructed an algorithm we call Quad Search. Quad Search converges to a local optimum on unimodal 1-D
USING GENETIC ALGORITHMS TO EVOLVE CHARACTER BEHAVIOURS IN MODERN VIDEO GAMES
Katchabaw, Michael James
USING GENETIC ALGORITHMS TO EVOLVE CHARACTER BEHAVIOURS IN MODERN VIDEO GAMES T. Bullen and M, evolutionary algorithms, computer and video games ABSTRACT Artificial intelligence is an important aspect to nearly every modern video game. Providing this, however, is all too often an arduous task, even
Vibrational genetic algorithm (VGA) and dynamic mesh in the optimization of 3D wing geometries
Ergüven Vatanda?; Abdurrahman Hacioglu; ?brahim Özkol
2007-01-01
The objective of this study is to combine dynamic mesh technique and heuristic algorithms [Vibrational Genetic Algorithm (VGA)] to improve aerodynamic design of a wing, in order to see the effect of thickness ratio constraint when it is taken into the design parameters, additionally to reduce the drag values as much as possible while holding the lift value fixed. To
A Sequential Similarity Metric for Case Injected Genetic Algorithms applied to TSPs
Louis, Sushil J.
Genetic Adaptive Systems LAB Dept. of Computer Science University of Nevada Reno, NV 89557 sushil@cs.unr.edu Yongmian Zhang Genetic Adaptive Systems LAB Dept. of Computer Science University of Nevada Reno, NV 89557, 1975; Gold berg, 1989). Current genetic algorithm based machine learning systems use rules to store
A Representation Scheme to Perform Program Induction in a Canonical Genetic Algorithm
Wineberg, Mark
attempt is Genetic Programming (GP) [Koza 92]. GP uses a modification of the GA to breed successiveA Representation Scheme to Perform Program Induction in a Canonical Genetic Algorithm Mark Wineberg, K1S 5B6 wineberg@scs.carleton.ca, oppacher@scs.carleton.ca Abstract. This paper studies Genetic
Michele Mosca
2008-08-04
This article surveys the state of the art in quantum computer algorithms, including both black-box and non-black-box results. It is infeasible to detail all the known quantum algorithms, so a representative sample is given. This includes a summary of the early quantum algorithms, a description of the Abelian Hidden Subgroup algorithms (including Shor's factoring and discrete logarithm algorithms), quantum searching and amplitude amplification, quantum algorithms for simulating quantum mechanical systems, several non-trivial generalizations of the Abelian Hidden Subgroup Problem (and related techniques), the quantum walk paradigm for quantum algorithms, the paradigm of adiabatic algorithms, a family of ``topological'' algorithms, and algorithms for quantum tasks which cannot be done by a classical computer, followed by a discussion.
Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping
NASA Astrophysics Data System (ADS)
Balakrishnan, D.; Quan, C.; Tay, C. J.
2013-06-01
The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.
Modified Niched Pareto Multi-objective Genetic Algorithm for Construction Scheduling Optimization
Kim, Kyungki
2012-10-19
This research proposes a Genetic Algorithm based decision support model that provides decision makers with a quantitative basis for multi-criteria decision making related to construction scheduling. In an attempt to overcome ...
NASA Technical Reports Server (NTRS)
Wang, Lui; Valenzuela-Rendon, Manuel
1993-01-01
The Space Station Freedom will require the supply of items in a regular fashion. A schedule for the delivery of these items is not easy to design due to the large span of time involved and the possibility of cancellations and changes in shuttle flights. This paper presents the basic concepts of a genetic algorithm model, and also presents the results of an effort to apply genetic algorithms to the design of propellant resupply schedules. As part of this effort, a simple simulator and an encoding by which a genetic algorithm can find near optimal schedules have been developed. Additionally, this paper proposes ways in which robust schedules, i.e., schedules that can tolerate small changes, can be found using genetic algorithms.
Vijayakumar, Bhuvaneshwaran
2001-01-01
The motivation for this work has been the use of tools, such as genetic algorithms and fuzzy sets, to address the various issues that are involved in an engineering design optimization problem. In order to address the variety, generality...
An analysis of posynomial MOSFET models using genetic algorithms and visualization
Salameh, Lynne Rafik
2007-01-01
Analog designers are interested in optimization tools which automate the process of circuit sizing. Geometric programming, which uses posynomial models of MOSFET parameters, represents one such tool. Genetic algorithms ...
An Analysis of Posynomial MOSFET Models Using Genetic Algorithms and Visualization
Salameh, Lynne Rafik
2007-06-05
Analog designers are interested in optimization tools which automate the process of circuit sizing. Geometric programming, which uses posynomial models of MOSFET parameters, represents one such tool. Genetic algorithms ...
Tai, Kang
This paper describes an intuitive way of defining geometry design variables for solving structural topology optimization problems using a genetic algorithm (GA). The geometry representation scheme works by defining a ...
The use of genetic algorithms to model protoplanetary discs
NASA Astrophysics Data System (ADS)
Hetem, Annibal; Gregorio-Hetem, Jane
2007-12-01
The protoplanetary discs of T Tauri and Herbig Ae/Be stars have previously been studied using geometric disc models to fit their spectral energy distribution (SED). The simulations provide a means to reproduce the signatures of various circumstellar structures, which are related to different levels of infrared excess. With the aim of improving our previous model, which assumed a simple flat-disc configuration, we adopt here a reprocessing flared-disc model that assumes hydrostatic, radiative equilibrium. We have developed a method to optimize the parameter estimation based on genetic algorithms (GAs). This paper describes the implementation of the new code, which has been applied to Herbig stars from the Pico dos Dias Survey catalogue, in order to illustrate the quality of the fitting for a variety of SED shapes. The star AB Aur was used as a test of the GA parameter estimation, and demonstrates that the new code reproduces successfully a canonical example of the flared-disc model. The GA method gives a good quality of fit, but the range of input parameters must be chosen with caution, as unrealistic disc parameters can be derived. It is confirmed that the flared-disc model fits the flattened SEDs typical of Herbig stars; however, embedded objects (increasing SED slope) and debris discs (steeply decreasing SED slope) are not well fitted with this configuration. Even considering the limitation of the derived parameters, the automatic process of SED fitting provides an interesting tool for the statistical analysis of the circumstellar luminosity of large samples of young stars.
Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques
Michael A. Lee; Hideyuki Takagi
1993-01-01
This paper proposes using fuzzy logic techniques to dynamically control parameter settings of ge- netic algorithms (GAs). We describe the Dy- namic Parametric GA: a GA that uses a fuzzy knowledge-based system to control GA parame- ters. We then introduce a technique for automati- cally designing and tuning the fuzzy knowledge- base system using GAs. Results from initial experiments show
Genetic Algorithms and Support Vector Machines for Time Series Classification
Theiler, James
Olympus who controlled thunder, lightning, and rain. Feature selection is performed by generating random introduce an algorithm for classifying time series data. Since our initial application is for lightning data this question by testing Zeus on a lightning classification task using data acquired from the Fast On
Reconstruction of Cracks from Eddy Current Signals Using Genetic Algorithm and Fuzzy Logic
NASA Astrophysics Data System (ADS)
Sikora, R.; Baniukiewicz, P.
2006-03-01
In this paper the authors present a fast inverse iterative algorithm with a feedback loop designed to reconstruct a crack shape using multifrequency eddy current data. It uses a parametric description of the flaws. Therefore, the algorithm can be applied to recognize natural and regularly-shaped flaws, especially of the profiles close to triangle, rectangle, ellipse and trapezium. The algorithm is not sensitive to the signal distortions caused by noise and lift-off fluctuations. The new forward model based on the ANFIS network has been proposed. The optimization problem has been solved by the means of genetic algorithms.
Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.
2013-01-01
Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected. PMID:24453933
Genetic Algorithm Based Approach For The Optimal Allocation of Facts Devices
NASA Astrophysics Data System (ADS)
Bhattacharyya, B.; Goswami, S. K.
2010-06-01
This paper presents Genetic Algorithm (GA) based approach for the allocation of FACTS devices for the improvement of Power transfer capacity in an interconnected Power System. Simulations are done on IEEE 30 BUS System. The result obtained by the GA (Genetic Algorithm) approach is compared with that of obtained by PSO (Particle Swarm Optimization) method. The comparison shows how the system performance can be greatly improved with the GA based proposed approach.
Sung Hyup You; Yong Hee Lee; Woo Jeong Lee
2011-01-01
A genetic algorithm was used to optimize the parameters of the two-dimensional Storm Surge\\/Tide Operational Model (STORM)\\u000a to improve sea level predictions. The genetic algorithm was applied to nine typhoons that affected the Korean Peninsula during\\u000a 2005–2007. The following model parameters were used: the bottom drag coefficient, the background horizontal diffusivity, Smagorinski’s\\u000a horizontal viscosity, and the sea level pressure scaling.
Simulating Evolution of Drosophila Melanogaster Ebony Mutants Using a Genetic Algorithm
Glennie Helles
2009-01-01
Genetic algorithms are generally quite easy to understand and work with, and they are a popular choice in many cases. One\\u000a area in which genetic algorithms are widely and successfully used is artificial life where they are used to simulate evolution\\u000a of artificial creatures. However, despite their suggestive name, simplicity and popularity in artificial life, they do not\\u000a seem to
Morales, Adrian
2011-02-22
the option to input the probability of success when compared to a Standard Genetic Algorithm. v DEDICATION For my mother, Aleida Salinas and father, Nicol?s Morales Para mi madre, Aleida Salinas y mi padre, Nicol?s Morales vi ACKNOWLEDGEMENTS... been used in a vast area of expertise within the petroleum industry, with the previously mentioned research only being a small portion of articles published. Literature Review of Genetic Algorithms for Well Placement Optimization One...
Truss Optimization for a Manned Nuclear Electric Space Vehicle using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Benford, Andrew; Tinker, Michael L.
2004-01-01
The purpose of this paper is to utilize the genetic algorithm (GA) optimization method for structural design of a nuclear propulsion vehicle. Genetic algorithms provide a guided, random search technique that mirrors biological adaptation. To verify the GA capabilities, other traditional optimization methods were used to generate results for comparison to the GA results, first for simple two-dimensional structures, and then for full-scale three-dimensional truss designs.
A High-Performance Genetic Algorithm: Using Traveling Salesman Problem as a Case
Tsai, Chun-Wei; Tseng, Shih-Pang; Yang, Chu-Sing
2014-01-01
This paper presents a simple but efficient algorithm for reducing the computation time of genetic algorithm (GA) and its variants. The proposed algorithm is motivated by the observation that genes common to all the individuals of a GA have a high probability of surviving the evolution and ending up being part of the final solution; as such, they can be saved away to eliminate the redundant computations at the later generations of a GA. To evaluate the performance of the proposed algorithm, we use it not only to solve the traveling salesman problem but also to provide an extensive analysis on the impact it may have on the quality of the end result. Our experimental results indicate that the proposed algorithm can significantly reduce the computation time of GA and GA-based algorithms while limiting the degradation of the quality of the end result to a very small percentage compared to traditional GA. PMID:24892038
Ralf Salomon
1996-01-01
In recent years, genetic algorithms (GAs) have become increasingly robust and easy to use. Current knowledge and many successful experiments suggest that the application of GAs is not limited to easy-to-optimize unimodal functions. Several results and GA theory give the impression that GAs easily escape from millions of local optima and reliably converge to a single global optimum. The theoretical
NASA Astrophysics Data System (ADS)
Zhou, Mandi; Shu, Jiong; Chen, Zhigang; Ji, Minhe
2012-11-01
Hyperspectral imagery has been widely used in terrain classification for its high resolution. Urban vegetation, known as an essential part of the urban ecosystem, can be difficult to discern due to high similarity of spectral signatures among some land-cover classes. In this paper, we investigate a hybrid approach of the genetic-algorithm tuned fuzzy support vector machine (GA-FSVM) technique and apply it to urban vegetation classification from aerial hyperspectral urban imagery. The approach adopts the genetic algorithm to optimize parameters of support vector machine, and employs the K-nearest neighbor algorithm to calculate the membership function for each fuzzy parameter, aiming to reduce the effects of the isolated and noisy samples. Test data come from push-broom hyperspectral imager (PHI) hyperspectral remote sensing image which partially covers a corner of the Shanghai World Exposition Park, while PHI is a hyper-spectral sensor developed by Shanghai Institute of Technical Physics. Experimental results show the GA-FSVM model generates overall accuracy of 71.2%, outperforming the maximum likelihood classifier with 49.4% accuracy and the artificial neural network method with 60.8% accuracy. It indicates GA-FSVM is a promising model for vegetation classification from hyperspectral urban data, and has good advantage in the application of classification involving abundant mixed pixels and small samples problem.
Research on Prediction Model of Time Series Based on Fuzzy Theory and Genetic Algorithm
NASA Astrophysics Data System (ADS)
Xiao-qin, Wu
Fuzzy theory is one of the newly adduced self-adaptive strategies,which is applied to dynamically adjust the parameters o genetic algorithms for the purpose of enhancing the performance.In this paper, the financial time series analysis and forecasting as the main case study to the theory of soft computing technology framework that focuses on the fuzzy theory and genetic algorithms(FGA) as a method of integration. the financial time series forecasting model based on fuzzy theory and genetic algorithms was built. the ShangZheng index cards as an example. The experimental results show that FGA perform s much better than BP neural network, not only in the precision, but also in the searching speed.The hybrid algorithm has a strong feasibility and superiority.
An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints
Rao, Yunqing; Qi, Dezhong; Li, Jinling
2013-01-01
For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem. PMID:24489491
A Constrained Genetic Algorithm with Adaptively Defined Fitness Function in MRS Quantification
NASA Astrophysics Data System (ADS)
Papakostas, G. A.; Karras, D. A.; Mertzios, B. G.; Graveron-Demilly, D.; van Ormondt, D.
MRS Signal quantification is a rather involved procedure and has attracted the interest of the medical engineering community, regarding the development of computationally efficient methodologies. Significant contributions based on Computational Intelligence tools, such as Neural Networks (NNs), demonstrated a good performance but not without drawbacks already discussed by the authors. On the other hand preliminary application of Genetic Algorithms (GA) has already been reported in the literature by the authors regarding the peak detection problem encountered in MRS quantification using the Voigt line shape model. This paper investigates a novel constrained genetic algorithm involving a generic and adaptively defined fitness function which extends the simple genetic algorithm methodology in case of noisy signals. The applicability of this new algorithm is scrutinized through experimentation in artificial MRS signals interleaved with noise, regarding its signal fitting capabilities. Although extensive experiments with real world MRS signals are necessary, the herein shown performance illustrates the method's potential to be established as a generic MRS metabolites quantification procedure.
Using genetic algorithms to search for an optimal portfolio strategy and test market efficiency
NASA Astrophysics Data System (ADS)
Xi, Haowen; Mandere, Edward
2008-03-01
In this numerical experiment we used genetic algorithms to search for an optimal portfolio investment strategy. The algorithm involves having a ``manager'' who divides his capital among various ``experts'' each of whom has a simple fixed investment strategy. The expert strategies act like population of genes which experiencing selection, mutation and crossover during evolution process. The genetic algorithm was run on actual portfolio with stock data (DowJones 30 stocks). We found that the genetic algorithm overwhelmingly selected optimal strategy that closely resembles a simple buy and hold portfolio, that is, evenly distribute the capital among all stocks. This study shows that market is very efficient, and one possible practical way to gauge market efficiency is to measure the difference between an optimal portfolio return and a simple buy and hold portfolio return.
NASA Astrophysics Data System (ADS)
Sharifi, Mani; Rezaei Moayed, Reza; Haratizadeh, Sara
2011-09-01
This paper presents two models for redundancy allocation problem (RAP) with cold standby redundancy policy subject to weight and cost constraints. Also, each element of the system can be damaged exponentially. And, damaged elements can be repaired exponentially by hiring some repairmen. The problem is to determine: (1) element type used in the system, (2) number of elements, and (3) number of repairmen. As the models are not solvable by exact solution methods in reasonable CPU time, an efficient genetic algorithm is developed for it. The genetic algorithm (GA) is hybridized with a local search procedure. Also, the algorithm accepts infeasible solutions after penalizing them based on their amounts of infeasibilities. Thereby, by using these two features, an efficient genetic algorithm is obtained.
Flow Control Optimization Using Neural Networks and Genetic Algorithms
Raymond P. LeBeau; Narendra K. Beliganur; Thomas Hauser
Evolutionary algorithms have now been used as tool to optimize complex design spaces in aerospace applications, notably in\\u000a the areas of Multidisciplinary Design Optimization (MDO) [4, 2] and flow control [9]. However, in the latter area a limiting\\u000a factor has been the cost of evaluating the performance of each tested flow control configuration. This process is conventionally\\u000a accomplished through computational
A Clustering Genetic Algorithm for Genomic Data Mining
José Juan Tapia; Enrique Morett; Edgar E. Vallejo
2009-01-01
In this chapter we summarize our work toward developing clustering algorithms based on evolutionary computing and its application\\u000a to genomic data mining. We have focused on the reconstruction of protein-protein functional interactions from genomic data.\\u000a The discovery of functional modules of proteins is formulated as an optimization problem in which proteins with similar genomic\\u000a attributes are grouped together. By considering
A multiobjective optimization algorithm is applied to a groundwater quality management problem involving remediation by pump-and-treat (PAT). The multiobjective optimization framework uses the niched Pareto genetic algorithm (NPGA) and is applied to simultaneously minimize the...
Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm
NASA Astrophysics Data System (ADS)
Chen, Chao; Xia, Jianghai; Liu, Jiangping; Feng, Guangding
2006-03-01
Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant result is that final solution is determined by the average model derived from multiple trials instead of one computation due to the randomness in a genetic algorithm procedure. These advantages were demonstrated by synthetic and real-world examples of inversion of potential-field data.
NASA Astrophysics Data System (ADS)
Zhang, Liangyue; Shen, Xiaoyan; Han, Ya; Sun, Jie; Li, Dongsheng
2015-08-01
FBG is a kind of promising high precision strain sensor, and it can not only detect the homogeneous strain, but also identify non-uniform strain distribution. In the application of FBG in inhomogeneous strain sensing, genetic algorithm is an important method to reconstruct the non-uniform strain from reflection spectra of FBG. However, the practical reconstruction of genetic algorithm demonstrates its shortcomings such as low computational efficiency, easily falling into local optimal solution, etc , and it is well known that there is a great relationship between computational efficiency and population initialization of genetic algorithm. In general genetic algorithm employed in FBG strain reconstruction, the initialized population is randomly distributed strain along FBG axial direction, which ignores the continuity between neighbor strains. To reduce the number of population parameters and make the original population more close to the real strain distribution, a new method of population initialization is proposed here, that is using polynomial function parameters to be the initialized population instead of the randomly distributed strain, supposed that the FBG axial strains can be described as a polynomial function with independent variable of axial position. In simulation experiments, the reflection spectrums of a 10mm-long FBG are obtained from T-Matrix method in four cases of strain-free, linear-distributed strain, parabola-distributed strain and exponential -distributed strain, and then the general genetic algorithm and the new genetic algorithm with simplified population initialization were applied to reconstruct the strain distribution from the reflection spectrums respectively. The experiment results verify the supposition of the polynomial function of the FBG, and show clearly that the new method can improve the computational efficiency of genetic algorithm in FBG inhomogeneous strain demodulation greatly. From the results, it is found that with the same calculation accuracy, the computing time of the new population initialization method is reduced to about 1/5 of the general on average.
NSDL National Science Digital Library
Dr Gene Tagliarini
CSC 325. (MAT 325) Numerical Algorithms (3) Prerequisite: CSC 112 or 121, MAT 162. An introduction to the numerical algorithms fundamental to scientific computer work. Includes elementary discussion of error, polynomial interpolation, quadrature, linear systems of equations, solution of nonlinear equations and numerical solution of ordinary differential equations. The algorithmic approach and the efficient use of the computer are emphasized.
Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution
NASA Technical Reports Server (NTRS)
Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria
2009-01-01
The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship s flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm s design, along with mathematical models of the algorithm s performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.
Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution
NASA Technical Reports Server (NTRS)
Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria
2009-01-01
The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship's flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm's design, along with mathematical models of the algorithm's performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.
Learning the Dominance in Diploid Genetic Algorithms for Changing Optimization Problems
Yang, Shengxiang
Learning the Dominance in Diploid Genetic Algorithms for Changing Optimization Problems Shengxiang Yang Abstract-- Using diploid representation with dominance scheme is one of the approaches developed for genetic al- gorithms to address dynamic optimization problems. This paper proposes an adaptive dominance
Applied Cloning Techniques for a Genetic Algorithm Used in Evolvable Hardware Design
Wu, Annie S.
Applied Cloning Techniques for a Genetic Algorithm Used in Evolvable Hardware Design Viet C. Trinh optima points. Therefore, an alternative cloning strategy is proposed which introduces a more powerful of the new method provides good insight on the application of cloning to this domain. Keywords: Genetic
A WEIGHT-CODED GENETIC ALGORITHM FOR THE MINIMUM WEIGHT TRIANGULATION PROBLEM
Julstrom, Bryant A.
. Natural selection inspires the selection pro- cess, and genetic recombination and mutation inspireA WEIGHT-CODED GENETIC ALGORITHM FOR THE MINIMUM WEIGHT TRIANGULATION PROBLEM Kerry Capp for your personal use. Not for redistribution. The de#12;nitive version was published in Applied Computing
Fuzzy-guided Genetic Algorithm applied to the Web Service Selection Problem
Ludwig, Simone
Fuzzy-guided Genetic Algorithm applied to the Web Service Selection Problem Min Chen North Dakota during the service selection task. In this paper, we propose an improved version of the standard genetic, and quality of the service. Service descriptions are necessary for discovery, selection, binding
Automatic Design Method of Dynamic Systems Based on Hungarian Algorithm and Genetic Programming
Li Shaobo; Guanci Yang; Xie Qingsheng
2008-01-01
This paper summarizes the present research status of automated design method for dynamic systems, investigates efficient method of fitness definition for automated design method of dynamic systems based on bond graphs and genetic programming. The automated design method based on Hungarian algorithm and genetic programming (HAGP) is proposed, and the statistic results of domain independent - an eigenvalues -placement design
Seismic Velocity Inversion with Genetic Algorithms Sushil J. Louis Qinxue Chen
Louis, Sushil J.
Seismic Velocity Inversion with Genetic Algorithms Sushil J. Louis Qinxue Chen Genetic Adaptivesurface models from seismic traveltime data. Given a subsurface model, the physics of wave propagation through refractive media can be used to compute travel times for seismic waves. How ever, in practice, we have
Genetic Algorithms with Memory-and Elitism-Based Immigrants in Dynamic Environments
Yang, Shengxiang
Genetic Algorithms with Memory- and Elitism- Based Immigrants in Dynamic Environments Shengxiang immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algo- rithms in dynamic environments. In these schemes, the best
Genetic Algorithms with Elitism-Based Immigrants for Changing Optimization Problems
Yang, Shengxiang
Genetic Algorithms with Elitism-Based Immigrants for Changing Optimization Problems Shengxiang Yang.g., via random immigrants. This paper proposes an elitism-based immigrants scheme for genetic al- gorithms in dynamic environments. In the scheme, the elite from previ- ous generation is used as the base to create
A Sequential Similarity Metric for Case Injected Genetic Algorithms applied to TSPs
Louis, Sushil J.
Genetic Adaptive Systems LAB Dept. of Computer Science University of Nevada Reno, NV 89557 sushil@cs.unr.edu Yongmian Zhang Genetic Adaptive Systems LAB Dept. of Computer Science University of Nevada Reno, NV 89557) are randomized parallel search algorithms that search from a population of points (Holland, 1975; Gold- berg
Using a Genetic Algorithm for Editing k-Nearest Neighbor Classifiers
Yao, Xin
scheme. Results achieved using the breast cancer database and the diabetes database from the UCI machineUsing a Genetic Algorithm for Editing k-Nearest Neighbor Classifiers R. Gil-Pita1 and X. Yao2,3, 1 of the complete training set in which some of the training patterns are excluded. In recent works, genetic
Algorithms for Regression and Classification Robust Regression and Genetic Association Studies
Fernandez, Thomas
on sporadic breast cancer and to handle larger data sets than the compared methods. In additionAlgorithms for Regression and Classification Robust Regression and Genetic Association Studies. The focus of this thesis is on robust regression and classification in genetic association studies
Optimization for a steam turbine stage efficiency using a genetic algorithm
Xiaoyong Qin; Lingen Chen; Fengrui Sun; Chih Wu
2003-01-01
Based on the genetic optimization theory, a design method for the flow path of an axial flow steam turbine stage is presented. In this method the maximum efficiency of the stage is taken as the objective, a series of functions are taken as constraints, and the optimal geometric and aerodynamic parameters are solved using the genetic algorithm process. The efficiency
Inverse airfoil design by using an accelerated genetic algorithm via distribution strategies
A. Hacio?lu; ?. Özkol
2005-01-01
In this study, the distribution strategies (DS) in evolutionary computations and their application to the inverse airfoil design problems have been introduced. Distribution strategies are based on the decomposition of the genetic operations for both upper and lower surfaces of the airfoil. We developed new strategies and combined these with a real coded genetic algorithm to obtain a faster and
Road Traffic Control Based on Genetic Algorithm for Reducing Traffic Congestion
NASA Astrophysics Data System (ADS)
Shigehiro, Yuji; Miyakawa, Takuya; Masuda, Tatsuya
In this paper, we propose a road traffic control method for reducing traffic congestion with genetic algorithm. In the not too distant future, the system which controls the routes of all vehicles in a certain area must be realized. The system should optimize the routes of all vehicles, however the solution space of this problem is enormous. Therefore we apply the genetic algorithm to this problem, by encoding the route of all vehicles to a fixed length chromosome. To improve the search performance, a new genetic operator called “path shortening” is also designed. The effectiveness of the proposed method is shown by the experiment.
Xu, Sheng-Hua; Liu, Ji-Ping; Zhang, Fu-Hao; Wang, Liang; Sun, Li-Jian
2015-01-01
A combination of genetic algorithm and particle swarm optimization (PSO) for vehicle routing problems with time windows (VRPTW) is proposed in this paper. The improvements of the proposed algorithm include: using the particle real number encoding method to decode the route to alleviate the computation burden, applying a linear decreasing function based on the number of the iterations to provide balance between global and local exploration abilities, and integrating with the crossover operator of genetic algorithm to avoid the premature convergence and the local minimum. The experimental results show that the proposed algorithm is not only more efficient and competitive with other published results but can also obtain more optimal solutions for solving the VRPTW issue. One new well-known solution for this benchmark problem is also outlined in the following. PMID:26343655
Optimal design of optical reference signals by use of a genetic algorithm
NASA Astrophysics Data System (ADS)
Saez-Landete, José; Salcedo-Sanz, Sancho; Rosa-Zurera, Manuel; Alonso, José; Bernabeu, Eusebio
2005-10-01
A new technique for the generation of optical reference signals with optimal properties is presented. In grating measurement systems a reference signal is needed to achieve an absolute measurement of the position. The optical signal is the autocorrelation of two codes with binary transmittance. For a long time, the design of this type of code has required great computational effort, which limits the size of the code to ˜30 elements. Recently, the application of the dividing rectangles (DIRECT) algorithm has allowed the automatic design of codes up to 100 elements. Because of the binary nature of the problem and the parallel processing of the genetic algorithms, these algorithms are efficient tools for obtaining codes with particular autocorrelation properties. We design optimum zero reference codes with arbitrary length by means of a genetic algorithm enhanced with a restricted search operator.
Zhang Changjiang; Wang Xiaodong
2008-11-06
An efficient typhoon cloud image restoration algorithm is proposed. Having implemented contourlet transform to a typhoon cloud image, noise is reduced in the high sub-bands. Weight median value filter is used to reduce the noise in the contourlet domain. Inverse contourlet transform is done to obtain the de-noising image. In order to enhance the global contrast of the typhoon cloud image, in-complete Beta transform (IBT) is used to determine non-linear gray transform curve so as to enhance global contrast for the de-noising typhoon cloud image. Genetic algorithm is used to obtain the optimal gray transform curve. Information entropy is used as the fitness function of the genetic algorithm. Experimental results show that the new algorithm is able to well enhance the global for the typhoon cloud image while well reducing the noises in the typhoon cloud image.
Ducatelle, Frederick
) analysis in electric utilities using Genetic Algorithm (GA) and Support Vector Machine (SVM). The main. Keywords: Support vector machine, Genetic algorithm, Electricity theft, Non-technical loss, Data mining. 1122 Intelligent System for Detection of Abnormalities and Theft of Electricity using Genetic
Modelling and genetic algorithm based optimisation of inverse supply chain
NASA Astrophysics Data System (ADS)
Bányai, T.
2009-04-01
The design and control of recycling systems of products with environmental risk have been discussed in the world already for a long time. The main reasons to address this subject are the followings: reduction of waste volume, intensification of recycling of materials, closing the loop, use of less resource, reducing environmental risk [1, 2]. The development of recycling systems is based on the integrated solution of technological and logistic resources and know-how [3]. However the financial conditions of recycling systems is partly based on the recovery, disassembly and remanufacturing options of the used products [4, 5, 6], but the investment and operation costs of recycling systems can be characterised with high logistic costs caused by the geographically wide collection system with more collection level and a high number of operation points of the inverse supply chain. The reduction of these costs is a popular area of the logistics researches. These researches include the design and implementation of comprehensive environmental waste and recycling program to suit business strategies (global system), design and supply all equipment for production line collection (external system), design logistics process to suit the economical and ecological requirements (external system) [7]. To the knowledge of the author, there has been no research work on supply chain design problems that purpose is the logistics oriented optimisation of inverse supply chain in the case of non-linear total cost function consisting not only operation costs but also environmental risk cost. The antecedent of this research is, that the author has taken part in some research projects in the field of closed loop economy ("Closing the loop of electr(on)ic products and domestic appliances from product planning to end-of-life technologies), environmental friendly disassembly (Concept for logistical and environmental disassembly technologies) and design of recycling systems of household appliances (Recycling of household appliances with emphasis on reuse options). The purpose of this paper is the presentation of a possible method for avoiding the unnecessary environmental risk and landscape use through unprovoked large supply chain of collection systems of recycling processes. In the first part of the paper the author presents the mathematical model of recycling related collection systems (applied especially for wastes of electric and electronic products) and in the second part of the work a genetic algorithm based optimisation method will be demonstrated, by the aid of which it is possible to determine the optimal structure of the inverse supply chain from the point of view economical, ecological and logistic objective functions. The model of the inverse supply chain is based on a multi-level, hierarchical collection system. In case of this static model it is assumed that technical conditions are permanent. The total costs consist of three parts: total infrastructure costs, total material handling costs and environmental risk costs. The infrastructure-related costs are dependent only on the specific fixed costs and the specific unit costs of the operation points (collection, pre-treatment, treatment, recycling and reuse plants). The costs of warehousing and transportation are represented by the material handling related costs. The most important factors determining the level of environmental risk cost are the number of out of time recycled (treated or reused) products, the number of supply chain objects and the length of transportation routes. The objective function is the minimization of the total cost taking into consideration the constraints. However a lot of research work discussed the design of supply chain [8], but most of them concentrate on linear cost functions. In the case of this model non-linear cost functions were used. The non-linear cost functions and the possible high number of objects of the inverse supply chain leaded to the problem of choosing a possible solution method. By the aid of analytical methods, the problem can not be so
NASA Astrophysics Data System (ADS)
Niwa, Keiichi; Hayashida, Tomohiro; Sakawa, Masatoshi; Yang, Yishen
2010-10-01
We consider two-level programming problems in which there are one decision maker (the leader) at the upper level and two or more decision makers (the followers) at the lower level and decision variables of the leader and the followers are 0-1 variables. We assume that there is coordination among the followers while between the leader and the group of all the followers, there is no motivation to cooperate each other, and fuzzy goals for objective functions of the leader and the followers are introduced so as to take fuzziness of their judgments into consideration. The leader maximizes the degree of satisfaction (the value of the membership function) and the followers choose in concert in order to maximize a minimum among their degrees of satisfaction. We propose a modified computational method that solves problems related to the computational method based on the genetic algorithm (the existing method) for obtaining the Stackelberg solution. Specifically, the distributed genetic algorithm is introduced with respect to the upper level genetic algorithm, which handles decision variables for the leader in order to shorten the computational time of the existing method. Parallelization of the lower level genetic algorithm is also performed along with parallelization of the upper level genetic algorithm. In order to demonstrate the effectiveness of the proposed computational method, numerical experiments are carried out.
Research on formation of microsatellite communication with genetic algorithm.
Wu, Guoqiang; Bai, Yuguang; Sun, Zhaowei
2013-01-01
For the formation of three microsatellites which fly in the same orbit and perform three-dimensional solid mapping for terra, this paper proposes an optimizing design method of space circular formation order based on improved generic algorithm and provides an intersatellite direct spread spectrum communication system. The calculating equation of LEO formation flying satellite intersatellite links is guided by the special requirements of formation-flying microsatellite intersatellite links, and the transmitter power is also confirmed throughout the simulation. The method of space circular formation order optimizing design based on improved generic algorithm is given, and it can keep formation order steady for a long time under various absorb impetus. The intersatellite direct spread spectrum communication system is also provided. It can be found that, when the distance is 1?km and the data rate is 1?Mbps, the input wave matches preferably with the output wave. And LDPC code can improve the communication performance. The correct capability of (512, 256) LDPC code is better than (2, 1, 7) convolution code, distinctively. The design system can satisfy the communication requirements of microsatellites. So, the presented method provides a significant theory foundation for formation-flying and intersatellite communication. PMID:24078796
Research on Formation of Microsatellite Communication with Genetic Algorithm
Wu, Guoqiang; Bai, Yuguang; Sun, Zhaowei
2013-01-01
For the formation of three microsatellites which fly in the same orbit and perform three-dimensional solid mapping for terra, this paper proposes an optimizing design method of space circular formation order based on improved generic algorithm and provides an intersatellite direct spread spectrum communication system. The calculating equation of LEO formation flying satellite intersatellite links is guided by the special requirements of formation-flying microsatellite intersatellite links, and the transmitter power is also confirmed throughout the simulation. The method of space circular formation order optimizing design based on improved generic algorithm is given, and it can keep formation order steady for a long time under various absorb impetus. The intersatellite direct spread spectrum communication system is also provided. It can be found that, when the distance is 1?km and the data rate is 1?Mbps, the input wave matches preferably with the output wave. And LDPC code can improve the communication performance. The correct capability of (512, 256) LDPC code is better than (2, 1, 7) convolution code, distinctively. The design system can satisfy the communication requirements of microsatellites. So, the presented method provides a significant theory foundation for formation-flying and intersatellite communication. PMID:24078796
NASA Astrophysics Data System (ADS)
Orozco-Monteagudo, Maykel; Taboada-Crispi, Alberto; Gutiérrez-Hernández, Liliana
2008-11-01
This paper deals with the controversial topic of the selection of the parameters of a genetic algorithm, in this case hierarchical, used for training of multilayer perceptron neural networks for the binary classification. The parameters to select are the crossover and mutation probabilities of the control and parametric genes and the permanency percent. The results can be considered as a guide for using this kind of algorithm.
Lin, J.; Bartal, Y.; Uhrig, R.E.
1995-03-01
Nuclear power plant status is monitored by a human operator. To enhance the operator`s capability to diagnose the nuclear power plant status in case of a transient, several systems were developed to identify the type of the transient. Few of them addressed the further question: how severe is the transient? In this paper, we explore the possibility of predicting the severity of a transient using genetic algorithms and nearest neighbor algorithms after its type has been identified.
T. Sari; V. Cakir; S. Kilic; E. Ece
2011-01-01
Basic elements of project planning are activity scheduling and resource allocation. In this study, meta-heuristic methods reviewed as an appropriate solution tool for resource- constrained project scheduling problem and the success of two meta-heuristic methods discussed. Resource constrained project scheduling problem which is an NP-hard problem solved with scatter search and genetic algorithm. Both algorithms do not disrupt the feasible
Simonett, Joseph M.; Sohrab, Mahsa A.; Pacheco, Jennifer; Armstrong, Loren L.; Rzhetskaya, Margarita; Smith, Maureen; Geoffrey Hayes, M.; Fawzi, Amani A.
2015-01-01
Age-related macular degeneration (AMD), a multifactorial, neurodegenerative disease, is a leading cause of vision loss. With the rapid advancement of DNA sequencing technologies, many AMD-associated genetic polymorphisms have been identified. Currently, the most time consuming steps of these studies are patient recruitment and phenotyping. In this study, we describe the development of an automated algorithm to identify neovascular (wet) AMD, non-neovascular (dry) AMD and control subjects using electronic medical record (EMR)-based criteria. Positive predictive value (91.7%) and negative predictive value (97.5%) were calculated using expert chart review as the gold standard to assess algorithm performance. We applied the algorithm to an EMR-linked DNA bio-repository to study previously identified AMD-associated single nucleotide polymorphisms (SNPs), using case/control status determined by the algorithm. Risk alleles of three SNPs, rs1061170 (CFH), rs1410996 (CFH), and rs10490924 (ARMS2) were found to be significantly associated with the AMD case/control status as defined by the algorithm. With the rapid growth of EMR-linked DNA biorepositories, patient selection algorithms can greatly increase the efficiency of genetic association study. We have found that stepwise validation of such an algorithm can result in reliable cohort selection and, when coupled within an EMR-linked DNA biorepository, replicates previously published AMD-associated SNPs. PMID:26255974
Simonett, Joseph M; Sohrab, Mahsa A; Pacheco, Jennifer; Armstrong, Loren L; Rzhetskaya, Margarita; Smith, Maureen; Geoffrey Hayes, M; Fawzi, Amani A
2015-01-01
Age-related macular degeneration (AMD), a multifactorial, neurodegenerative disease, is a leading cause of vision loss. With the rapid advancement of DNA sequencing technologies, many AMD-associated genetic polymorphisms have been identified. Currently, the most time consuming steps of these studies are patient recruitment and phenotyping. In this study, we describe the development of an automated algorithm to identify neovascular (wet) AMD, non-neovascular (dry) AMD and control subjects using electronic medical record (EMR)-based criteria. Positive predictive value (91.7%) and negative predictive value (97.5%) were calculated using expert chart review as the gold standard to assess algorithm performance. We applied the algorithm to an EMR-linked DNA bio-repository to study previously identified AMD-associated single nucleotide polymorphisms (SNPs), using case/control status determined by the algorithm. Risk alleles of three SNPs, rs1061170 (CFH), rs1410996 (CFH), and rs10490924 (ARMS2) were found to be significantly associated with the AMD case/control status as defined by the algorithm. With the rapid growth of EMR-linked DNA biorepositories, patient selection algorithms can greatly increase the efficiency of genetic association study. We have found that stepwise validation of such an algorithm can result in reliable cohort selection and, when coupled within an EMR-linked DNA biorepository, replicates previously published AMD-associated SNPs. PMID:26255974
Chaos-based image encryption using a hybrid genetic algorithm and a DNA sequence
NASA Astrophysics Data System (ADS)
Enayatifar, Rasul; Abdullah, Abdul Hanan; Isnin, Ismail Fauzi
2014-05-01
The paper studies a recently developed evolutionary-based image encryption algorithm. A novel image encryption algorithm based on a hybrid model of deoxyribonucleic acid (DNA) masking, a genetic algorithm (GA) and a logistic map is proposed. This study uses DNA and logistic map functions to create the number of initial DNA masks and applies GA to determine the best mask for encryption. The significant advantage of this approach is improving the quality of DNA masks to obtain the best mask that is compatible with plain images. The experimental results and computer simulations both confirm that the proposed scheme not only demonstrates excellent encryption but also resists various typical attacks.
NASA Astrophysics Data System (ADS)
Yang, Xiaohua; Yang, Zhifeng; Yin, Xinan; Li, Jianqiang
2008-10-01
In order to reduce the computational amount and improve computational precision for nonlinear optimizations and pollution source identification in convection-diffusion equation, a new algorithm, chaos gray-coded genetic algorithm (CGGA) is proposed, in which initial population are generated by chaos mapping, and new chaos mutation and Hooke-Jeeves evolution operation are used. With the shrinking of searching range, CGGA gradually directs to an optimal result with the excellent individuals obtained by gray-coded genetic algorithm. Its convergence is analyzed. It is very efficient in maintaining the population diversity during the evolution process of gray-coded genetic algorithm. This new algorithm overcomes any Hamming-cliff phenomena existing in other encoding genetic algorithm. Its efficiency is verified by application of 20 nonlinear test functions of 1-20 variables compared with standard binary-coded genetic algorithm and improved genetic algorithm. The position and intensity of pollution source are well found by CGGA. Compared with Gray-coded hybrid-accelerated genetic algorithm and pure random search algorithm, CGGA has rapider convergent speed and higher calculation precision.
A High Fuel Consumption Efficiency Management Scheme for PHEVs Using an Adaptive Genetic Algorithm
Lee, Wah Ching; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei; Wu, Chung Kit; Chui, Kwok Tai; Lau, Wing Hong; Leung, Yat Wah
2015-01-01
A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day. PMID:25587974
NASA Astrophysics Data System (ADS)
Nuhanovi?, Amir; Hivziefendi?, Jasna; Hadžimehmedovi?, Amir
2013-09-01
This paper discusses the problem of finding the optimal network topological configuration by changing the feeder status. The reconfiguration problem is considered as a multiobjective problem aiming to minimize power losses and total interruptions costs subject to the system constraints: the network radiality voltage limits and feeder capability limits. Due to its complexity, the metaheuristic methods can be applied to solve the problem and often the choice is genetic algorithm. NSGA II is used to solve the multiobjective optimization problem in order to get Pareto optimal set with possible solutions. The proposed method has been tested on real 35 kV distribution network. The numerical results are presented to illustrate the feasibility of the proposed genetic algorithm. radial distribution network, multiobjective optimization, reconfiguration, genetic algorithms, NSGA II
A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics
NASA Technical Reports Server (NTRS)
Kobayashi, Takahisa; Simon, Donald L.
2001-01-01
In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.
A high fuel consumption efficiency management scheme for PHEVs using an adaptive genetic algorithm.
Lee, Wah Ching; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei; Wu, Chung Kit; Chui, Kwok Tai; Lau, Wing Hong; Leung, Yat Wah
2015-01-01
A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day. PMID:25587974
Thomas, Clayton L.; Lee, Shaun W.
2015-01-01
Mucopolysaccharidosis type IIIA (MPS-IIIA, Sanfilippo syndrome) is a Lysosomal Storage Disease caused by cellular deficiency of N-sulfoglucosamine sulfohydrolase (SGSH). Given the large heterogeneity of genetic mutations responsible for the disease, a comprehensive understanding of the mechanisms by which these mutations affect enzyme function is needed to guide effective therapies. We developed a multiparametric computational algorithm to assess how patient genetic mutations in SGSH affect overall enzyme biogenesis, stability, and function. 107 patient mutations for the SGSH gene were obtained from the Human Gene Mutation Database representing all of the clinical mutations documented for Sanfilippo syndrome. We assessed each mutation individually using ten distinct parameters to give a comprehensive predictive score of the stability and misfolding capacity of the SGSH enzyme resulting from each of these mutations. The predictive score generated by our multiparametric algorithm yielded a standardized quantitative assessment of the severity of a given SGSH genetic mutation toward overall enzyme activity. Application of our algorithm has identified SGSH mutations in which enzymatic malfunction of the gene product is specifically due to impairments in protein folding. These scores provide an assessment of the degree to which a particular mutation could be treated using approaches such as chaperone therapies. Our multiparametric protein biogenesis algorithm advances a key understanding in the overall biochemical mechanism underlying Sanfilippo syndrome. Importantly, the design of our multiparametric algorithm can be tailored to many other diseases of genetic heterogeneity for which protein misfolding phenotypes may constitute a major component of disease manifestation. PMID:25807448
Li, P-C; Chiang, Y-Y; Tsai, K-S; Young, S-T
2005-09-01
Speech audiometric tests have been widely used for advanced hearing diagnoses and in rehabilitation. However, there are no standardised speech tests for more than 90% of the world's population, who do not speak English. A major problem in the design of a speech audiometric test is that the selection of test materials is subject to multiple criteria, and its complexity rises dramatically as the structure of test items changes from phonemic or monosyllabic forms to disyllabic or polysyllabic forms. A genetic algorithm is presented that can automatically select a set of disyllabic words from a large Mandarin corpus. The selection accords with the following principal criteria for the items constituting a speech discrimination test: similarity in structure, familiarity to the subjects, and a phonemically balanced composition. The performance of the genetic algorithm was evaluated by computation of the distance between a target vector, specifying the desired distribution of initial and final syllables and tone patterns for daily disyllabic word usage, and the vector derived by the search results of the algorithm. The use of the genetic algorithm was illustrated by its application to the selection of test lists from two Mandarin corpora. The results showed that, for a given corpus, at least 12 disyllabic word lists with a distance of less than 20 could be generated within 72 h. The genetic algorithm performed an efficient, robust and low-complexity search of the problem space and can be easily modified to adapt to the material selection of other languages. PMID:16411638
Optimal placement of tuning masses on truss structures by genetic algorithms
NASA Technical Reports Server (NTRS)
Ponslet, Eric; Haftka, Raphael T.; Cudney, Harley H.
1993-01-01
Optimal placement of tuning masses, actuators and other peripherals on large space structures is a combinatorial optimization problem. This paper surveys several techniques for solving this problem. The genetic algorithm approach to the solution of the placement problem is described in detail. An example of minimizing the difference between the two lowest frequencies of a laboratory truss by adding tuning masses is used for demonstrating some of the advantages of genetic algorithms. The relative efficiencies of different codings are compared using the results of a large number of optimization runs.
Simultaneous optimization of the cavity heat load and trip rates in linacs using a genetic algorithm
NASA Astrophysics Data System (ADS)
Terzi?, Balša; Hofler, Alicia S.; Reeves, Cody J.; Khan, Sabbir A.; Krafft, Geoffrey A.; Benesch, Jay; Freyberger, Arne; Ranjan, Desh
2014-10-01
In this paper, a genetic algorithm-based optimization is used to simultaneously minimize two competing objectives guiding the operation of the Jefferson Lab's Continuous Electron Beam Accelerator Facility linacs: cavity heat load and radio frequency cavity trip rates. The results represent a significant improvement to the standard linac energy management tool and thereby could lead to a more efficient Continuous Electron Beam Accelerator Facility configuration. This study also serves as a proof of principle of how a genetic algorithm can be used for optimizing other linac-based machines.
Effect of Population Size in Extended Parameter-Free Genetic Algorithm
NASA Astrophysics Data System (ADS)
Adachi, Susumu
We propose an extended parameter-free genetic algorithm. The first step of this study is that each individual includes additional gene whose phenotype indicates a mutation rate. The second step is an extension of the selection rule of the parameter-free genetic algorithm, in which each individual has a characteristic neighborhood radius and the individuals generated near the parents are not selected to avoid trapping a local minimum. The characteristic neighborhood radius of an individual is given by the distance between before mutation and after mutation. As a result of the experiment for function minimization problems, effect of the population size appears and the success rate is improved.
Neural network and genetic algorithm technology in data mining of manufacturing quality information
NASA Astrophysics Data System (ADS)
Song, Limei; Qu, Xing-Hua; Ye, Shenghua
2002-03-01
Data Mining of Manufacturing Quality Information (MQI) is the key technology in Quality Lead Control. Of all the data mining methods, Neural Network and Genetic Algorithm is widely used for their strong advantages, such as non-linear, collateral, veracity etc. But if you singly use them, there will be some limitations preventing your research, such as convergence slowly, searching blindness etc. This paper combines their merits and use Genetic BP Algorithm in Data Mining of MQI. It has been successfully used in the key project of Natural Science Foundation of China (NSFC) - Quality Control and Zero-defect Engineering (Project No. 59735120).
Multi-objective Optimal Public Investment: An Extended Model and Genetic Algorithm-Based Case Study
Lei Tian; Liyan Han; Hai Huang
2007-01-01
Under the multi-region and multi-sector consideration, the previous double-objective optimal public investment model is extended\\u000a to involve optimal employment rate objective and time-flow total income maximization objective first. Then genetic algorithm\\u000a is applied to solve the multi-objective model. Finally a case study is carried out to verify the superiority of the genetic\\u000a algorithm-based solution to traditional public investment distribution approach.
Genetic algorithm optimization of a large U.K. coal mine ventilation network
Yang, Z.Y.; Lowndes, I.S.; Denby, B.
1999-07-01
This paper reports on results of an application of a genetic algorithm to the optimization of a large UK Coal Mine Ventilation Network. The genetic algorithm technique has been developed into a computer program for minimizing the total network operating fan power costs. The application of booster fans may become an attractive alternative for ventilation engineers to provide an adequate supply of fresh air around the working areas in some deep and/or extensive mines. The objective of this research is to minimize the total power consumption of a ventilation system by determining the optimum combinations of (1) main fan and booster fans ratings and (2) booster fan position(s). A modular computer program, which combines the application of the genetic algorithm optimization technique together with a ventilation network simulator, has been developed using the C++ language. The ventilation network simulator uses the standard hardy-cross iterative scheme implicit within the VNET mine ventilation software that was developed at the University of Nottingham. This paper presents detail of a study on an extensive UK coal mine ventilation network. The ventilation of this network is investigated using various configurations--a single main surface fan, or a main surface fan with either a single, two or three underground booster fans. The paper highlights the major genetic operators that are used to evolve the optimum solution. It is concluded that the genetic algorithm approach is an efficient and flexible solution method.
NASA Astrophysics Data System (ADS)
Dai, Shao-sheng; Liu, Jin-song; Xiang, Hai-yan; Du, Zhi-hui; Liu, Qin
2014-07-01
Aiming at these disadvantages like lack of details, poor contrast and blurry edges of infrared images reconstructed by traditional controllable microscanning super-resolution reconstruction (SRR), this paper proposes a novel algorithm, which samples multiple low-resolution images (LRIs) by uncontrollable microscanning, and then uses LRIs as chromosomes of genetic algorithm (GA). After several generations of evolution, optimal LRIs are got to reconstruct the high-resolution image (HRI). The experimental results show that the average gradient of the image reconstructed by the proposed algorithm is increased to 1.5 times of that of the traditional SRR algorithm, and the amounts of information, the contrast and the visual effect of the reconstructed image are improved.
Evaluation of Genetic Algorithm Concepts Using Model Problems. Part 2; Multi-Objective Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.
2003-01-01
A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of simple model problems. Several new features including a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all optimization problems attempted. The binning algorithm generally provides pareto front quality enhancements and moderate convergence efficiency improvements for most of the model problems. The gene-space transformation procedure provides a large convergence efficiency enhancement for problems with non-convoluted pareto fronts and a degradation in efficiency for problems with convoluted pareto fronts. The most difficult problems --multi-mode search spaces with a large number of genes and convoluted pareto fronts-- require a large number of function evaluations for GA convergence, but always converge.
Optimal tracking controller for an autonomous wheeled mobile robot using fuzzy genetic algorithm
NASA Astrophysics Data System (ADS)
Kim, Sangwon; Park, Chongkug
2005-12-01
This paper deals with development of a kinematics model, a trajectory tracking, and a controller of fuzzy-genetics algorithm for 2-DOF Wheeled Mobile Robot (WMR). The global inputs to the WMR are a reference position, P r= (x r,y r,? r) t and a reference velocity q r=(v r,? r) t, which are time variables. The global output of WMR is a current posture P c= (x c,y c,? c) t. The position of WMR is estimated by dead-reckoning algorithm. Dead-reckoning algorithm can determine present position of WMR in real time by adding up the increased position data to the previous one in sampling period. The tracking controller makes position error to be converged 0. In order to reduce position error, a compensation velocities q=(v,?) t on the track of trajectory is necessary. Therefore, a controller using fuzzy-genetic algorithm is proposed to give velocity compensation in this system. Input variables of two fuzzy logic controllers (FLCs) are position errors in every sampling time. The output values of FLCs are compensation velocities. Genetic algorithms (GAs) are implemented to adjust the output gain of fuzzy logic. The computer simulation is performed to get the result of trajectory tracking and to prove efficiency of proposed controller.
Mohamad, Mohd Saberi; Abdullah, Afnizanfaizal
2015-01-01
This paper presents an in silico optimization method of metabolic pathway production. The metabolic pathway can be represented by a mathematical model known as the generalized mass action model, which leads to a complex nonlinear equations system. The optimization process becomes difficult when steady state and the constraints of the components in the metabolic pathway are involved. To deal with this situation, this paper presents an in silico optimization method, namely the Newton Cooperative Genetic Algorithm (NCGA). The NCGA used Newton method in dealing with the metabolic pathway, and then integrated genetic algorithm and cooperative co-evolutionary algorithm. The proposed method was experimentally applied on the benchmark metabolic pathways, and the results showed that the NCGA achieved better results compared to the existing methods. PMID:25961295
Ismail, Mohd Arfian; Deris, Safaai; Mohamad, Mohd Saberi; Abdullah, Afnizanfaizal
2015-01-01
This paper presents an in silico optimization method of metabolic pathway production. The metabolic pathway can be represented by a mathematical model known as the generalized mass action model, which leads to a complex nonlinear equations system. The optimization process becomes difficult when steady state and the constraints of the components in the metabolic pathway are involved. To deal with this situation, this paper presents an in silico optimization method, namely the Newton Cooperative Genetic Algorithm (NCGA). The NCGA used Newton method in dealing with the metabolic pathway, and then integrated genetic algorithm and cooperative co-evolutionary algorithm. The proposed method was experimentally applied on the benchmark metabolic pathways, and the results showed that the NCGA achieved better results compared to the existing methods. PMID:25961295
Wagner F. Sacco; Marcelo D. Machado; Cláudio M. N. A. Pereira; Roberto Schirru
2004-01-01
This article extends previous efforts on genetic algorithms (GAs) applied to a core design optimization problem. We introduce the application of a new Niching Genetic Algorithm (NGA) to this problem and compare its performance to these previous works. The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average
Comparison of Multi-Objective Genetic Algorithms in Optimizing Q-Law Low-Thrust Orbit Transfers
Arizona, University of
Comparison of Multi-Objective Genetic Algorithms in Optimizing Q-Law Low-Thrust Orbit Transfers.Lee@jpl.nasa.gov ABSTRACT Multi-objective genetic algorithms (MOGA) are used to optimize a low-thrust spacecraft control law and distribution of the resulting Pareto front for the low-thrust orbit-transfer optimization problem. The new
Hao Li; Qiuyun Mo; Zhilin Zhao
2010-01-01
A novel direct torque control strategy, using genetic algorithm on-line to optimize the fuzzy PI controller, is proposed. In this approach, according to speed error and its first time derivative, the proportional coefficient Kp and integral coefficient Ki can be on-line adjusted by fuzzy adaptive PI speed regulation, and the fuzzy logic adapter parameters are optimized by genetic algorithm to
New Developments in Genetic Algorithms for Developing Software
Yousif Al-Bastaki
2003-01-01
tr ra ac ct t: : Genetic Programming is the extension of the genetic model of learning the space of programs. These programs are expressed as trees. In this work we use another representation, where, in contrast to the above, the chromosomes are a list of integers representing the programs rather than a tree representation. This is called GADS (Genetic
A Study of Penalty Function Methods for Constraint Handling with Genetic Algorithm
NASA Technical Reports Server (NTRS)
Ortiz, Francisco
2004-01-01
COMETBOARDS (Comparative Evaluation Testbed of Optimization and Analysis Routines for Design of Structures) is a design optimization test bed that can evaluate the performance of several different optimization algorithms. A few of these optimization algorithms are the sequence of unconstrained minimization techniques (SUMT), sequential linear programming (SLP) and the sequential quadratic programming techniques (SQP). A genetic algorithm (GA) is a search technique that is based on the principles of natural selection or "survival of the fittest". Instead of using gradient information, the GA uses the objective function directly in the search. The GA searches the solution space by maintaining a population of potential solutions. Then, using evolving operations such as recombination, mutation and selection, the GA creates successive generations of solutions that will evolve and take on the positive characteristics of their parents and thus gradually approach optimal or near-optimal solutions. By using the objective function directly in the search, genetic algorithms can be effectively applied in non-convex, highly nonlinear, complex problems. The genetic algorithm is not guaranteed to find the global optimum, but it is less likely to get trapped at a local optimum than traditional gradient-based search methods when the objective function is not smooth and generally well behaved. The purpose of this research is to assist in the integration of genetic algorithm (GA) into COMETBOARDS. COMETBOARDS cast the design of structures as a constrained nonlinear optimization problem. One method used to solve constrained optimization problem with a GA to convert the constrained optimization problem into an unconstrained optimization problem by developing a penalty function that penalizes infeasible solutions. There have been several suggested penalty function in the literature each with there own strengths and weaknesses. A statistical analysis of some suggested penalty functions is performed in this study. Also, a response surface approach to robust design is used to develop a new penalty function approach. This new penalty function approach is then compared with the other existing penalty functions.
Coello, Carlos A. Coello
Fuzzy-Pareto-Dominance Driven Multiobjective Genetic Algorithm Mario KÂ¨oppen, Katrin Franke. The approach is based on fuzzifi- cation of Pareto dominance relation. Using fuzzy degrees of dominance, a set and evaluated on benchmark function. Keywords: Multiobjective Optimization, Fuzzy Pareto Dominance, Evolutionary
Design and Implementation of Genetic Algorithms for Solving Problems in the Biomedical Sciences
Levin, Michael
2 Design and Implementation of Genetic Algorithms for Solving Problems in the Biomedical Sciences 200 Longwood Ave. Boston, MA 02115 #12;3 Abstract Many problems in the biomedical sciences can be re of resources such as public- domain (freely-available) GA software and various internet-based GA advice groups
"Offshore Wind farm layout optimization using a Genetic Algorithm" Michael Ameckson
Mountziaris, T. J.
"Offshore Wind farm layout optimization using a Genetic Algorithm" Michael Ameckson Faculty Mentor a large scale systems perspective, thinking about how a large number of wind farms should be sited considering their interactions. We are developing a model for large scale offshore wind farm planning using
Wu, Jeff
Identifying Promising Compounds in Drug Discovery: Genetic Algorithms and Some New Statistical are asked to prioritize compounds for subsequent stages of the drug discovery process, given results from for a priori scientific knowledge; compounds can then be prioritized based on their desirability scores
Natural Language Grammar Induction of Indonesian Language Corpora Using Genetic Algorithm
Arya Tandy Hermawan; Gunawan; Joan Santoso
2011-01-01
Grammar Induction is a machine learning process for learning grammar from corpora. This paper will discuss the process of grammar induction for Indonesian language corpora using genetic algorithm. The Grammar production rules will be modeled in the form of chromosomes. The fitness function is used to count how many sentences can be parsed. The data used are Indonesian fairy tales
A Hybrid Genetic Algorithm with Pattern Search for Finding Heavy Atoms in Protein Crystals
Eppstein, Margaret J.
A Hybrid Genetic Algorithm with Pattern Search for Finding Heavy Atoms in Protein Crystals Joshua L is a technique called iso-morphous replacement, in which crystallographers dope protein crystals with heavy atoms with and without the heavy atoms, the locations of the heavy atoms can be estimated. Once the locations
Improving the Diversity Defense of Genetic Algorithm-Based Moving Target Approaches
Erway, Jennifer
Improving the Diversity Defense of Genetic Algorithm-Based Moving Target Approaches Michael B) defense is to intermediately change a system's configuration (operating systems and/or applications defense strategy [2]. A Moving Target (MT) defense is a way to disrupt the reconnaissance phase of a cyber
Optimisation of the Gas-Exchange System of Combustion Engines by Genetic Algorithm
Marsland, Stephen
Optimisation of the Gas-Exchange System of Combustion Engines by Genetic Algorithm C. D. Rose, S. R of combustion engine gas-exchange systems still predominantly use trial and error. This paper proposes a new. INTRODUCTION The gas-exchange system is a primary factor in the performance of a combustion engine. Designing
AUTONOMOUS ROBOT NAVIGATION USING A GENETIC ALGORITHM WITH AN EFFICIENT GENOTYPE ADITIA HERMANU
Wainwright, Roger L.
1 AUTONOMOUS ROBOT NAVIGATION USING A GENETIC ALGORITHM WITH AN EFFICIENT GENOTYPE STRUCTURE ADITIA path in minimal time while avoiding obstacles in a navigation environment. Autonomous navigation allows that represents paths in the navigation environment. The genotype must represent a valid path, but still be simple
Design of electrically small wire antennas using a pareto genetic algorithm
Hosung Choo; Robert L. Rogers; Hao Ling
2005-01-01
We report on the use of a genetic algorithm (GA) in the design optimization of electrically small wire antennas, taking into account of bandwidth, efficiency and antenna size. For the antenna configuration, we employ a multisegment wire structure. The Numerical Electromagnetics Code (NEC) is used to predict the performance of each wire structure. To efficiently map out this multiobjective problem,
Hosung Choo; Robert Rogers; Hao Ling
2002-01-01
Altshuler (see (see 2001 USNC\\/URSI Nation Radio Science Meeting, p.226, Salt Lake City, UT, July 2000)) reported on the use of genetic algorithm (GA) for designing electrically small wire antennas. The wire configuration that results in maximum bandwidth was generated using the GA for a given antenna size. However, it is well known that antenna miniaturization impacts antenna efficiency as
Supersonic Business Jet Design using a Knowledge-Based Genetic Algorithm with an
Stanford University
Supersonic Business Jet Design using a Knowledge-Based Genetic Algorithm with an Adaptive 94305 In the design of supersonic low-boom aircraft, it is important to balance the aero- dynamic cost and characteristics of such an approach for the design optimization of a low-boom supersonic
A Dynamic Island-Based Genetic Algorithms Frederic Lardeux and Adrien Goeffon
Goëffon, Adrien
A Dynamic Island-Based Genetic Algorithms Framework Fr´ed´eric Lardeux and Adrien Go¨effon LERIA.lardeux@univ-angers.fr adrien.goeffon@univ-angers.fr Abstract. This work presents a dynamic island model framework for helping, the possible migrations among islands are represented by a complete graph. The migrations probabilities
Credit card fraud detection: An application of the gene expression messy genetic algorithm
Kargupta, H.; Gattiker, J.R.; Buescher, K.
1996-05-01
This paper describes an application of the recently introduced gene expression messy genetic algorithm (GEMGA) (Kargupta, 1996) for detecting fraudulent transactions of credit cards. It also explains the fundamental concepts underlying the GEMGA in the light of the SEARCH (Search Envisioned As Relation and Class Hierarchizing) (Kargupta, 1995) framework.
Enhancement of the Shifting Balance Genetic Algorithm for Highly Multimodal Problems
Wineberg, Mark
diversity usually can be done in two ways: either by decelerating the process of gene fixation of the Genetic Algorithm (GA) that was created to promote guided diversity to improve performance in highly. To a great degree lessening premature convergence can be seen as the problem of diversity maintenance. Many
Price prediction of target of mergers and acquisitions based on genetic-algorithm BP neural network
Hongjiu Liu; Weimin Ma
2009-01-01
In order to predict the price of candidates in acquisition and evaluate its feasibility, this paper puts forward a model of price prediction of candidates based on genetic-algorithm and BP neural network. The model is trained by the data of market deals which were made in the past. The result of simulation and test indicates that average error of prediction
Oil-pumping system control using nonlinear homotopy BP neural network and genetic algorithm
Ying Li; Yuanchun Li; Guangjun Liu
2005-01-01
Under loading and empty pumping problems are associated with pumping unit of oil wells and cause waste of energy and inefficient usage of equipment. To solve these problems, a control method combining neural network (NN) and genetic algorithm (GA) is proposed and applied to intermittent oil-pumping control. Especially, the nonlinear homotopy BP NN is proposed to improve the convergence speed
Partial abductive inference in Bayesian belief networks using a genetic algorithm q
de Campos, Luis M.
Partial abductive inference in Bayesian belief networks using a genetic algorithm q L.M. de Campos, Spain Abstract Abductive inference in Bayesian belief networks is the process of generating the u most's variables, this problem is called partial abductive inference. Both problems are NP-hard, and so exact
Building Logistics Cost Forecast Based on High Speed and Precise Genetic Algorithm Neural Network
Meijuan Gao; Jingwen Tian; Jin Xu
2009-01-01
The building logistics cost forecasting was a complicated nonlinear problem, due to the factors that influence building logistics cost are anfratuous, and it was difficult to describe it by traditional methods. So a modeling and forecasting method of building logistics cost based on high speed and precise genetic algorithm neural network is presented in this paper. The high speed and
APPLYING GENETIC ALGORITHMS TO THE U-SHAPED ASSEMBLY LINE BALANCING PROBLEM
Wainwright, Roger L.
APPLYING GENETIC ALGORITHMS TO THE U-SHAPED ASSEMBLY LINE BALANCING PROBLEM Debora A. Ajenblit to a station only after all its predecessors have been assigned to stations. The U-shaped assembly line balancing problem is a relatively new problem derived from the traditional assembly line balancing problem
Optimization of trusses using genetic algorithms for discrete and continuous variables
M. R. Ghasemi; E. Hinton; R. D. Wood
1999-01-01
This paper demonstrates the use of genetic algorithms (GAs) for size optimization of trusses. The concept of rebirthing is shown to be considerably effective for problems involving continuous design variables. Some benchmark examples are studied involving 4-bar, 10-bar, 64-bar, 200-bar and 940-bar two-dimensional trusses. Both continuous and discrete variables are considered.
Using Genetic Algorithms to Optimize Social Robot Behavior for Improved Pedestrian Flow
Maciejewski, Anthony A. "Tony"
to the simulation of pedestrian crowds. Each person or robot is treated as a circular particle with a particularUsing Genetic Algorithms to Optimize Social Robot Behavior for Improved Pedestrian Flow Bryce D on previous research on the effect of introducing social robots into crowded situations in order to improve
Gene Selection for Microarray Data by a LDA-based Genetic Algorithm
Hao, Jin-Kao
Gene Selection for Microarray Data by a LDA-based Genetic Algorithm Edmundo Bonilla Huerta, B,bd,hao}@info.univ-angers.fr Abstract. Gene selection aims at identifying a (small) subset of infor- mative genes from the initial data classification accuracies (81%-100%) with a very small number of genes (2-19). Keywords : Linear discriminant
Alexander V. Halevin
2003-09-05
We present population synthesis modeling of the X-ray background with genetic algorithm - based optimization method. In our models the best fit could be achieved for lower values of high-energy exponential cut-off (~ 170 keV) and larger amount of the highly obscured (log N_H=25.5) AGNs.
A Study and Improvement of the Genetic Algorithm in the CAMBrain Machine
Gent, Universiteit
A Study and Improvement of the Genetic Algorithm in the CAMBrain Machine Yvan Saeys and Herwig Van of the CAMBrain Machine (CBM), a hardware tool which implements a cellular automata based neural network. 1 Introduction In the domain of artificial intelligence, there has already been a lot of research
A Method Based on Genetic Algorithm for Anti-ship Missile Path Planning
Xuechun Zhao; Xiaohong Fan
2009-01-01
This paper presented a novel approach to search and optimize path points for anti-ship missile path planning. We utilized the method of MAKLINK graph to construct free space, and then, a global state connected graph is built up for searching for all possible routes. genetic algorithm is used to search and optimize path points severally in these local routes. According
An application of genetic algorithms to geometric model-guided interpretation of brain anatomy
Peter E. Undrill; Kostas Delibasis; George G. Cameron
1997-01-01
This work applies 3D Fourier Descriptors (FDs) and Genetic Algorithms (GAs) to the optimisation of the shape and position of models of anatomical objects within the human brain. Using magnetic resonance image data, we perform an approximate segmentation on one lateral ventricle and use the FDs from this as seeding values for the GAs to search for the left and
Web Service Selection using Particle Swarm Optimization and Genetic Algorithms Simone A. Ludwig
Ludwig, Simone
Web Service Selection using Particle Swarm Optimization and Genetic Algorithms Simone A. Ludwig time, high reliability, and availability. This paper investigates service selection, and proposes two. Traditional approaches to service discovery and selection have generally relied on the existence of pre
Application of a genetic algorithm to meter allocation in electric power systems
Hiroyuki Mori; Seiji Iida
1993-01-01
This paper presents a genetic algorithm (GA)-based approach to meter allocation in electric power systems. State estimation plays a key role in power system security control. The accuracy of the state estimation is highly influenced by meter allocation. In this paper, meter allocation of redundant measurements are examined to improve the accuracy. The proposed method has been successfully applied to
Application of Genetic Algorithms in Colombian Interconnected Power System Operative Planning
Jaime A. Valencia; Walter M. Villa; Esteban Velilla; G. Marin A; José I. Gutiérrez; Mónica M. Montoya
2009-01-01
Each interconnected electric system has to define its operating mode and how expansion and operation planning are made. This paper presents the first phase of the work of implementing a tool with application to Colombian power system operative planning. The tool uses genetic algorithms to optimize the cost functions that arise in which the elements of the system are the
Protein Structure Alignment Using a Genetic Algorithm Joseph D. Szustakowski and Zhiping Weng*
Weng, Zhiping
Protein Structure Alignment Using a Genetic Algorithm Joseph D. Szustakowski and Zhiping Weng a novel, fully automatic method for aligning the three-dimensional structures of two proteins. The basic approach is to first align the proteins' secondary structure elements and then extend the alignment
Design of a Metafilm-composite Dielectric Shielding Structure Using a Genetic Algorithm
Jingyu Huang; M. Y. Koledintseva; P. C. Ravva; J. L. Drewniak; R. E. DuBroff; B. Archambeault
2006-01-01
An analytical model for a shielding structure containing both bulk composite layers and planar metafilms (MFs) made of perfect electric conductors is presented, allowing for synthesis of shielding structures using the genetic algorithm (GA) optimization. MFs can be of two different types: patch or aperture. The frequency response, specifically, transmission (T) and reflection ( ) coefficients in a plane-wave formulation,
Papalambros, Panos
Interactive Genetic Algorithms for use as Creativity Enhancement Tools Jarod Kelly and Panos Y Ann Arbor, MI 48109-1109 Abstract It is proposed that creativity can be enhanced through the use in creativity that we simulate through two separate IGA populations developed by different means. The conver
Louis, Sushil J.
Seismic Velocity Inversion with Genetic Algorithms Sushil J. Louis Qinxue Chen Satish to compute travel times for seismic waves. However, in practice, we have to solve the inverse problem: travel synthetic seismic models shows that large population sizes are crit- ical to generating good seismic
Genetic algorithms based robust frequency estimation of sinusoidal signals with stationary errors
Kundu, Debasis
Genetic algorithms based robust frequency estimation of sinusoidal signals with stationary errors t In this paper, we consider the fundamental problem of frequency estimation of multiple sinusoidal signals for the frequency estimation problem. In the simulation studies and real life data analysis, it is observed
Cyclic Genetic Algorithm with Conditional Branching in a Predator-Prey Scenario
Parker, Gary B.
Cyclic Genetic Algorithm with Conditional Branching in a Predator-Prey Scenario Gary Parker with conditional branching to generate a controller for the predator in a predator-prey scenario. Keywords importantly, is a means for learning adaptive control in a changing environment. In past research, control
Design of Genetic Algorithm Based Fuzzy Logic Power System Stabilizers in Multimachine Power System
Manisha Dubey
2008-01-01
This paper presents the design of fuzzy logic power system stabilizers using genetic algorithms in multimachine power system. In the proposed fuzzy expert system, generator speed deviation and acceleration are chosen as input signals to fuzzy logic power system stabilizer. In this approach gains, centers of membership functions and the parameters of the fuzzy logic controllers have been tuned using
Manisha Dubey; A. Dubey
2006-01-01
This paper presents the simultaneous tuning of power system stabilizers in a multi-machine power system. The problem of selecting the parameters of power system stabilizers in converted into an optimization problem that is solved by genetic algorithm using eigen value based objective function. The dynamic performance of the system has been investigated under small perturbation and large disturbance. The performance
Using Genetic Algorithms to Explore Pattern Recognition in the Immune System
New Mexico, University of
Using Genetic Algorithms to Explore Pattern Recognition in the Immune System DRAFT July 28, 1993 an immune system model based on binary strings. The purpose of the model is to study the pattern recognition processes and learning that take place at both the individual and species levels in the immune system