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Geneticalgorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic geneticalgorithms concepts are introduced, geneticalgorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of geneticalgorithm technology.

Geneticalgorithms (GAs) are search methods based on principles of natural selection and genetics (Fraser, 1957;Bremermann, 1958;Holland, 1975). We start with a brief introduction to simple geneticalgorithms and associated terminology.

This tutorial covers the canonical geneticalgorithm as well as more experimentalforms of geneticalgorithms, including parallel island models and parallel cellular geneticalgorithms. The tutorial also illustrates genetic search by hyperplane sampling. Thetheoretical foundations of geneticalgorithms are reviewed, include the schema theoremas well as recently developed exact models of the canonical geneticalgorithm.Keywords: GeneticAlgorithms, Search, Parallel Algorithms1 Introduction...

Geneticalgorithms, search algorithms based on the genetic processes observed in natural evolution, have been used to solve difficult problems in many different disciplines. When applied to very large-scale problems, geneticalgorithms exhibit high computational cost and degradation of the quality of the solutions because of the increased complexity. One of the most relevant research trends in geneticalgorithms is

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.

We analyze the performance of a geneticalgorithm (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

(i) We investigate spectral and geometric properties of the mutation-crossover operator in a geneticalgorithm with general-size alphabet. By computing spectral estimates, we show how the crossover operator enhances the averaging procedure of the mutation operator in the random generator phase of the geneticalgorithm. By mapping our model to the multi-set model often investigated in the literature, we compute

This paper presents stochastic models for two classes of GeneticAlgorithms. We present important distinctions throughout between classes of GeneticAlgorithms which sample with and without replacement, in terms of their search dynamics. For both classes of algorithm, we derive sufficient conditions for convergence, and analyse special cases of GeneticAlgorithm optimisation. We also derive a long-run measure of crossover

This paper introduces the compact geneticalgorithm (cGA) which represents the population as a probability distribu- tion over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. It processes each gene independently and requires less memory than the simple GA. The development of the compact GA is guided by a

Georges R. Harik; Fernando G. Lobo; David E. Goldberg

This paper introduces the “compact geneticalgorithm” (cGA). The cGA represents the population as a probability distribution over the set of solutions, and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. It processes each gene independently and requires less memory than the simple GA

Hybrid geneticalgorithms (GAs) for the graph partitioning problem are described. The algorithms include a fast local improvement heuristic. One of the novel features of these algorithms is the schema preprocessing phase that improves GAs' space searching capability, which in turn improves the performance of GAs. Experimental tests on graph problems with published solutions showed that the new geneticalgorithms

The first part of this presentation is a tutorial level introduction to the principles of genetic search and models of simple geneticalgorithms. The second half covers the combination of geneticalgorithms with local search methods to produce hybrid geneticalgorithms. Hybrid algorithms can be modeled within the existing theoretical framework developed for simple geneticalgorithms. An application of a hybrid to geometric model matching is given. The hybrid algorithm yields results that improve on the current state-of-the-art for this problem.

Large arrays are difficult to thin in order to obtain low sidelobes. Traditional statistical methods of aperiodic array synthesis fall far short of optimum configurations. Traditional optimization methods are not well suited for optimizing a large number of parameters or discrete parameters. This paper presents how to optimally thin an array using geneticalgorithms. The geneticalgorithm determines which elements

We address deceptiveness, one of at least four reasons geneticalgorithms can fail to converge to function optima. We construct fully deceptive functions and other functions of intermediate deceptiveness. For the fully deceptive functions of our construction, we generate linear transformations that induce changes of representation to render the functions fully easy. We further model geneticalgorithm selection recombination as

Messy geneticalgorithms define a rare class of algorithms that realize the need for detecting appropriate relations among members of the search domain in optimization. This paper reviews earlier works in messy geneticalgorithms and describes some recent developments. It also describes the gene expression messy GA (GEMGA)--an {Omicron}({Lambda}{sup {kappa}}({ell}{sup 2} + {kappa})) sample complexity algorithm for the class of order-{kappa} delineable problems (problems that can be solved by considering no higher than order-{kappa} relations) of size {ell} and alphabet size {Lambda}. Experimental results are presented to demonstrate the scalability of the GEMGA.

Kargupta, H. [Los Alamos National Lab., NM (United States). Computational Science Methods Group

According to the information processing mechanism of immune system in life sciences, based on simple geneticalgorithm, a new approach of immune geneticalgorithm for job shop scheduling is proposed through combining immune algorithm with improved geneticalgorithm (strategy of multiple crossover per couple with incest prevention). A immune geneticalgorithm aiming at job shop scheduling is set up. The

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. Geneticalgorithms (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.

Fennel, Theron R.; Underbrink, A. J., Jr.; Williams, George P. W., Jr.

This paper presents a tutorial and overview of geneticalgorithms for electromagnetic optimization. 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 the GA is discussed. Step-by-step implementation aspects of the GA are detailed, through an example with the objective of providing useful guidelines for

Geneticalgorithm (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.

In this paper, we solve the motion smoothing problem using geneticalgorithms. Smooth motion generation is essential in the computer animation and virtual reality area. The motion of a rigid body in general consists of translation and orientation. The former is described by a space curve in three-dimensional Euclidean space while the latter is represented by a curve in the

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 geneticalgorithm. Several examples demonstrate the utility of this method. 14 refs., 8 figs.

Benson, R.W.; Schmitendorf, W.E. (California Univ., Irvine, CA (USA). Dept. of Mechanical Engineering)

Geneticalgorithms (GAs) have proved to be a versatile and effective approach for solvingcombinatorial optimization problems. Nevertheless, there are many situations in which thesimple GA does not perform particularly well, and various methods of hybridization have beenproposed. These often involve incorporating other methods such as simulated annealing orlocal optimization as an `add-on\\

The present paper has the goal of studying orbital maneuvers of Rendezvous, that is an orbital transfer where a spacecraft has to change its orbit to meet with another spacecraft that is travelling in another orbit. This transfer will be accomplished by using a multi-impulsive control. A geneticalgorithm is used to find the transfers that have minimum fuel consumption.

Souza dos Santos, Denílson Paulo; Rodrigo Barretto Teodoro, Anderson; Bertachini de Almeida Prado, Antônio F.

In recent years geneticalgorithm (GA) was used successfully to solve many optimization problems. One of the most difficult questions of applying GA to a particular problem is that of coding. In this Paper a scheme is derived to optimize one aspect of the coding in an automatic fashion. This is done by using a high cardinality alphabet and optimizing

function of the structure being evaluated, and the learning task can be viewed as an optimization problem in a noisy environment. Previous studies have shown that geneticalgorithms can perform effectively in the presence of noise. This work ex- plores in detail the tradeoffs between the amount of effort spent on evaluating each structure and the number of structures evaluated

This paper considers deception in the context of ordering geneticalgorithms (GAs). Order-four deceptive ordering problems are designed for absolute and relative ordering decoding. Three different crossover operators are used in both absolute and relative or- dering problems, and for each combination of crossover operator and coding, the schema survival probability is calculated. Simulation results show that no single crossover

Few investigations of the geneticalgorithm (GA) have been studied for the real-world power economic load dispatch (PELD) problem. This paper proposes an improved geneticalgorithm with multiplier updating (IGAMU) to solve practical PELD problems of complexity having nonconvex cost curves where conventional mathematical methods are inapplicable. The proposed IGAMU integrates the improved geneticalgorithm (IGA) and the multiplier updating

This article is a tutorial on using geneticalgorithms to optimize antenna and scattering patterns. Geneticalgorithms are “global” numerical-optimization methods, patterned after the natural processes of genetic recombination and evolution. The algorithms encode each parameter into binary sequences, called a gene, and a set of genes is a chromosome. These chromosomes undergo natural selection, mating, and mutation, to arrive

A geneticalgorithm technique was implemented to determine a set of unknown parameters that best matched the Blaze II chemical laser model predictions with experimental data. This is the first known application of the geneticalgorithm technique for modeling lasers, chemically reacting flows, and chemical lasers. Overall, the geneticalgorithm technique worked exceptionally well for this chemical laser modeling problem

Geneticalgorithms are highly parallel, adaptive search procedures (i.e., problem-solving methods) loosely based on the processes of population genetics and Darwinian survival of the fittest. Geneticalgorithms have proven useful in domains where other optimization techniques perform poorly. The main purpose of the paper is to discuss a NASA-sponsored software development project to develop a general-purpose tool for using geneticalgorithms. The tool, called Splicer, can be used to solve a wide variety of optimization problems and is currently available from NASA and COSMIC. This discussion is preceded by an introduction to basic geneticalgorithm concepts and a discussion of geneticalgorithm applications.

A novel biclustering algorithm is proposed in this paper, which can be used to cluster gene expression data. One of the contributions of this paper is a novel and effective residue function of the biclustering algorithm. Furthermore, the parallel geneticalgorithm is firstly used to the algorithm of the biclustering for gene expression data. This method can avoid local convergence

Generic algorithms have gained popularity as an effective procedure for obtaining solutions to traditionally difficult space mission optimization problems. In this paper, a brief survey of the use of geneticalgorithms to solve astrodynamics problems is presented and is followed by new results obtained from applying a Pareto geneticalgorithm to the optimization of low-thrust interplanetary spacecraft missions.

Coverstone-Carroll, V.; Hartmann, J. W.; Williams, S. N.; Mason, W. J.

Traveling Salesman Problem (TSP) is an NP-hard Problem, which has many different real life applications. GeneticAlgorithms (GA) are robust and probabilistic search algorithms based on the mechanics of natural selection and survival of the fittest that is used to solve optimization and many real life problems. This paper presents GeneticAlgorithm for TSP. Moreover it also shows best suitable

The hereditary algorithm is a kind of random searching and method of optimizing based on living beings natural selection and hereditary mechanism. In recent years, because of the potentiality in solving complicate problems and the successful application in the fields of industrial project, hereditary algorithm has been widely concerned by the domestic and international scholar. Routing Selection communication has been defined a standard communication model of IP version 6.This paper proposes a service model of Routing Selection communication, and designs and implements a new Routing Selection algorithm based on geneticalgorithm.The experimental simulation results show that this algorithm can get more resolution at less time and more balanced network load, which enhances search ratio and the availability of network resource, and improves the quality of service.

In this paper a new geneticalgorithm called the Breeder GeneticAlgorithm(BGA) is introduced. The BGA is based on artificial selectionsimilar to that used by human breeders. A predictive model for the BGA ispresented which is derived from quantitative genetics. The model is used topredict the behavior of the BGA for simple test functions. Different mutationschemes are compared by computing

Excursion-set-mediated geneticalgorithm (ESMGA) is embodiment of method of searching for and optimizing computerized mathematical models. Incorporates powerful search and optimization techniques based on concepts analogous to natural selection and laws of genetics. In comparison with other geneticalgorithms, this one achieves stronger condition for implicit parallelism. Includes three stages of operations in each cycle, analogous to biological generation.

In this paper a generic geneticalgorithm processor (GAP) with high flexibility in parameter tuning is introduced. The proposed processor utilizes pipeline structure to have low processing time. In order to further increase in the speed, genetic population has been duplicated, one for replacement stage of geneticalgorithm (GA) and another for selection phase. Additionally, parallel processing method in the

This paper describes a geneticalgorithm application to the DNA fragment assembly problems. The geneticalgorithm 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 geneticalgorithm 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.

Parsons, R.; Burks, C. (Los Alamos National Lab., NM (United States)); Forrest, S. (New Mexico Univ., Albuquerque, NM (United States). Dept. of Computer Science)

Micromechanical sensors are routinely simulated using finite element software. Once a structure has ben proposed, various parameters are optimized using experience, intuition, and trial-and-error. However, using proven finite element modeling coupled with a geneticalgorithm (GA), optimal designs can be 'evolved' using a hands-free approach on a workstation. Once a problem is defined, the sole task required of the designer is the specification of a mathematical objective function expressing the desired properties of the sensor; the sensor geometry that maximizes the given function is then synthesized by the algorithm. We have developed an optimization tool and have applied it to the design of tuning fork gyroscopes (TFG). In this paper, we demonstrate how a TFG was optimized using GA's. TFG suspension beam lengths were adjusted through the robust search technique, which is resistant to trapping in local maxima. Desired vibration mode order and mode frequency separations were governed by the objective function as specified by the designer. This multidimensional nonlinear optimization problem had a solution space of over eight million possible designs. Industry-standard mechanical computer-aided engineering tools were integrate along with a GA toolbox and a web-based control interface. Designs offering reduced vibration sensitivity and increased sensor dynamic range have been produced. A tenfold decrease in total sensor optimization time has been documented, resulting in reduced development time.

Kirkos, Gregory A.; Jurgilewicz, Robert P.; Duncan, Stephen J.

For multiobjective control systems design, we use geneticalgorithms to find the Pareto optimal set of various control system performance indices. We also propose a modified multiobjective selection scheme and the use of the improved rank-based fitness assignment. By combining multiobjective geneticalgorithm (MGA) with the pole-zero placement algorithm which can avoid specified pole-zero cancellations, we construct a MATLAB based

Distributed relational database query optimisation is a combinatorial optimisation problem. This paper reports on an initial investigation into the potential for a geneticalgorithm (GA) to optimise distributed queries. A geneticalgorithm is developed and its performance compared with alternative stochastic optimisation techniques: random search, multistart and simulated annealing. The problem of fully reducing all tables in a tree query

We present the first closed loop image segmentation system which incorporates a geneticalgorithm to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions such as time of day, time of year, clouds, etc. The segmentation problem is formulated as an optimization problem and the geneticalgorithm efficiently searches the hyperspace of segmentation parameter combinations

A three-phased framework for learning dynamic system control is presented. A geneticalgorithm is employed to derive control rules encoded as decision tables. Next, the rules are automatically transformed into comprehensible form by means of inductive machine learning. Finally, a geneticalgorithm is applied again to optimize the numerical parameters of the induced rules. The approach is experimentally verified on

In this paper we describe a method for hybridiz ing a geneticalgorithm 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

This paper presents a set of experiments using a geneticalgorithm to develop solutions to a Vigenere alphabetic code. A Vigenere code replaces each character in the plain text with a new character generated by adding the value of a character in the corresponding place in a “keyword”. The geneticalgorithm uses the number of characters in valid English words

Candlestick analysis, a form of stock market technical analysis, is well suited for use with a genetic search algorithm. This paper explores an implementation of marrying these two techniques by creating agents that attempt to identify stocks that will change in price. The best of run individuals, produced by the geneticalgorithm, performed statistically better than an agent that makes

Facility layout is an important aspect of designing any manufacturing setup. However, the problem of finding optimal layouts is hard and deterministic techniques are not computationally feasible. In this work a geneticalgorithm is presented for obtaining efficient layouts. The different aspects involved in the design of efficient geneticalgorithms are discussed in detail. It is shown that the population

This paper aims at demonstrating, through examples, the applicability of geneticalgorithms to wide classes of problems encountered in astronomy and astrophysics. Geneticalgorithms are heuristic search techniques that incorporate, in a computational setting, the biological notion of evolution by means of natural selection. While increasingly in use in the fields of computer science, artificial intelligence, and computed-aided engineering design,

In this paper, the problem of automatic determination of point correspondence between two images is formulated as a multimodal function optimization and the usefulness of geneticalgorithms (GAs) as a multimodal optimizer is explored. Initially, a number of variations of GAs, capable of simultaneously discovering multiple extremes of an objective function are evaluated on a mathematical benchmark objective function with

Konstantinos K. Delibasis; Pantelis A. Asvestas; George K. Matsopoulos

In order to achieve high video coding efficiency, a new motion estimation and compensation algorithm is proposed based on GeneticAlgorithm. This algorithm exploits the uniformity and correlation in the properties of the cluster of blocks called Super-Block. These Super-Blocks have adaptive boundaries that are used to partially generate initial population for fast convergence to global minimum. Rest of the population is generated using pure Random Number Generator (RNG). This population then generates offspring which then competes within itself by the virtue of it"s fitness to survive into the next generation. The fitness value in each generation is calculated by comparing the reference frame with the predicted frame. The algorithm stops after convergence or when maximum generations are reached. This algorithm compares well against conventional algorithms like FSA (Full Search Algorithm), One-Step Method or N-Step Method in terms of number of searches, complexity, robustness and scalability.

Using geneticalgorithm 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.

Forrest, S. (New Mexico Univ., Albuquerque, NM (USA). Dept. of Computer Science); Perelson, A.S. (Los Alamos National Lab., NM (USA))

Using geneticalgorithm 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.

In this paper we present a new nature-inspired variation operator for binary encodings in geneticalgorithms (GAs). Our method, called implicit alternative splicing (iAS), is repeatedly applied to the individual encodings in the algorithm's population and inverts randomly chosen segments of decreasing size in a systematic fashion. Its goal is to determine the largest possible segment the inversion of which

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which geneticalgorithms (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...

We report on the development of general-purpose algorithms for global parameter minimization in scientific applications where comparison of results with data takes the form of a pattern recognition problem. Our basic approach is to implement model calculations for the problem of interest in parallel on a Beowulf cluster with a geneticalgorithm to optimize the parameters of the calculation and

We introduce geneticalgorithms as a means to analyze supernovae type Ia data and extract model-independent constraints on the evolution of the Dark Energy equation of state w(z) {identical_to} P{sub DE}/{rho}{sub DE}. Specifically, we will give a brief introduction to the geneticalgorithms along with some simple examples to illustrate their advantages and finally we will apply them to the supernovae type Ia data. We find that geneticalgorithms can lead to results in line with already established parametric and non-parametric reconstruction methods and could be used as a complementary way of treating SNIa data. As a non-parametric method, geneticalgorithms provide a model-independent way to analyze data and can minimize bias due to premature choice of a dark energy model.

Bogdanos, Charalampos; Nesseris, Savvas, E-mail: Charalampos.Bogdanos@th.u-psud.fr, E-mail: nesseris@nbi.dk [LPT, Universite de Paris-Sud-11, Bat. 210, 91405 Orsay CEDEX (France)] [LPT, Universite de Paris-Sud-11, Bat. 210, 91405 Orsay CEDEX (France)

This report provides documentation for matlab R ? implementation of the extended compact geneticalgorithm (eCGA). The implementation works for integer decision variables where each variable can be of differing cardinality.

Application of optimization techniques for determining the optimal operating policy of reservoirs is a major issue in water\\u000a resources planning and management. As an optimization GeneticAlgorithm, ruled by evolution techniques, have become popular\\u000a in diversified fields of science. The main aim of this study is to explore the efficiency and effectiveness of geneticalgorithm\\u000a in optimization of multi-reservoirs. A

Geneticalgorithm is one important method for solving vehicle routing problem. Geneticalgorithm has the special advantages in solving vehicle routing problem. A geneticalgorithm for logistics vehicle routing problem is designed and implemented in this paper. At the same time the key technologies such as the implementing of the geneticalgorithm based on natural number encode in the logistics

Zengyu Cai; Yuan Feng; Yong Gan; Baowei Zhang; Shuru Liu

PID parameter optimization is an important problem in control field. This paper presents a kind of fast geneticalgorithms, which have a lot of improvements about population, selection, crossover and mutation in comparison with simple geneticalgorithms. These fast geneticalgorithms are used in PID parameter optimization for common objective model to remedy flaws of simple geneticalgorithms and accelerate

Geneticalgorithms (GAs) are a class of optimization algorithms. GAs attempt to solve problems through modeling a simplified version of genetic processes. There are many problems for which a GeneticAlgorithm approach is useful. It is, however, undetermined if cryptanalysis is such a problem. Therefore, this work trying to explore the use of GeneticAlgorithms in cryptography. The focus is

Describes a C++ package used to analyze a class of geneticalgorithms. The parameters of the best geneticalgorithms have been searched by a geneticalgorithm. The ultimate goal of the work is to find out if it would be possible to utilize geneticalgorithm techniques in certain difficult and complex robot control problems, such as task planning, adaptation, error

The research proposed in this document investigated multiobjective optimization approaches based upon the GeneticAlgorithm (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 geneticalgorithm in to an N-branch geneticalgorithm, then the N-branch GA was compared with a version of the popular Multi-Objective GeneticAlgorithm (MOGA). Because the geneticalgorithm 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.

This research investigated geneticalgorithm approaches for smart actuator placement to provide aircraft maneuverability without requiring hinged flaps or other control surfaces. The effort supported goals of the Multidisciplinary Design Optimization focus efforts in NASA's Aircraft au program. This work helped to properly identify various aspects of the geneticalgorithm operators and parameters that allow for placement of discrete control actuators/effectors. An improved problem definition, including better definition of the objective function and constraints, resulted from this research effort. The work conducted for this research used a geometrically simple wing model; however, an increasing number of potential actuator placement locations were incorporated to illustrate the ability of the GA to determine promising actuator placement arrangements. This effort's major result is a useful geneticalgorithm-based approach to assist in the discrete actuator/effector placement problem.

Geneticalgorithms (GAs) are search procedures based on the mechanics of natural selection and natural genetics. They combine the use of string codings or artificial chromosomes and populations with the selective and juxtapositional power of reproduction and recombination to motivate a surprisingly powerful search heuristic in many problems. Despite their empirical success, there has been a long standing objection to the use of GAs in arbitrarily difficult problems. A new approach was launched. Results to a 30-bit, order-three-deception problem were obtained using a new type of geneticalgorithm called a messy geneticalgorithm (mGAs). Messy geneticalgorithms combine the use of variable-length strings, a two-phase selection scheme, and messy genetic operators to effect a solution to the fixed-coding problem of standard simple GAs. The results of the study of mGAs in problems with nonuniform subfunction scale and size are presented. The mGA approach is summarized, both its operation and the theory of its use. Experiments on problems of varying scale, varying building-block size, and combined varying scale and size are presented.

There is a lot of research in geneticalgorithm about structural optimization. But as far as the large multi-goal program is concerned, it limits the application of geneticalgorithm for the reason of its specialty and large calculation. In order to explore a new resolution, the author proposed a combining algorithm for structural optimization, which is based on geneticalgorithm

Weijin Jiang I; Dingti Luol; Yusheng Xu; Xingming Sun

This paper investigates the feasibility of applying geneticalgorithms 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 geneticalgorithm 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.

Wells, B. Earl; Weir, John; Trevino, Luis; Patrick, Clint; Steincamp, Jim

This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and GeneticAlgorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and GeneticAlgorithm were programmed using MATLAB«.

Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

This paper presents a multiobjective geneticalgorithm to solve the multicast routing problem without using multicast trees. The mechanism to find routes aims to fulfill two conflicting objectives: maximization of the common links in source-destination routes and minimization of the route sizes. The proposed GA can be characterized by representation of network links in a permutation problem, local viability restrictions

The geneticalgorithm (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

Gautam Roy; Hyunyoung Lee; Jennifer L. Welch; Yuan Zhao; Vijitashwa Pandey; Deborah L. Thurston

The essential parameters determining the behaviour of geneticalgorithms were investigated. Computer runs were made while systematically varying the parameter values. Results based on the progress curves obtained from these runs are presented along with results based on the variability of the population as the run progresses.

A new criterion of fitness evaluation for GeneticAlgorithms is introduced where the fitness value of an individual is determined by considering its own fitness as well as those of its ancestors. Some guidelines for selecting the weighting coefficients for quantifying the importance to be given to the fitness of the individual and its ancestors are provided. This is done

Genetic and evolutionary algorithms are a possible way of searching for the solution to problems in large dimension search spaces. In this work the authors have applied these methods to the generation of musical sequences using melodic and musical theory concepts such as fitness function

A flow shop sequencing model (FSSM) that incorporates actual constraints encountered in practice is proposed for the difficult case of specialized precast production scheduling. The model is solved using a geneticalgorithm (GA). The traditional minimize makespan and the more practical minimize tardiness penalty objective functions are optimized separately, as well as simultaneously using a weighted approach. Experiments are conducted

Single Nucleotide Polymorphisms (SNPs) provide valuable information on human evolutionary history and may lead us to identify genetic variants responsible for human complex diseases. Unfortunately, molecular haplotyping methods are costly, laborious, and time consuming; therefore, algorithms for constructing full haplotype patterns from small available data through computational methods, Tag SNP selection problem, are convenient and attractive. This problem is proved

Ghasem Mahdevar; Javad Zahiri; Mehdi Sadeghi; Abbas Nowzari-Dalini; Hayedeh Ahrabian

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 geneticalgorithms to make the fuzzy logic control systems behave as closely as possible to the operator or expert behavior

Francisco Herrera; Manuel Lozano; José L. Verdegay

This paper proposes a novel hybrid geneticalgorithm 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

Evolutionary chemistry combines the evaluation of molecular properties and synthesis of novel compounds in a feedback loop to arrive at molecules with the desired properties. Inspired by natural evolutionary processes, combinatorial chemistry in combination with mathematical optimization methods and biological testing provides new approaches to drug discovery. Geneticalgorithms have been applied with success in the design and automated synthesis

In this paper geneticalgorithms are applied as an optimization strategy in a controller order reduction procedure. GAs capability to get out local minima is used to determine a reduced-order controller which optimally approximate the full-order one. Particular attention has been devoted to guarantee a good stability margin and a suitable index is minimized, by using GAs, in order to

Riccardo Caponetto; Luigi Fortuna; Giovanni Muscato; Maria Gabriella Xibilia

Geneticalgorithms (GAs) are multi-dimensional and stochastic search methods, involving complex in- teractions among their parameters. For last two decades, researchers have been trying to understand the mechanics of GA parameter interactions by using various techniques. The methods include careful 'func- tional' decomposition of parameter interactions, empirical studies, and Markov chain analysis. Although the complex knot of these interactions are

Baseball has been widely studied in various ways, including math and statistics. In a baseball game, an optimized batting order helps the team achieves greater number of runs in a season. This paper introduces a method that combines a geneticalgorithm with a statistical simulation to identify a non-optimal batting order. The biggest issue is how we evaluate a batting

Purpose - On the metaphoric level, much as been written about complex adaptive systems (CAS) for implementing total quality management (TQM) and organizational learning (OL) in turbulent or unpredictable environments. The aim of this paper is to add practical insights on how a specific CAS-technique called geneticalgorithms (GA) can be used for designing quality management systems for keeping the

Geneticalgorithms (GAs) are a class of adaptive search techniques which have gained popularity in optimisation. In particular they have successfully been applied to NP hard problems such as those resulted in mathematical modelling of facilities design problems. The typical steps required to implement GAs are: encoding of feasible solutions into chromosomes using a representation method, evaluation of fitness function,

This paper is concerned with the application of the technique of geneticalgorithms to solve the problem of optimal facilities' layout in manufacturing systems design. A mathematical model is developed to examine the machines' layout and the pattern of material flow for the typical job shop and flow shop manufacturing environments. The analysis also considers various practical aspects, such as

Many practical pattern classification applications require a careful selection of attributes or features (froma much larger set) to represent the patterns to be classified. This feature subset selection problem is a multicriterionoptimization problem. We propose a solution to this problem using a geneticalgorithm. Our experimentsdemonstrate the feasibility of this approach for feature subset selection in the automated design of

Practical pattern classification and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classified. This paper presents an approach to the multi-criteria optimization problem of feature subset selection using a geneticalgorithm. Our experiments demonstrate the feasibility of this approach for feature subset selection in the

Practical pattern-classification and knowledge-discovery problems require the selection of a subset of attributes or features to represent the patterns to be classified. The authors' approach uses a geneticalgorithm to select such subsets, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features

A modified geneticalgorithm is used to solve the parameter identification problem for linear and nonlinear IIR digital filters. Under suitable hypotheses, the estimation error is shown to converge in probability to zero. The scheme is also applied to feedforward and recurrent neural networks

The population size of geneticalgorithms (GAs) affects the quality of the solutions and the time required to find them. While progress has been made in estimating the population sizes required to reach a desired solution quality for certain problems, in ...

F. Fernandez de Vega E. Cantu-Paz J. I. Lopez T. Manzano

The main goal of this paper is to describe the modeling, implementation and evaluation of the GeneticAlgorithm's (GA) efficiency when applied to robotic group formation and coordination. The robotic task in this paper is performed over a natural disaster, simulated as a forest fire. The robot squad mission is to surround the fire and avoid fire's propagation. Experiments have

Gustavo Pessin; F. Osorio; Denis F. Wolf; M. A. Dias

A geneticalgorithm approach is employed to obtain optimal placement of wind turbines for maximum production capacity while limiting the number of turbines installed and the acreage of land occupied by each wind farm. Specifically, three cases are considered—(a) unidirectional uniform wind, (b) uniform wind with variable direction, and (c) non-uniform wind with variable direction. In Case (a), 600 individuals

\\u000a This paper introduces a new robust optimization technique which performs tolerance and parameter design using a geneticalgorithm.\\u000a It is demonstrated how tolerances for control parameters can be specified while reducing the product’s sensitivity to noise\\u000a factors. As generations of solutions undergo standard genetic operations, new designs evolve, which exhibit several important\\u000a characteristics. First, all control parameters in an evolved

This thesis investigates the use of problem-specific knowledge to enhance a geneticalgorithm 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.Geneticalgorithms 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 geneticalgorithm 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 geneticalgorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard geneticalgorithms 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.

Geneticalgorithms, there are many difficult issues, such as premature convergence, choice of control parameters. This combination of all improvements, the optimal preservation strategy, adaptive set the crossover probability and mutation probability, the idea of fitness scaling into the simple geneticalgorithm, the algorithm is improved and used Matlab program to achieve the improved algorithm, prove the correctness and practicability of this method.

A transmission loss minimum re-scheduling method by geneticalgorithm and modified shuffled frog leaping algorithm are proposed. The problem is a complex mixed integer programming problem. A geneticalgorithm (GA) is a search or optimization algorithm based on the mechanics of natural selection and natural genetics where modified shuffled frog-leaping algorithm (MSFLA), which is an improved version of memetic algorithm,

Linear image restoration techniques induce erroneous detail around sharp intensity changes. Thus, considerable work has centered on nonlinear methods, which incorporate constraints to reduce the artifacts generated in the restoration. In our paper, we examine the applicability of geneticalgorithms to solving optimization problems posed by nonlinear image recovery techniques, particularly by maximum entropy restoration. Each point in the solution space is a feasible image, with the pixels as decision variables. Search is multiobjective: the entropy of the estimate must be maximized, subject to constraints dependent on the observed data and image degradation model. We use Pareto techniques to achieve this combined requirement, and problem-oriented knowledge to direct the search. Typical issues for geneticalgorithms are addressed: chromosomal representation, genetic operators, selection scheme, and initialization.

IntroductionRapid expansion of the Internet increases demandon reliable and efficient routing algorithms. At thebeginning of this year, there were 43,230,000 hostsconnected to the Internet (Source: Network Wizards(

The authors have been using geneticalgorithms to study the structures of atomic clusters and related problems. This is a problem where local minima are easy to locate, but barriers between the many minima are large, and the number of minima prohibit a systematic search. They use a novel mating algorithm that preserves some of the geometrical relationship between atoms, in order to ensure that the resultant structures are likely to inherit the best features of the parent clusters. Using this approach, they have been able to find lower energy structures than had been previously obtained. Most recently, they have been able to turn around the building block idea, using optimized structures from the GA to learn about systematic structural trends. They believe that an effective GA can help provide such heuristic information, and (conversely) that such information can be introduced back into the algorithm to assist in the search process.

Morris, J.R.; Deaven, D.M.; Ho, K.M.; Wang, C.Z.; Pan, B.C.; Wacker, J.G.; Turner, D.E. [Ames Lab., IA (United States)]|[Iowa State Univ., Ames, IA (United States). Dept. of Physics

This paper describes the application of a multi-population geneticalgorithm to the selection of feature subsets for classification\\u000a problems. The multi-population geneticalgorithm based on the independent evolution of different subpopulations is to prevent\\u000a premature convergence of each subpopulation by migration. Experimental results with UCI standard data sets show that multi-population\\u000a geneticalgorithm outperforms simple geneticalgorithm.

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which geneticalgorithms (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.

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. Geneticalgorithms 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 geneticalgorithms 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.

We explore several different techniques in our quest to improve the overall model performance of a geneticalgorithm calibrated probabilistic cellular automata. We use the Kappa statistic to measure correlation between ground truth data and data predicted by the model. Within the geneticalgorithm, we introduce a new evaluation function sensitive to spatial correctness and we explore the idea of evolving different rule parameters for different subregions of the land. We reduce the time required to run a simulation from 6 hours to 10 minutes by parallelizing the code and employing a 10-node cluster. Our empirical results suggest that using the spatially sensitive evaluation function does indeed improve the performance of the model and our preliminary results also show that evolving different rule parameters for different regions tends to improve overall model performance. Copyright 2005 ACM.

Talaie, S.; Leigh, R.; Louis, S. J.; Raines, G. L.

In this paper we present a heuristic based upon geneticalgorithms for the multidimensional knapsack problem. A heuristic operator which utilises problem-specific knowledge is incorporated into the standard geneticalgorithm approach. Computational results show that the geneticalgorithm heuristic is capable of obtaining high-quality solutions for problems of various characteristics, whilst requiring only a modest amount of computational effort. Computational

A hybrid geneticalgorithm is described for performing the difficult optimization task of resolving closely spaced objects appearing in space based and ground based surveillance data. This application of geneticalgorithms is unusual in that it uses a powerful domain-specific operation as a genetic operator. Results of applying the algorithm to real data from telescopic observations of a star field are presented.

An improved geneticalgorithm based on analyzing the geneticalgorithm performance bottlenecks is proposed. It applies Objective Adaptive Parallel GeneticAlgorithm to solve the Markowitz model which is multi-objective limited investment restrictions. In this process, it discusses operator parameter design and studies dynamic adjustment group size and group diversity on the impact of the crossover and mutation probability technology. Matlab

The use of a geneticalgorithm for the minimum thickness design of composite laminated plates is explored. A previously developed geneticalgorithm for laminate design is thoroughly revised and improved, by incorporating knowledge of the physics of the problem into the geneticalgorithm. Constraints are accounted for by combining fixed and progressive penalty functions. Improved selection, mutation, and permutation operators

This paper describes the use of geneticalgorithms (GAs) for the optimal design of phononic bandgaps in periodic elastic two-phase media. In particular, we link a GA with a computational finite element method for solving the acoustic wave equation, and find optimal designs for both metal–matrix composite systems consisting of Ti\\/SiC, and H2O-filled porous ceramic media, by maximizing the relative

George A. Gazonas; Daniel S. Weile; Raymond Wildman; Anuraag Mohan

The geneticalgorithm (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

This paper considers the use of geneticalgorithms (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

\\u000a To track passive target efficiently and accurately, an improved particle filter algorithm based on geneticalgorithm (SGAPF)is\\u000a proposed.By incorporating the newest observation into sampling process and using geneticalgorithm, the degeneracy problem\\u000a is overcome and the predication performance of particle filter is improved. The improved algorithm guarantees the diversity\\u000a of the particles and particles are moved to the regions where

In this paper, a new scheduling algorithm has been introduced based on dynamic geneticalgorithm, which is more efficient in comparison with the previous similar algorithms and has more reliability. SQEFG is a new proposed algorithm, with new parameters and metrics defined, delays and response time to jobs have been decreased and the user satisfaction level has been increased. Analysis

Mohsen Nejadkheirallah; Reza Sookhtsaraei; Kobra Darvish; Mehdi Shahnazi

In this paper, we propose an algorithm for reference bands selection in hyperspectral image (HSI) compression. In HSI compression, many algorithms need to select the reference bands, but all those algorithms uniformly select the reference bands. The uniform selection method isn't optimal, so we propose a geneticalgorithm (GA) based reference band selection method to get lower prediction residual. The

The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with geneticalgorithm 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 geneticalgorithm. Preliminary results show that the performance of the overall system is shown to improve with geneticalgorithm tuning.

A type of genetic simulated annealing algorithms (GSAAs) is presented, which is used to optimize the parameters of proportional-integral-derivative (PID) controllers. This approach combines the merits of geneticalgorithms (GAs) and simulated annealing algorithms (SAAs). By integrating the global search ability of GA with the local search ability of SAA, the search ability of GSAA is much stronger than GA's

A genetics-based algorithm is proposed to solve an economic dispatch problem for valve point discontinuities. The algorithm utilizes payoff information of candidate solutions to evaluate their optimality. Thus, the constraints of classical LaGrangian techniques on unit curves are circumvented. The formulations of an economic dispatch computer program using geneticalgorithms are presented and the program's performances using two different encoding

The population size of geneticalgorithms (GAs) affects the quality of the solutions and the time required to find them. While progress has been made in estimating the population sizes required to reach a desired solution quality for certain problems, in practice the sizing of populations is still usually performed by trial and error. These trials might lead to find a population that is large enough to reach a satisfactory solution, but there may still be opportunities to optimize the computational cost by reducing the size of the population. This paper presents a technique called plague that periodically removes a number of individuals from the population as the GA executes. Recently, the usefulness of the plague has been demonstrated for genetic programming. The objective of this paper is to extend the study of plagues to geneticalgorithms. We experiment with deceptive trap functions, a tunable difficult problem for GAs, and the experiments show that plagues can save computational time while maintaining solution quality and reliability.

de Vega, F F; Cantu-Paz, E; Lopez, J I; Manzano, T

Geneticalgorithms (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.

We present a new procedure for localizing simultaneously active multiple brain sources that overlap in both space and time on EEG recordings. The source localization technique was based on a spatio-temporal model and a geneticalgorithm search routine. The method was successfully applied to the localization of two dipole sources from several sets of simulated potentials with various signal-to-noise ratios (SNR). The different SNR values resembled evoked responses and epileptic spikes as commonly seen in the laboratory. Results of the simulation studies yielded localization accuracy ranging from 0.01 to 0.07 cm with an SNR of 10; from 0.02 to 0.26 cm with an SNR of 5; and from 0.06 to 0.73 cm when the SNR was equal to 2. Additionally, two sets of simulations were based on the dipole arrangements and time activities of data obtained during electrical stimulation of the median nerve in human subjects. These studies yielded localization accuracy within 0.1 cm. We also studied the localization accuracy of the algorithm using a physical model incorporating potential measurements of two current dipoles embedded in a sphere. In this situation the algorithm was successful in localizing the two simultaneously active sources to within 0.07-0.15 cm. PMID:8699877

McNay, D; Michielssen, E; Rogers, R L; Taylor, S A; Akhtari, M; Sutherling, W W

Geneticalgorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of geneticalgorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that geneticalgorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.

In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid geneticalgorithm - GeneticAlgorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.

Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui

The geneticalgorithm (GA) is a machine-based optimization routine which connects evolutionary learning to natural genetic laws. The present work addresses the problem of obtaining the dominant takeover regimes in the GA dynamics. Estimated GA run times are computed for slow and fast convergence in the limits of high and low fitness ratios. Using Euler's device for obtaining partial sums in closed forms, the result relaxes the previously held requirements for long time limits. Analytical solution reveal that appropriately accelerated regimes can mark the ascendancy of the most fit solution. In virtually all cases, the weak (logarithmic) dependence of convergence time on problem size demonstrates the potential for the GA to solve large N-P complete problems.

The cell represents the basic unit of life. It can be interpreted as a chemical machine which can solve special problems. The present knowledge we have of molecular biology allows the characterization of the metabolism as a processing method. This method is an evolutionary product which has been developed over millions of years. First we will present the analyzed features of the metabolism. Then we will go on to compare this processing method with methods which are discussed in computer science. The comparison shows that there is no method in the field of computer science which uses all the metabolic features. This is the reason why we formalize the metabolic processing method. In this paper we choose to use a grammatical formalism. A genetic grammar is the basis of the metabolic system which represents the metabolic processing method. The basic unit of this system (logic unit) will be shown. This allows the discussion of the complexity of realizing the metabolic system in hardware.

This article describes the design of highly complex physical instruments by using a canonical geneticalgorithm (GA). The procedure can be applied to all instrument designs where performance goals can be quantified. It is particularly suited to the optimization of instrument design where local optima in the performance figure of merit are prevalent. Here, a GA is used to evolve the design of the neutron spin-echo spectrometer WASP which is presently being constructed at the Institut Laue-Langevin, Grenoble, France. A comparison is made between this artificial intelligence approach and the traditional manual design methods. We demonstrate that the search of parameter space is more efficient when applying the geneticalgorithm, and the GA produces a significantly better instrument design. Furthermore, it is found that the GA increases flexibility, by facilitating the reoptimization of the design after changes in boundary conditions during the design phase. The GA also allows the exploration of 'nonstandard' magnet coil geometries. We conclude that this technique constitutes a powerful complementary tool for the design and optimization of complex scientific apparatus, without replacing the careful thought processes employed in traditional design methods.

Hoelzel, Robert; Bentley, Phillip M.; Fouquet, Peter [Institut Laue-Langevin, BP 156, F-38042 Grenoble Cedex 9 (France); Hahn-Meitner Institut, Glienicker Strasse 100, D-14109 Berlin (Germany); Institut Laue-Langevin, BP 156, F-38042, Grenoble Cedex 9 (France)

Radio astronomy interferometric arrays traditionally use Earth rotation aperture image synthesis. Existing radio telescopes consist of dozens antennas separated at hundreds and thousands wavelengths, and these arrays are very sparse comparing to the common radar & communications phased arrays. New projects of superlarge radio telescopes, Square Kilometer Array (SKA), Low Frequency Array (LOFAR), Atacama Large Millimeter Array (ALMA) presume both Earth rotation and snapshot imaging. Optimizing an array configuration is an important stage of the array design. Due to the sparseness of the radio interferometers, the following cost functions might be chosen during optimization process: sidelobe minimization, or, in more specific way, the maximum sidelobe amplitude or the baseline histogram. Geneticalgorithm is proposed in this paper for solving the optimization problem. It provides the global maximum of a cost function in a multimodal task and admits easy implementation of different constrains: desirable angular resolution (maximal antenna spacing), sensitivity to extended image features (minimal spacing), topography limitations, etc. Several examples of array configuration optimization using geneticalgorithms are given in the paper.

Geneticalgorithms (GA) have several important features that predestine them to solve design problems. Their main disadvantage however is the excessively long run-time that is needed to deliver satisfactory results for large instances of complex design problems. The main aims of this paper are (1) to demonstrate that the effective and efficient application of the GA concept to design problem

We present an abstraction of the geneticalgorithm (GA), termed population-based incremental learning (PBIL), that explicitly maintains the statistics contained in a GA's population, but which abstracts away the crossover operator and redefines the role of the population. This results in PBIL being simpler, both computationally and theoreti- cally, than the GA. Empirical results reported elsewhere show that PBIL is

Combining the advantages of both the geneticalgorithm (GA) and the chase decoding algorithm, a novel improved decoding algorithm of the block turbo code (BTC) with lower computation complexity and more rapid decoding speed is proposed in order to meet the developing demands of optical communication systems. Compared with the traditional chase decoding algorithm, the computation complexity can be reduced and the decoding speed can be accelerated by applying the novel algorithm. The simulation results show that the net coding gain (NCG) of the novel BTC decoding algorithm is 1.1 dB more than that of the traditional chase decoding algorithm at the bit error rate (BER) of 10-6. Therefore, the novel decoding algorithm has better decoding correction-error performance and is suitable for the BTC in optical communication systems.

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 geneticalgorithm 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.

This paper presents the application of geneticalgorithms in the optimization of an offset reflector antenna. The antenna shape is designed in order to obtain a uniform radiation pattern on the Brazilian territory. Modified genetic operators are proposed with the aim to increase the efficiency of the real coded geneticalgorithms used here.

Geneticalgorithms are on the rise in electromagnetics as design tools and problem solvers because of their versatility and ability to optimize in complex multimodal search spaces. This paper describes the basic geneticalgorithm and recounts its history in the electromagnetics literature. Also, the application of advanced genetic operators to the field of electromagnetics is described, and design results are

The training of product neural networks using geneticalgorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and geneticalgorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a geneticalgorithm, and one method of overcoming this is presented.

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, geneticalgorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by geneticalgorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Geneticalgorithms 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.

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, geneticalgorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by geneticalgorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Geneticalgorithms 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.

. This paper presents a new genetic local search algorithm forthe graph coloring problem. The algorithm combines an original crossoverbased on the notion of union of independent sets and a powerful localsearch operator (tabu search). This new hybrid algorithm allows us toimprove on the best known results of some large instances of the famousDimacs benchmarks.1 IntroductionThe graph coloring problem is

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…

A PID-like Neural Net control algorithm is developed, and GeneticAlgorithm is integrated into for optimizing the learning rates. The dynamic properties for an intermittent Heating Ventilation Air Conditioning (HVAC) system are analyzed, and MATLAB\\/Simulink model for Neural Net control algorithm is introduced. The simulation for large delay systems and the industrial intermittent HVAC system application is reported.

One objective of this project was to develop a global-local algorithm for wing structure design based on parallel geneticalgorithms for the lower (local) level and homotopy algorithms for the upper (global) level. A second goal is to develop a similar pr...

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 GeneticAlgorithm 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.

Sorting by reversals is an important problem in inferring the evolutionary relationship between two genomes. The problem of sorting unsigned permutation has been proven to be NP-hard. The best guaranteed error bounded is the 3\\/2- approximation algorithm. However, the problem of sorting signed permutation can be solved easily. Fast algorithms have been developed both for finding the sorting sequence and

The standard versions of Evolutionary Algorithms (EAs) have two main drawbacks: unlearned termination criteria and slow convergence. Although several attempts have been made to modify the original versions of Evolutionary Algorithms (EAs), only very few of them have considered the issue of their termination criteria. In general, EAs are not learned with automatic termination criteria, and they cannot decide when

Abdel-Rahman Hedar; Bun Theang Ong; Masao Fukushima

We utilize geneticalgorithms aided by simulated annealing to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse intervals and perform the optimization with respect to pulse type and order. In this manner, we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite-pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure that underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.

We model an artificial chemistry comprised of interacting polymers by specifying two initial conditions: a distribution of polymers and a fixed set of reversible catalytic reactions. A geneticalgorithm is used to find a set of reactions that exhibit a desired dynamical behavior. Such a technique is useful because it allows an investigator to determine whether a specific pattern of dynamics can be produced, and if it can, the reaction network found can be then analyzed. We present our results in the context of studying simplified chemical dynamics in theorized protocells - hypothesized precursors of the first living organisms. Our results show that given a small sample of plausible protocell reaction dynamics, catalytic reaction sets can be found. We present cases where this is not possible and also analyze the evolved reaction sets.

Geneticalgorithms, which simulate evolution in natural systems, have been used to find solutions to optimization problems that seem intractable to standard approaches. In this study, the feasibility of using a GA to find an optimum, fixed profile nozzle for a pulse detonation engine (PDE) is demonstrated. The objective was to maximize impulse during the detonation wave passage and blow-down phases of operation. Impulse of each profile variant was obtained by using the CFD code Mozart/2.0 to simulate the transient flow. After 7 generations, the method has identified a nozzle profile that certainly is a candidate for optimum solution. The constraints on the generality of this possible solution remain to be clarified.

Billings, Dana; Turner, James E. (Technical Monitor)

A multimodal problem generator was used to test three versions of a geneticalgorithm and the binary particle swarm algorithm in a factorial time-series experiment. Specific strengths and weaknesses of the various algorithms were identified

Superquadric parameter extraction is essential for superquadric-based reconstruction from 2D images and 3D data, but most of the search algorithms for superquadric parameter extraction are suboptimal and they are susceptible to being trapped into local optima. In this paper, we propose a search based on a real-coded geneticalgorithm (RCGA) for parameter extraction, which applies the geneticalgorithm to superquadric-based

A method for aerodynamic shape optimization based on a geneticalgorithm 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 geneticalgorithm is easy to implement, flexible in application and extremely reliable.

Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)

A kind of improved geneticalgorithm for identifying transfer function of thermal process in power plant is introduced. In the algorithm, floating-point coding, rank-based selection, elitist reservation and grouping method are used, the premature convergence is restrained, the global and local searching ability is improved. The geneticalgorithm-based model identification MATLAB program is designed, the transfer functions of thermal process

Liu Changliang; Liu Jizhen; Niu Yuguang; Yao Wanye

An improved geneticalgorithm with multiplier updating (IGAMU) to solve practical power economic load dispatch (PELD) problems of different sizes and complexities with non-convex cost curves, where conventional mathematical methods are inapplicable, is developed. The improved geneticalgorithm (IGA) provides an improved evolutionary direction operator and a migrating operator, enabling it to efficiently search and actively explore solutions. Multiplier updating

. We use a geneticalgorithm augmented with a long termmemory to design control strategies for a simulated robot, a mobile vehicleoperating in a two-dimensional environment. The simulated robothas five touch sensors, two sound sensors, and two motors that drivelocomotive tank tracks. A geneticalgorithm trains the robot in severalspecially-designed simulation environments for evolving basic behaviorssuch as food approach, obstacle

Achieving good performance with a parallel geneticalgorithm requires properly configuring control parameters such as mutation rate, crossover rate, and population size. We consider the problem of setting control parameter values in a standard, island-model distributed geneticalgorithm. As an alternative to tuning parameters by hand or using a self-adaptive approach, we propose a very simple strategy which statically assigns

A modified geneticalgorithm has been developed for the task of optimal parameter selection for compartmental models. As a case study, a predictive model of the emerging health threat of obesity in America was developed which incorporated varying levels of three treatment strategies in an attempt to decrease the amount of overweight Americans over a ten-year period. The geneticalgorithm

In this paper we describe an efficient approach for multimodal function optimization using geneticalgorithms (GAs). We recommend the use of adaptive probabilities of crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the, convergence capacity of the GA. In the adaptive geneticalgorithm (AGA), the probabilities of crossover and mutation, pc and

The grouping geneticalgorithm (GGA) is a geneticalgorithm heavily modified to suit the structure of grouping problems. Those are the problems where the aim is to find a good partition of a set or to group together the members of the set. The bin packing problem (BPP) is a well known NP-hard grouping problem: items of various sizes have

In this paper we develop a study on several types of parallel geneticalgorithms (PGAs). Our mo- tivation is to bring some uniformity to the proposal, comparison, and knowledge exchange among the traditionally opposite kinds of serial and parallel GAs. We comparatively analyze the properties of steady-state, generational, and cellular geneticalgorithms. Afterwards, this study is extended to consider a

The heart of any tracking system is its data association algorithm where measurements, received as sensor returns, are assigned to a track, or rejected as clutter. In this paper, we investigate the use of geneticalgorithms (GA) for the multiple target tracking data association problem. GA are search methods based on the mechanics of natural selection and genetics. They have

Jean-Yves Carrier; John Litva; Henry Leung; Titus K. Lo

The accuracy of segmenting Chinese character, especially connected Chinese characters, is essential for the performance of a Chinese character recognition system. In this paper, a new approach for segmenting connected Chinese characters based on geneticalgorithm is proposed. The best segmentation path is evolved by geneticalgorithm from a fixed area located in the middle of character image which is

By analyzing the deficiency of traditional geneticalgorithm in solving the Traveling Salesman Problem, an improved geneticalgorithm is proposed for TSP. In this paper, the ordinal real-number encoder is used for chromosome encoding and ordered crossover operators is advanced that utilizes local and global information to construct offspring. In order to guarantee global convergence, heuristic knowledge and self-learning is

A dynamic Bayesian network (DBN) is a probabilistic network that models inter- dependent entities that change over time. Given example sequences of multivariate data, we use a geneticalgorithm to synthesize a network structure that models the causal relationships that explain the sequence. We use a multi-objective evalua- tion strategy with a geneticalgorithm. The multi-objective criteria are a network's

A new method, using geneticalgorithms, for constructing a tri-state neural network is presented. The global searching features of the geneticalgorithms are adopted to help us easily find the interconnection weight matrix of a bipolar neural network. The construction method is based on the biological nervous systems, which evolve the parameters encoded in genes. Taking the advantages of conventional

The problem of estimating regions of asymptotic stability for nonlinear dynamic systems is considered as an optimization problem. Geneticalgorithms are then proposed to solve the resulting optimization problems. Three test systems are used to evaluate the performance of the proposed geneticalgorithms. The test systems are 6th, 8th, and 17th order nonlinear power electronics systems. The performance of the

Benjamin P. Loop; Scott D. Sudhoff; S. H. Zak; Edwin L. Zivi

Purpose – To propose and to evaluate a new geneticalgorithm (GA) for solving the dynamic pickup and delivery problem with time windows (DPDPTW). Design\\/methodology\\/approach – First, a grouping geneticalgorithm (GGA) for the (static) PDPTW is described. In order to solve the dynamic problem, the GGA then is embedded in a rolling horizon framework. Special updating mechanisms are provided

Using an approach similar to the biological processes of natural selection and evolution, the geneticalgorithm (GA) is a nonconventional optimum search technique. Geneticalgorithms have the ability to search large and complex decision spaces and handle nonconvexities. In this paper, the GA is applied for solving the optimum classification of rainy and non-rainy day occurrences based on vertical velocity,

This paper describes the application of a geneticalgorithm to the stacking sequence optimization of a laminated composite plate for buckling load maximization. Two approaches for reducing the number of analyses required by the geneticalgorithm are described. First, a binary tree is used to store designs, affording an efficient way to retrieve them and thereby avoid repeated analyses of

Discussion of information retrieval, query optimization techniques, and relevance feedback focuses on geneticalgorithms, which are derived from artificial intelligence techniques. Describes an evaluation of different geneticalgorithms using a residual collection method and compares results with the Ide dec-hi method (Salton and Buckley, 1990…

Lopez-Pujalte, Cristina; Guerrero Bote, Vicente P.; Moya Anegon, Felix de

One of the most important practical considerations in the optimization of discrete structures is that the structural members are generally to be selected from available profiles list. Geneticalgorithm shows certain advantages over other classical optimization procedures in structural optimization of discrete variables. In this paper we introduce the idea of directed mutation into the simple geneticalgorithms field and

Robust object tracking is quite important in computer vision. In this paper, a novel tracking approach for single object which combines geneticalgorithm and Kalman filter is proposed. Geneticalgorithm is introduced and reasonably applied to find the tracked object in a search area. A further step called multi-blocks voting is exploited for obtaining more accurate object localization. Kalman filter

A new search strategy for geneticalgorithms is introducedwhich allows iterative searches with completereinitialization of the population preservingthe progress already made toward solving an optimizationtask. Delta coding is a simple searchstrategy based on the idea that the encoding usedby a geneticalgorithm can express a distance awayfrom some previous partial solution. Delta valuesare added to a partial solution before evaluatingthe

L. Darrell Whitley; Keith E. Mathias; Patrick A. Fitzhorn

A new algorithm for extracting valuable information from industrial data is presented in this paper. The proposed methodology produces dynamic Radial Basis Function (RBF) neural network models and uses GeneticAlgorithms (GAs) to auto-configure the structure of the networks. The effectiveness of the method is illustrated through the development of a dynamical model for a chemical reactor, used in pulp

Haralambos Sarimveis; Alex Alexandridis; Stefanos Mazarakis; George Bafas

Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. This paper presents data clustering using improved geneticalgorithm (IGA) and the popular Nelder-Mead(NM) Simplex search . To improve the accuracy of data clustering, an improved GA

Suresh Chandra Satapathy; J. V. R. Murthy; P. V. G. D. Prasada Reddy

Sorting by reversals is an important problem in inferring the evolutionary relationship between two genomes. The problem of sorting unsigned permutation has been proven to be NP-hard. The best guaranteed error bounded is the 3\\/2-approximation algorithm. However, the problem of sorting signed permutation can be solved easily. Fast algorithms have been developed both for finding the sorting sequence and finding

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 geneticalgorithms (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.

SanScoucie, M. P.; Hull, P. V.; Tinker, M. L.; Dozier, G. V.

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 geneticalgorithms. 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.

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 geneticalgorithm. 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.

We present a self-contained theoretical framework for a scaled geneticalgorithm over the alphabet {0,1} which converges asymptotically\\u000a to global optima as anticipated by Davis and Principe in analogy to the simulated annealing algorithm. The algorithm employs\\u000a multiple-bit mutation, single-cut-point crossover and power-law scaled proportional fitness selection based upon an arbitrary\\u000a fitness function. In order to achieve asymptotic convergence to

The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of geneticalgorithms. 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 geneticalgorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the geneticalgorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, geneticalgorithms are potentially an effective and structured approach for learning fuzzy membership functions.

Inverse synthetic aperture radar (ISAR) has a capability of obtaining fine resolution images of moving targets. Target rotation relative to the radar is the source for obtaining cross-range resolution, while the unwanted translational motion causes image blurring, which needs to be removed before imaging. Translation compensation is usually accomplished in two steps: range alignment and phase compensation. Many different algorithms

A semi-supervised clustering algorithm is proposed that combines thebenefits of supervised and unsupervised learning methods. Data are segmented\\/clustered using an unsupervised learning technique that is biasedtoward producing segments or clusters as pure as possible in terms ofclass distribution. These clusters can then be used to predict the classof future points. For example in database marketing, the technique canbe used to

A semi-supervised clustering algorithm is proposed that combines the benefits of supervised and unsupervised learning methods. Data are seg- mented\\/clustered using an unsupervised learning technique that is biased toward producing segments or clusters as pure as possible in terms of class distribution. These clusters can then be used to predict the class of future points. For example in database marketing,

Ayhan Demiriz; Kristin P. Bennett; Mark J. Embrechts

A geneticalgorithm 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 geneticalgorithm 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 geneticalgorithm 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.

A geneticalgorithm 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 geneticalgorithm 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 geneticalgorithm 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.

A geneticalgorithm controlled multispot transmitter is proposed as an alternative approach to optimizing the power distribution for single element receivers in fully diffuse mobile indoor optical wireless communication systems. By specifically tailoring the algorithm, it is shown that by dynamically altering the intensity of individual diffusion spots, a consistent power distribution, with negligible impact on bandwidth and rms delay

Matthew D. Higgins; Roger J. Green; Mark S. Leeson

This paper presents a two-phase geneticalgorithm for economic load dispatching of generators in power systems. The problem of ELD is expressed as a Lagrange function. The conventional GA has a drawback that the algorithm is not so effective as the number of variables increases. To improve the GA characteristic, a two-phase GA is proposed to obtain better solutions. The

In this technical report we present a new method for optimizing the generation of paths in Monte Carlo global illumination rendering algorithms. Ray tracing, particle tracing, and bidirectional ray tracing all use random walks to estimate various fluxes in the scene. The probability density functions neces- sary to generate these random walks are optimized using a geneticalgorithm, such that

A real coded geneticalgorithm (RCGA) for parameter optimization of multiarea automatic generating control (AGC) has been proposed. Instead of using a traditional analysis algorithm to obtain the controller parameters, GA optimization technology is introduced and the MATLAB Simulink model is designed as an AGC parameter optimization tool to deal with the interconnection of the AGC loops. Utilizing GA's parallel

This article is concerned with investigating some aspects of the usefulness of using geneticalgorithms (GAs) for musical composition and to highlight some of their limitations. It also demonstrates the limited amount of work done so far in examining the output of GAs used in creative applications, rather than simply describing the architecture and functionality of such an algorithm, and

Support Vector Machines (SVMs) are originally designed for the solution of two-class problems. In multiclass applications, several strategies divide the original problem into binary subtasks, whose results are combined. In a previous work, GeneticAlgorithms were used to determine the combination of binary SVMs in a multiclass solution. In order to improve the classification performance obtained, this algorithm was extended

Ana Carolina Lorena; André Carlos Ponce Leon Ferreira De Carvalho

This paper studies static and dynamic decentralized versions of the search model known as cellular geneticalgorithm (cGA), in which individuals are located in a specific topology and interact only with their neighbors. Making changes in the shape of such topology or in the neighborhood may give birth to a high number of algorithmic variants. We perform these changes in

We report on progress in the development of general-purpose algorithms for global parameter minimization in scientific applications where comparison of results with data takes the form of a pattern recognition problem. Our basic approach is to implement model calculations for the problem of interest in parallel on a Beowulf cluster, with a geneticalgorithm to optimize the parameters of the

G. Edirisinghe; D. Edirisinghe; O. Messer; M. Guidry

A hybrid optimization algorithm using Differential Evolution (DE) and GeneticAlgorithm (GA) is proposed in this study to address the problem of network parameters determination associated with the Nonlinear Autoregressive with eXogenous inputs Network (NARX-network). The proposed algorithm involves a two level optimization scheme to search for both optimal network architecture and weights. The DE at the upper level is formulated as combinatorial optimization to search for the network architecture while the associated network weights that minimize the prediction error is provided by the GA at the lower level. The performance of the algorithm is evaluated on identification of a laboratory rotary motion system. The system identification results show the effectiveness of the proposed algorithm for nonparametric model development.

Salami, M. J. E.; Tijani, I. B.; Abdullateef, A. I.; Aibinu, M. A.

A hybrid optimization algorithm combining finite state method (FSM) and geneticalgorithm (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.

The use of an optimization technique known as a geneticalgorithm 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 geneticalgorithm are explored in both a theoretical and experimental sense. Recent developments in geneticalgorithm 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 geneticalgorithm provides an attractive alternative to the classical techniques used to solve these problems.

We present geneticalgorithms (GAs) as a decentralised topology control mechanism distributed among active running software agents to achieve a uniform spread of terrestrial unmanned vehicles (UVs) over an unknown geographical area. This problem becomes m...

C. S. Sahin E. Urrea G. Bertoli M. Conner M. U. Uyar

PGAPack is the first widely distributed parallel geneticalgorithm library. Since its release, several thousand copies have been distributed worldwide to interested users. In this paper we discuss the key components of the PGAPack design philosophy and pr...

This research effort develops the necessary interfaces between the radar signal processing components and an optimization routine, such as geneticalgorithms, to develop Electronic Countermeasure (ECM) waveforms under a Hardware-in-the-Loop (HILS) archite...

A new method, using geneticalgorithms, for constructing a tri-state neural network is presented. The global searching features of the geneticalgorithms are adopted to help us easily find the interconnection weight matrix of a bipolar neural network. The construction method is based on the biological nervous systems, which evolve the parameters encoded in genes. Taking the advantages of conventional (binary) geneticalgorithms, a two-level chromosome structure is proposed for training the tri-state neural network. A Matlab program is developed for simulating the network performances. The results show that the proposed geneticalgorithms method not only has the features of accurate of constructing the interconnection weight matrix, but also has better network performance.

Context: Mathematical optimization can be used as a computational tool to obtain the optimal solution to a given problem in a systematic and efficient way. For example, in twice-differentiable functions and problems with no constraints, the optimization consists of finding the points where the gradient of the objective function is zero and using the Hessian matrix to classify the type of each point. Sometimes, however it is impossible to compute these derivatives and other type of techniques must be employed such as the steepest descent/ascent method and more sophisticated methods such as those based on the evolutionary algorithms. Aims: We present a simple algorithm based on the idea of geneticalgorithms (GA) for optimization. We refer to this algorithm as AGA (asexual geneticalgorithm) and apply it to two kinds of problems: the maximization of a function where classical methods fail and model fitting in astronomy. For the latter case, we minimize the chi-square function to estimate the parameters in two examples: the orbits of exoplanets by taking a set of radial velocity data, and the spectral energy distribution (SED) observed towards a YSO (Young Stellar Object). Methods: The algorithm AGA may also be called genetic, although it differs from standard geneticalgorithms in two main aspects: a) the initial population is not encoded; and b) the new generations are constructed by asexual reproduction. Results: Applying our algorithm in optimizing some complicated functions, we find the global maxima within a few iterations. For model fitting to the orbits of exoplanets and the SED of a YSO, we estimate the parameters and their associated errors.

\\u000a Hierarchical clustering algorithms have been studied extensively in the last years. However, existing approaches for hierarchical\\u000a clustering suffer from several drawbacks. The representation of the results is often hard to interpret even for large datasets.\\u000a Many approaches are not robust to noise objects or overcome these limitation only by difficult parameter settings. As many\\u000a approaches heavily depend on their initialization,

Christian Böhm; Annahita Oswald; Christian Richter; Bianca Wackersreuther; Peter Wackersreuther

This paper suggests a new framework of multidimensional geneticalgorithm and applies it to the real-world problem of very large scale integration (VLSI) partitioning. The framework consists of a new multidimensional genetic operator, called geographic crossover, and a new genetic encoding scheme. Geographic crossover enables more powerful creation of new solutions by allowing a diverse mixture of parent solutions. Its

In this paper we present the outcome of two recent sets of experiments to evaluate the effectiveness of a new adjunct genetic operator GeneRepair. This operator was developed to correct invlaid tours which may be generated following crossover or mutation of our particular implementation of the geneticalgorithm. Following implementation and testing of our genetic algotihm with GeneRepair we found

George G. Mitchell; Diarmuid O'Donoghue; David Barnes; Mark McCarville

This paper discusses the trade-off between accuracy, reliability and computing time in global optimization. Particular compromises provided by traditional methods (Quasi-Newton and Nelder-Mead's simplex methods) and geneticalgorithms are addressed and illustrated by a particular application in the field of nonlinear system identification. Subsequently, new hybrid methods are designed, combining principles from geneticalgorithms and “hill-climbing” methods in order to

Geneticalgorithms (GA) have been widely applied to solve water resources system optimization. With the increase of the complexity\\u000a and the larger problem scale of water resources system, GAs are most frequently faced with the problems of premature convergence,\\u000a slow iterations to reach the global optimal solution and getting stuck at a local optimum. A novel chaos geneticalgorithm\\u000a (CGA)

Chun-Tian Cheng; Wen-Chuan Wang; Dong-Mei Xu; K. W. Chau

The combination of multiobjective geneticalgorithms with wastewater treatment plant (WWTP) models provides an efficient framework\\u000a for the evaluation, optimisation and comparison of WWTP control laws. This chapter presents a methodology developed for this\\u000a efficient combination. Existing models and simulation software are used. They are combined with NSGA-II, a multiobjective\\u000a geneticalgorithm capable of finding the best tradeoffs (Pareto front)

This paper presents a new method to label parts of human body automatically based on the joint probability density function\\u000a (PDF). To adapt to different motion for different articulation, the probabilistic models of each triangle different number\\u000a of mixture components with MML are adopted. To solve the computation load problem of geneticalgorithm (GA), a constraint-based\\u000a geneticalgorithm (CBGA) is

Fu Yuan Hu; Hau-san Wong; Zhi Qiang Liu; Hui-yang Qu

This paper discusses the application of a geneticalgorithm to control system design for boiler-turbine plant. In particular we study the ability of the geneticalgorithm to develop a proportional-integral (PI) controller and a state feedback controller for a nonlinear multi-input\\/multi-output (MIMO) plant model. The plant model is presented along with a discussion of the inherent difficulties in such controller

In this paper, an optimal design is performed for powder die-pressing process based on the geneticalgorithm approach. It includes the shape optimization of powder component, the optimal design of punch movements, and the friction optimization of powder–tool interface. The geneticalgorithm is employed to perform an optimal design based on a fixed-length vector of design variables. The technique is

To investigate factors limiting the performance of a GaAs solar cell, geneticalgorithm is employed to fit the experimentally measured internal quantum efficiency (IQE) in the full spectra range. The device parameters such as diffusion lengths and surface recombination velocities are extracted. Electron beam induced current (EBIC) is performed in the base region of the cell with obtained diffusion length agreeing with the fit result. The advantage of geneticalgorithm is illustrated. (author)

Xiong, Kanglin; Yang, Hui [Institute of Semiconductors, CAS, No. A35, Qing Hua East Road, Beijing 100083 (China); Suzhou Institute of Nano-tech and Nano-bionics, CAS, Ruoshui Road 398, Suzhou 215125 (China); Lu, Shulong; Zhou, Taofei; Wang, Rongxin; Qiu, Kai; Dong, Jianrong [Suzhou Institute of Nano-tech and Nano-bionics, CAS, Ruoshui Road 398, Suzhou 215125 (China); Jiang, Desheng [Institute of Semiconductors, CAS, No. A35, Qing Hua East Road, Beijing 100083 (China)

In this paper, the optimization of power-delay-product (PDP) of a high-speed flip-flop via transistor sizing is presented. The optimization is performed using the geneticalgorithm (GA). The flip-flop which is used in this optimization is called modified hybrid latch flip-flop (MHLFF). The geneticalgorithm is implemented in MATLAB with the fitness function expressed in terms of the power and the

This paper presents an investigation into the optimal scheduling of real-time tasks of a multiprocessor system using hybrid\\u000a geneticalgorithms (GAs). A comparative study of heuristic approaches such as ‘Earliest Deadline First (EDF)’ and ‘Shortest\\u000a Computation Time First (SCTF)’ and geneticalgorithm is explored and demonstrated. The results of the simulation study using\\u000a MATLAB is presented and discussed. Finally, conclusions

SPLICER computer program used to solve search and optimization problems. Geneticalgorithms adaptive search procedures (i.e., problem-solving methods) based loosely on processes of natural selection and Darwinian "survival of fittest." Algorithms apply genetically inspired operators to populations of potential solutions in iterative fashion, creating new populations while searching for optimal or nearly optimal solution to problem at hand. Written in Think C.

A common practice is to design a controller by plant observations (i.e., experiments) and to optimize some of its parameters by trial-and-error. This paper proposes a geneticalgorithm for the automation of the search procedure. A chemical process is introduced to explain the proposed approach. The process is controlled by a programmable logic controller (PLC). A geneticalgorithm was implemented

Large-scale association studies hold promise for discovering the genetic basis of common human disease. These studies will consist of a large number of individuals, as well as large number of genetic markers, such as single nucleotide polymorphisms (SNPs). The potential size of the data and the resulting model space require the development of efficient methodology to unravel associations between phenotypes and SNPs in dense genetic maps. Our approach uses a geneticalgorithm (GA) to construct logic trees consisting of Boolean expressions involving strings or blocks of SNPs. These blocks or nodes of the logic trees consist of SNPs in high linkage disequilibrium (LD), that is, SNPs that are highly correlated with each other due to evolutionary processes. At each generation of our GA, a population of logic tree models is modified using selection, cross-over and mutation moves. Logic trees are selected for the next generation using a fitness function based on the marginal likelihood in a Bayesian regression frame-work. Mutation and cross-over moves use LD measures to pro pose changes to the trees, and facilitate the movement through the model space. We demonstrate our method and the flexibility of logic tree structure with variable nodal lengths on simulated data from a coalescent model, as well as data from a candidate gene study of quantitative genetic variation. PMID:16220001

Clark, Taane G; De Iorio, Maria; Griffiths, Robert C; Farrall, Martin

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 geneticalgorithm 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 geneticalgorithms. The performance of a reconfigurable hardware implementation of the new algorithm on an FPGA is compared to traditional single processor implementations.

The optimization design of structure with discrete variables is generally a combinatorial optimization problem. Being simple geneticalgorithm has the defects of premature phenomenon, slow convergence speed and poor stability, a hybrid geneticalgorithm is proposed to deal with structure optimization based on relative difference quotient method and improved geneticalgorithm. The advantages of geneticalgorithm in global optimization and

Genetic Programming is very efficient in problem solving compared to other proposals but its performance is very slow when the size of the data increases. This paper proposes a model for multi-threaded Genetic Programming classification evaluation using a NVIDIA CUDA GPUs programming model to parallelize the evaluation phase and reduce computational time. Three different well-known Genetic Programming classification algorithms are evaluated using the parallel evaluation model proposed. Experimental results using UCI Machine Learning data sets compare the performance of the three classification algorithms in single and multithreaded Java, C and CUDA GPU code. Results show that our proposal is much more efficient.

GeneticAlgorithm (GA) is a search method that simulates the process of natural selection and it attempts to find a good solution to some problem by randomly generating a collection of potential solutions to the problem and then manipulating those solutions using genetic operators. Through selection, mutation and re-combination (crossover) operations, better solutions are hopefully generated out of the current

The paper proposes a new optimization technique based on geneticalgorithms for the determination of the cutting parameters in machining operations. In metal cutting processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product. This paper presents a new methodology for continual improvement of cutting conditions with GA (Genetic

The aim of this paper is to propose a means by whichneural network fitness evaluation can be applied toa geneticalgorithm (GA), and an application of thissystem to musical rhythm composition. An AdaptiveResonance Theory (ART) neural network is trainedusing binary information representing classificationpatterns. By comparing new genetically derived individualsto clustered data, a measure of fitness of thenew patterns is determined;

This paper presents a review and experimental results on the major benchmarking functions used for performance control of GeneticAlgorithms (GAs). Parameters considered include the eect of population size, crossover probability and pseudo-random number generators (PNGs). The general computational behavior of two basic GAs models, the Generational Replacement Model (GRM) and the Steady State Replacement Model (SSRM) is evaluated. Genetic

A new method for extracting valuable process information from input–output data is presented in this paper. The proposed methodology produces dynamical radial basis function (RBF) neural network models based on a specially designed geneticalgorithm (GA), which is used to auto-configure the structure of the networks and obtain the model parameters. The new RBF network training technique formulates a complete

Haralambos Sarimveis; Alex Alexandridis; Stefanos Mazarakis; George V. Bafas

The mathematic model of a two-bar truss is built in MATLAB and the analysis is carried out by the geneticalgorithm toolbox. In order to compare with each other, the parametric model of the planar truss is also established by the ANSYS Parametric Design Language and solutions are obtained using the first-order method native to ANSYS. The comparison of the

This paper proposes a new algorithm for topology optimization by combining the features of geneticalgorithms (GAs) and bi-directional\\u000a evolutionary structural optimization (BESO). An efficient treatment of individuals and population for finite element models\\u000a is presented which is different from traditional GAs application in structural design. GAs operators of crossover and mutation\\u000a suitable for topology optimization problems are developed. The

A geneticalgorithm, GENEsYs, is applied to an NP-complele problem, the 0\\/1 multiple knapsack problem. The partitioning of the search space resulting from this highly constrained problem may include substantially large infeasible regions. Our implementation allows for the breeding and participation of infeasible strings in the population. Unlike many other GA-based algorithms lhat are augmented with domainspecific knowledge, GENEsYs uses

Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored only with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self- adaptive feature of real-parameter geneticalgorithms (GAs) using simulated binary crossover (SBX) operator and without any mutation operator. The connection

We develop a new class of geneticalgorithm that computationally determines efficient pulse sequences to implement a quantum gate U in a three-qubit system. The method is shown to be quite general, and the same algorithm can be used to derive efficient sequences for a variety of target matrices. We demonstrate this by implementing the inversion-on-equality gate efficiently when the

A new evolutionary optimal designing method is presented for the designing of steel structures. We combine finite element structural analysis codes with geneticalgorithm to minimize the total weight of the structure subject to external loads and constraints. The proposed algorithm is implemented using the Message Passing Interface(MPI) library over High Performance Computing Cluster-Dawning 4000. A master-slave paradigm is used

The use of geneticalgorithms (GAs) for the design of composite laminates is presented. Unlike the traditional hill-climbing techniques, GAs are global search procedures based on the mechanics of natural selection with the result that they are robust over a wide range of environments, particularly the multimodal search spaces encountered in composite design. The GA requires coding of the design variables as a finite-length string over a finite alphabet. Here, the design variables are the lamina orientations and stacking sequence required for maximum laminate strength and/or stiffness with minimum weight. Numerical results are presented to demonstrate that the geneticalgorithm can be a viable alternative to traditional search procedures in the design of composite laminates. Finally, based on the results of this study several suggestions are mentioned for improvement in the performance of the geneticalgorithm.

In this paper, we describe a flexible system for automatic page layout that makes use of geneticalgorithms 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 geneticalgorithms, 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 geneticalgorithms 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.

Energy consumption and the overhead of rerouting frequency are important concern in the routing algorithm design for Wireless Sensor Networks(WSN) with the Mobile Sink, this paper proposes a data aggregation algorithm based on grid and adaptive geneticalgorithm for WSN with a mobile sink.AGA is applied to find out the optimal route of MA aggregating data, the entire area of

Geneticalgorithms(GA) are very efficient at exploring the entire search space; how- ever, they are relatively poor at finding the precise local optimal solution in the region at which the algorithm converges. Hybrid geneticalgorithms are the combination of improvement proce- dures, usually working as evaluation functions, and geneticalgorithms. There are two basic strat- egies in using hybrid GAs,

In an effort to reduce the dose to operating technicians performing fixed-time procedures on encapsulated source material, a program has been developed to optimize the layout of workstations within a facility by use of a geneticalgorithm. Taking into account the sources present at each station and the time required to complete each procedure, the program utilizes a point kernel dose calculation tool for dose estimates. The geneticalgorithm driver employs the dose calculation code as a cost function to determine the optimal spatial arrangement of workstations to minimize the total worker dose.

McLawhorn, S. L. (Steve L.); Kornreich, D. E. (Drew E.); Dudziak, Donald J.

A geneticalgorithm rainfall intensity (GARI) model has been developed and used to predict the intensities for given return period. It is a one-step solution procedure that may not require any mathematical transformation. The problem formulation is given and the geneticalgorithm solution of the problem is presented. The results show that the proposed GARI model can be used to solve the rainfall intensity-duration-frequency relations with lowest mean-squared error between measured and predicted intensities. Predicted intensities are in good agreement between measured and predicted values for given return periods. Copyright

An optimization method for flush-orifice air data system design has been developed using the GeneticAlgorithm approach. The optimization of the orifice array minimizes the effect of normally distributed random noise in the pressure readings on the calculation of air data parameters, namely, angle of attack, sideslip angle and freestream dynamic pressure. The optimization method is applied to the design of Pressure Distribution/Air Data System experiment (PD/ADS) proposed for inclusion in the Aeroassist Flight Experiment (AFE). Results obtained by the GeneticAlgorithm method are compared to the results obtained by conventional gradient search method.

Deshpande, Samir M.; Kumar, Renjith R.; Seywald, Hans; Siemers, Paul M., III

In this letter, aiming to obtain the best multicast performance of optical network in which the video conference information is carried by specified wavelength, we extend the solutions of matrix games with the network coding theory and devise a new method to solve the complex problems of multicast network switching. In addition, an experimental optical network has been testified with best switching strategies by employing the novel numerical solution designed with an effective way of geneticalgorithm. The result shows that optimal solutions with geneticalgorithm are accordance with the ones with the traditional fictitious play method.

A geneticalgorithm coupled with a simplified acoustic analysis was used to generate low-noise rotor blade designs. The model includes thickness, steady loading and blade-vortex interaction noise estimates. The paper presents solutions for several variations in the fitness function, including thickness noise only, loading noise only, and combinations of the noise types. Preliminary results indicate that the analysis provides reasonable assessments of the noise produced, and that geneticalgorithm successfully searches for 'good' designs. The results show that, for a given required thrust coefficient, proper blade design can noticeably reduce the noise produced at some expense to the power requirements.

When building genetic maps, it is necessary to choose from several marker ordering algorithms and criteria, and the choice is not always simple. In this study, we evaluate the efficiency of algorithms try (TRY), seriation (SER), rapid chain delineation (RCD), recombination counting and ordering (RECORD) and unidirectional growth (UG), as well as the criteria PARF (product of adjacent recombination fractions), SARF (sum of adjacent recombination fractions), SALOD (sum of adjacent LOD scores) and LHMC (likelihood through hidden Markov chains), used with the RIPPLE algorithm for error verification, in the construction of genetic linkage maps. A linkage map of a hypothetical diploid and monoecious plant species was simulated containing one linkage group and 21 markers with fixed distance of 3 cM between them. In all, 700 F(2) populations were randomly simulated with 100 and 400 individuals with different combinations of dominant and co-dominant markers, as well as 10 and 20% of missing data. The simulations showed that, in the presence of co-dominant markers only, any combination of algorithm and criteria may be used, even for a reduced population size. In the case of a smaller proportion of dominant markers, any of the algorithms and criteria (except SALOD) investigated may be used. In the presence of high proportions of dominant markers and smaller samples (around 100), the probability of repulsion linkage increases between them and, in this case, use of the algorithms TRY and SER associated to RIPPLE with criterion LHMC would provide better results. PMID:19639011

Mollinari, M; Margarido, G R A; Vencovsky, R; Garcia, A A F

With the improvement of science and technology, the development of the network, and the exploitation of the HDTV, the demands of audio and video become more and more important. Depending on the video coding technology would be the solution for achieving these requirements. Motion estimation, which removes the redundancy in video frames, plays an important role in the video coding. Therefore, many experts devote themselves to the issues. The existing fast algorithms rely on the assumption that the matching error decreases monotonically as the searched point moves closer to the global optimum. However, geneticalgorithm is not fundamentally limited to this restriction. The character would help the proposed scheme to search the mean square error closer to the algorithm of full search than those fast algorithms. The aim of this paper is to propose a new technique which focuses on combing the hexagon-based search algorithm, which is faster than diamond search, and geneticalgorithm. Experiments are performed to demonstrate the encoding speed and accuracy of hexagon-based search pattern method and proposed method. PMID:24592178

This paper presents calculations of the selection intensity of common selection and replacement methods used in geneticalgorithms (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.

In this experiment we used geneticalgorithms 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.

Geneticalgorithms 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 geneticalgorithm 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

One of the difficulties that has been apparent in applying image processing algorithms not just for automatic target recognition but also for associated tasks in image processing and understanding is that of the optimal choice of parameters and algorithms. Firstly we must select an algorithm to use and secondly the actual parameters that are required by that algorithm. It is

Paul G. Ducksbury; Margaret J. Varga; P. J. Kent; Stephen B. Foulkes; David M. Booth

Traveling Salesman Problem (TSP) is a classical problem of optimization for researchers and its modeling is of great interest for Engineering, Operations Research and Computer Science. For solving TSP, many methods have been proposed, including heuristic ones. Our work extends the hybrid model, based on Particle Swarm Optimization, GeneticAlgorithms and Fast Local Search, for the symmetric blind travelling salesman

3-D shape modeling is very important for efficient shape description and recognition. Superquadrics that is a parametric 3-D shape modeling function can represent various shapes by using a single equation with some parameters. In this study, the superquadrics parameters of 3-D shape are estimated from a 2-D shading image by using a geneticalgorithm (GA), which is an optimizing technique

Evolutionary computational methods as GeneticAlgorithms GAs have proven to be a robust search technique for solving deterministic problems. GA handles its search by giving out a popu- lation of solutions for an optimisation problem based on principles from evolution. This paper de- scribes the use of GA strategies in a stochastic process of a Kanban based manufacturing system, to

In this letter, a geneticalgorithm (GA) optimization technique is applied to determine the switching angles for a cascaded multilevel inverter which eliminates specified higher order harmonics while maintaining the required fundamental voltage. This technique can be applied to multilevel inverters with any number of levels. As an example, in this paper a seven-level inverter is considered, and the optimum

In this paper, a geneticalgorithm (GA) optimization technique is applied to multilevel inverter to determine optimum switching angles for cascaded multilevel inverters for eliminating some higher order harmonics while maintaining the required fundamental voltage. This technique can be applied to multilevel inverters with any number of levels; as an example in this paper, a 7-level inverter is considered, and

A genetic-algorithm (GA) based strategy is presented for suitable weight selection in H? loop shaping. The loop shaping is currently performed manually and usually a trial and error method. To solve this problem an automatic loop-shaping method based on GAs is introduced and air vehicle example is used to examine the performance of the proposed automatic loop shaping. A classical

Advanced geneticalgorithms (GAs) are used to automatically carry out the fine-tuning of the parameter settings of classical PID (proportional plus integral plus derivative) controllers. The basic concept and working principle of GAs are introduced and compared with those of traditional optimization techniques. An advanced GA which can rapidly optimize the parameter auto-tuning process of classical PID controllers is designed

We describe a parallel geneticalgorithm (GA) that automatically generates circuit designs using evolutionary search. A circuit-construction programming language is introduced and we show how evolution can generate practical analog circuit designs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. We present experimental results as applied to analog filter and amplifier design tasks.

Lohn, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris; Norvig, Peter (Technical Monitor)

A geneticalgorithm is used with density functional theory to investigate the catalytic properties of 38- and 79-atom bimetallic core-shell nanoparticles for the oxygen reduction reaction. Each particle is represented by a two-gene chromosome that identifies its core and shell metals. The fitness of each particle is specified by how close the d-band level of the shell is to that of the Pt(111) surface, a catalyst known to be effective for oxygen reduction. The geneticalgorithm starts by creating an initial population of random core-shell particles. The fittest particles are then bred and mutated to replace the least-fit particles in the population and form successive generations. The geneticalgorithm iteratively refines the population of candidate catalysts more efficiently than Monte Carlo or random sampling, and we demonstrate how the average energy of the surface d-band can be tuned to that of Pt(111) by varying the core and shell metals. The binding of oxygen is a more direct measure of catalytic activity and is used to further investigate the fittest particles found by the geneticalgorithm. The oxygen binding energy is found to vary linearly with the d-band level for particles with the same shell metal, but there is considerable variation in the trend across different shells. Several particles with oxygen binding energies similar to Pt(111) have already been investigated experimentally and found to be active for oxygen reduction. In this work, many other candidates are identified.

The performance (in term of error rate) of biometric systems can be improved by combining them. Multiple fusion techniques can be applied from classical logical operations to more complex ones based on score fusion. In this paper, we use a geneticalgorithm to learn the parameters of different multibiometrics fusion functions. We are interested in biometric systems usable on any

The authors outline an approach to four-coloring of maps using a geneticalgorithm. The objective of this map coloring problem is to shade each region of the map with a color such that no adjacent regions are of the same color. Simulation results show that the 48-region USA map problem can be solved on a PC platform within 400 generations

We present a geneticalgorithm (GA) for solving an ill-posed inverse problem from exploration geophysics, namely the estimation of a distribution of conductivities from a set of electrical current penetration depths. We formulate the inversion as a Bayesian inference problem and use a GA to efficiently sample the posterior parameter distribution. In particular, the conductivity distribution with maximum entropy relative

Response surface methodology (RSM) is a methodology that combines experimental designs and statistical techniques, for empirical model building and optimisation. By conducting experiments and applying regression analysis, RSM seeks to relate a response to some input variables. This work aims at integrating response surface methodology with geneticalgorithms (GAs) to realise a GA-based prototype system for the determination of near

A geneticalgorithm (GA) for automating the segmentation of the prostate on pelvic computed tomography (CT) images is presented here. The images consist of slices from three-dimensional CT scans. Segmentation is typically performed manually on these images for treatment planning by an expert physician, who uses the \\

This paper presents results from the first known application of the geneticalgorithm (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

This paper describes a design procedure for the decentralized fuzzy control of a 5-dof robotic manipulator based on GeneticAlgorithms (GAs). Compared to traditional PID, fuzzy controllers better lend themselves to the nonlinear, coupled dynamics of industrial manipulators, thanks to their universal approximation capabilities. In addition, GAs allow a full exploitation of the potentialities of fuzzy control, being able to

F. Cupertino; Vincenzo Giordano; David Naso; Luigi Salvatore; Biagio Turchiano

This paper aims at the construction of the music composition system that has two important points. The one is that the system helps musically unskilled people to compose their own music. The other is that the system composes musical works reflecting users subjective evaluation of music. This paper uses the technique of Interactive GeneticAlgorithm, since the interaction between users

The eight basic elements to design geneticalgorithms (GA) are described and applied to solve a low demand distribution problem of passengers for a hub airport in Alicante and 30 touristic destinations in Northern Africa and Western Europe. The flexibility of GA and the possibility of creating mutually beneficial feed-back processes with human intelligence to solve complex problems as well

This paper examines a novel optimization technique called geneticalgorithms and its application to the optimization of reliability allocation strategies. Reliability allocation should occur in the initial stages of design, when the objective is to determine an optimal breakdown or allocation of reliability to certain components or subassemblies in order to meet system specifications. The reliability allocation optimization is applied to the design of a cluster tool, a highly complex piece of equipment used in semiconductor manufacturing. The problem formulation is presented, including decision variables, performance measures and constraints, and geneticalgorithm parameters. Piecewise ``effort curves`` specifying the amount of effort required to achieve a certain level of reliability for each component of subassembly are defined. The geneticalgorithm evolves or picks those combinations of ``effort`` or reliability levels for each component which optimize the objective of maximizing Mean Time Between Failures while staying within a budget. The results show that the geneticalgorithm is very efficient at finding a set of robust solutions. A time history of the optimization is presented, along with histograms or the solution space fitness, MTBF, and cost for comparative purposes.

In this paper, we propose a novel trajectory planning method for a robot manipulator whose workspace includes several obstacles. To generate the robot’s trajectory we developed a geneticalgorithm (GA) to search for valid and optimal solutions to the trajectory in task space. In this method, a polynomial based on Hermite cubic interpolation is applied to approximate the time histories

The objective of this paper is to present first results of a running study on optimization of aircraft components (composite panels of a typical vertical tail plane) by using GeneticAlgorithms (GA) and Neural Networks (NN). The panels considered are standardized to some extent but still there is a wide scope of discrete and continuous design variables that can be

We propose and demonstrate an automatic optical fiber align- ment system using geneticalgorithms. Connecting optical fibers is dif- ficult because the connecting edges should be aligned with sub-micron- meter resolution. It, therefore, takes long time even for a human expert. Although automatic fiber alignment systems are being developed, they cannot be used practically if the degrees of freedom of

Congestion cost allocation is an important issue in congestion management. This paper presents a geneticalgorithm (GA) to determine the optimal generation levels in a deregulated market. The main issue is congestion in lines, which limits transfer capability of a system with available generation capacity. Nodal pricing method is used to determine locational marginal price (LMP) of each generator at

S. M. H. Nabav; S. Jadid; M. A. S. Masoum; A. Kazemi

Recently, in order to successfully combine the positive attributes of both periodic and random arrays into one design, a novel class of arrays, known as fractal-random arrays, has been introduced. In addition, several researchers have successfully used geneticalgorithms, robust global optimization techniques based on natural selection, to find solutions to complex array layout problems. This paper introduces a type

This paper explains the development and implementation of a new methodology for expanding existing computer networks. Expansion is achieved by adding new communication links and computer nodes such that reliability measures of the network are optimized within specified constraints. A geneticalgorithm-based computer network expansion methodology (GANE) is developed to optimize a specified objective function (reliability measure) under a given

Urban bus route network design involves determining a route configuration with a set of transit routes and associated frequencies that achieves the desired objective. This can be formulated as an optimization problem of minimizing the overall cost (both the user`s and the operator`s) incurred. In this paper, the use of geneticalgorithms (GAs), a search and optimization method based on

A new technique for the design optimization of electromagnetic devices that adopts the geneticalgorithms (GAs) as the search method is presented. The method is applied to the optimization of the shape of a pole face in an electric motor. The electromagnetic analysis of the devices implemented is performed using 2D finite elements. The results show an excellent promise and

This paper compares two evolutionary computation paradigms: geneticalgorithms and particle swarm optimization. The operators of each paradigm are reviewed, focusing on how each affects search behavior in the problem space. The goals of the paper are to provide additional insights into how each paradigm works, and to suggest ways in which performance might be improved by incorporating features from

We describe a parallel geneticalgorithm (GA) that automatically generates circuit designs using evolutionary search. A circuit-construction programming language is introduced and we show how evolution can generate practical analog circuit designs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. We present experimental results as applied to analoglter and amplier design tasks.

Jason D. Lohn; Silvano P. Colombano; Dimitris Stassinopoulos

This thesis investigates the application of GeneticAlgorithms (GAs) to multiplecriteria problems in engineering design and operation. The GA is an evolutionarycomputing technique which applies Darwinian principles such as survival of the fittest,mating and mutation to a population of individuals to evolve good solutions to a broadrange of problems. GAs are normally used as single criterion optimisers. However, aMultiple Criteria

Many real-world search and optimization problems involve inequality and\\/or equality con- straints and are thus posed as constrained optimization pro blems. In trying to solve con- strained optimization problems using geneticalgorithms ( GAs) or classical optimization methods, penalty function methods have been the most popular approach, because of their simplicity and ease of implementation. However, since the penalty function

Many real-world search and optimization problems involve inequality and\\/or equality constraints and are thus posed as constrained optimization problems. In trying to solve constrained optimization problems using geneticalgorithms (GAs) or classical optimization methods, penalty function methods have been the most popular approach, because of their simplicity and ease of implementation. However, since the penalty function approach is generic and

This paper presents an optimization method for stall-regulated horizontal-axis wind turbines. A hybrid approach is used that combines the advantages of a geneticalgorithm 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

The travelling salesman problem (TSP) is one of the extensively studied optimization problem. The numerous direct applications of the TSP bring life to the research area and help to direct future work. To solve this problem many techniques have been developed. Geneticalgorithm is one among those which solves this problem by using the processes observed in natural evolution to

Ramani R. Geetha; Vasumathy Seenuvasan; Nishaa Bouvanasilan

Tam and Chan (1998) present a parallel geneticalgorithm approach to solve the facility layout problem. They adopt a slicing tree representation of a floor layout. The coding scheme represents a layout as a string with three parts. This paper demonstrates the difficulties in applying classical crossover and mutation operators for solving facility layout problems. The paper modifies the representation

This paper presents how an optimal optimization method, geneticalgorithm (GA), is applied for finding a suitable combination of VISSIM parameters. The north-south (N-S) expressway is investigated and simulated in VISSIM platform using field data obtained from traffic information collecting system (TICS) in Shanghai. Numerous simulation tests indicate that the following main parameters have affected simulation precision most deeply, such

This paper presents how an optimal optimization method, GeneticAlgorithm (GA), is applied for finding a suitable combination of VISSIM parameters. The North-South (N- S) Expressway is investigated and simulated in VISSIM platform using field data obtained from Traffic Information Collecting System (TICS) in Shanghai. Numerous simula- tion tests indicate that the following main parameters have affected simulation precision most

In this paper, the geneticalgorithm is used to optimize the milling parameters in the milling process so that the tool life can be enhanced and processing costs can be reduced. LABVIEW is used as software development platform to program, monitor the tool wear and determine the tool life. Through the method of orthogonal experiment to design experiment and then

Traditional warehouse operation management always relies on experience to arrange inventory goods to available space once they arrived, resulting in the inefficient warehouse work. This paper considers goods' turnover rate and shelves' stability as principles to construct a multiobjective optimization mathematical model. By setting up random goal weight to improve traditional geneticalgorithm, and based on MATLAB software platform to

Abstract This report provides documentation for the general purpose geneticalgorithm toolbox for matlab in C++. The fitness function used in the toolbox is written in matlab. The toolbox provides dierent selection, recombination, mutation, niching, and constraint-handling operators. Problems with single and multiple objectives can be solved with the toolbox. Moreover, the toolbox is easily extensible and customizable for incorporating

The flow shop scheduling problem has the property of modeling complexity, computational complexity, dynamic multi-constraint and multi-targeted. In recent years, a variety of evolutionary computation methods and the application of geneticalgorithms have been gradually introduced into the production scheduling problem. In the paper, we design a new production scheduler program by using Matlab system and the method based on

A multiobjective geneticalgorithm (MOGA) is used to compare submarine propulsion concepts and seek out tradeoffs in their design. Concepts include the novel integrated electric propulsion (IEP) concept and hybrid steam turbine and electric motor drive. System design and supervisory controllers are optimized under multiple operating conditions to give maximum propulsive efficiency. The advantages of each propulsive topology are compared,

Benjamin A. Skinner; Geoffrey T. Parks; Patrick R. Palmer

This paper presents a study on optimization of methanol synthesis reactor to enhance overall production. A mathematical heterogeneous model of the reactor was used for optimization of reactor performance, both at steady state and dynamic conditions. Here, geneticalgorithms were used as powerful methods for optimization of complex problems. Initially, optimal temperature profile along the reactor was studied. Then, a

Queuing research and its applications have been studied extensively by concentrating mainly on design, performance and running of the service facility under study. In this paper we show how a simple behavioral queuing system can be modeled using a Cellular Automata; and then we show how a GeneticAlgorithm can be used to optimize the behavioral properties of this agent

K. Sankaranarayanan; E. R. Larsen; A. van Ackere; C. A. Delgado

Stacking sequence design of a composite laminate with a given set of plies is a combinatorial problem of seeking an optimal permutation. Permutation geneticalgorithms optimizing the stacking sequence of a composite laminate for maximum buckling load are studied. A new permutation GA named gene–rank GA is developed and compared with an existing Partially Mapped Permutation GA, originally developed for

Boyang Liu; Raphael T. Haftka; Mehmet A. Akgün; Akira Todoroki

This paper describes the use of a geneticalgorithm with memory for the design of minimum thickness composite laminates subject to strength, buckling and ply contiguity constraints. A binary tree is used to efficiently store and retrieve information about past designs. This information is used to construct a set of linear approximations to the buckling load in the neighbourhood of

N. KOGISO; L. T. WATSON; Z. GÜRDAL; R. T. HAFTKA; S. NAGENDRA

In this paper, we examine the use of geneticalgorithms to fit piecewise linear functions to data in R2. The number of pieces, the location of the knots, and the underlying distribution of the data are assumed to be unknown. We discuss existing methods wh...

In the paper, an optimization method based on geneticalgorithm was proposed. The objective of the optimization procedure is to minimize the material and construction costs of reinforced concrete structural elements subjected to serviceability and strength requirements described by the code for design of concrete structures Code. Different constraints conditions according to the code for design of concrete structures were

The structural members are generally to be selected from available profiles list is most important practical considerations in the optimization of discrete structures. Geneticalgorithms show certain advantages over other classical optimization procedures in structural optimization of discrete variables. In order to overcoming the shortcoming of simple GA, we introduce the idea of directed mutation and present an active evolution

A geneticalgorithm controlled multispot transmitter is proposed as an alternative approach to optimising the power distribution for single element receivers in fully diffuse mobile indoor optical wireless communication systems. Results are presented that show by dynamically controlling the powers of individual diffusion spots, a consistent power distribution, with negligible impact to bandwidth and rms delay spread, can be created

Matthew D. Higgins; Roger J. Green; Mark S. Leeson

The paper presents an application of geneticalgorithms to the design of a longitudinal flight controller for a hypersonic accelerator vehicle which is to be used to launch small satellites. A feature of hypersonic air-breathing flight vehicles is the high level of engine integration with the airframe. As a result, maintenance of vehicle attitude is not simply an issue of

The development of flight control systems using classical control techniques is a costly and time-consuming process. This paper shows how to use geneticalgorithm (GA) and modern control techniques to determine the control parameters of the flight control law within the classical structure. An example of designing longitudinal flight control law with the proposed method is presented. The designed flight

Feng Qing-tang; Jiang Zhi-hong; Zhu Ji-hong; Liu Shi-qian

The performance of classiflcation algorithms in machine learn- ing is afiected by the features used to describe the labeled examples pre- sented 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

FRESIM is a microscopic time-stepping simulation model for freeway corridor traffic operations. To enable FRESIM to realistically simulate expressway traffic flow in Singapore, parameters that govern the movement of vehicles needed to be recalibrated for local traffic conditions. This paper presents the application of a geneticalgorithm as an optimization method for finding a suitable combination of FRESIM parameter values.

Ruey-Long Cheu; Xin Jin; D. Srinivasa; K. C. Ng; Y. L. Ng

In this article the author posits a field of computing based on the geneticalgorithm. This approach to programming mimics evolution by utilizing a computer to solve problems on a trial and error basis and ascertain the best answer through natural selection of the best of the computer's guesses. The author discusses the viability of this system in comparison to that of artificial intelligence.

Walbridge, C.T. (US Government on Aquatic Toxicology (US))

PGAPack is the first widely distributed parallel geneticalgorithm library. Since its release, several thousand copies have been distributed worldwide to interested users. In this paper we discuss the key components of the PGAPack design philosophy and present a number of application examples that use PGAPack.

It is appealing to consider hybrids of neural-network learning algorithmswith evolutionary search procedures, simply because Nature hasso successfully done so. In fact, computational models of learning andevolution offer theoretical biology new tools for addressing questionsabout Nature that have dogged that field since Darwin [Belew, 1990].The concern of this paper, however, is strictly artificial: Can hybridsof connectionist learning algorithms and genetic

Richard K. Belew; John Mcinerney; Nicol N. Schraudolph

The paper introduces the principle of geneticalgorithm and analyses the selection of parameters of geneticalgorithm. By an example, the paper researches the different effect of each parameter. Such as, the size of the population (M), the probability of crossover (Pc) and the probability of mutation (Pm). By the experimentation and simulation, The paper brings forward a general method

An account is given of illustrative applications of geneticalgorithms 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.

A geneticalgorithm code, JavaGenes, was written in Java and used to evolve pharmaceutical drug molecules and digital circuits. JavaGenes was run under the Condor cycle-scavenging batch system managing 100-170 desktop SGI workstations. Geneticalgorithms mimic biological evolution by evolving solutions to problems using crossover and mutation. While most geneticalgorithms evolve strings or trees, JavaGenes evolves graphs representing (currently) molecules and circuits. Java was chosen as the implementation language because the geneticalgorithm requires random splitting and recombining of graphs, a complex data structure manipulation with ample opportunities for memory leaks, loose pointers, out-of-bound indices, and other hard to find bugs. Java garbage-collection memory management, lack of pointer arithmetic, and array-bounds index checking prevents these bugs from occurring, substantially reducing development time. While a run-time performance penalty must be paid, the only unacceptable performance we encountered was using standard Java serialization to checkpoint and restart the code. This was fixed by a two-day implementation of custom checkpointing. JavaGenes is minimally integrated with Condor; in other words, JavaGenes must do its own checkpointing and I/O redirection. A prototype Java-aware version of Condor was developed using standard Java serialization for checkpointing. For the prototype to be useful, standard Java serialization must be significantly optimized. JavaGenes is approximately 8700 lines of code and a few thousand JavaGenes jobs have been run. Most jobs ran for a few days. Results include proof that geneticalgorithms can evolve directed and undirected graphs, development of a novel crossover operator for graphs, a paper in the journal Nanotechnology, and another paper in preparation.

Global structural optimizations with a geneticalgorithm were performed for atomic cluster and surface systems including aluminum atomic clusters, Si magic clusters on the Si(111) 7 x 7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstructions. First, the global structural optimizations of neutral aluminum clusters Al{sub n} (n up to 23) were performed using a geneticalgorithm coupled with a tight-binding potential. Second, a geneticalgorithm in combination with tight-binding and first-principles calculations were performed to study the structures of magic clusters on the Si(111) 7 x 7 surface. Extensive calculations show that the magic cluster observed in scanning tunneling microscopy (STM) experiments consist of eight Si atoms. Simulated STM images of the Si magic cluster exhibit a ring-like feature similar to STM experiments. Third, a geneticalgorithm coupled with a highly optimized empirical potential were used to determine the lowest energy structure of high-index semiconductor surfaces. The lowest energy structures of Si(105) and Si(114) were determined successfully. The results of Si(105) and Si(114) are reported within the framework of highly optimized empirical potential and first-principles calculations. Finally, a geneticalgorithm coupled with Si and Ag tight-binding potentials were used to search for Ag-induced Si(111) reconstructions at various Ag and Si coverages. The optimized structural models of {radical}3 x {radical}3, 3 x 1, and 5 x 2 phases were reported using first-principles calculations. A novel model is found to have lower surface energy than the proposed double-honeycomb chained (DHC) model both for Au/Si(111) 5 x 2 and Ag/Si(111) 5 x 2 systems.

This paper introduces the theory of geneticalgorithm. The specific operation flow of geneticalgorithm is described. The application of geneticalgorithm in function optimization has been achieved by the using of matlab programming language. The process of programming shows that it is very easy, flexible and efficient to optimize and compute with matlab language, and the effectiveness of genetic

GeneticAlgorithm (GeneticAlgorithm Chine write for the GA) is a kind of hunting Algorithm bionic global optimization imitating the Darwinian biological evolution theories, is advancing front of complex nonlinear science and artificial intelligence science. In the basic of introducing the GA basic principle and optimization Algorithm, this text leads the GA into the domain of the water pollution control

The Urban Transit Routing Problem (UTRP) involves solving a set of transit route networks, which proved to be a highly complex multi-constrained problem. In this study, a bus route network to find an efficient network to meet customer demands given information on link travel times is considered. An evolutionary optimization technique, called GeneticAlgorithm is proposed to solve the UTRP. The main objective is to minimize the passenger costs where the quality of the route sets is evaluated by a set of parameters. Initial computational experiments show that the proposed algorithm performs better than the benchmark results for Mandl's problems.

A modified geneticalgorithm (GA) is proposed for water distribution network optimization. Several changes are introduced in the selection and mutation processes of a simple GA. In each generation a constant number of solutions is eliminated, the selected ones are ranked for crossover, and the new solutions are allowed to undergo at most one mutation. All these modifications greatly increase the algorithm convergence. The modified GA is tested on the New York City water supply expansion problem. It obtains the lowest-cost feasible solution reported in the literature in far fewer generations than any previous GA.

Montesinos, Pilar; Garcia-Guzman, Adela; Ayuso, Jose Luis

GAs have been shown to be an effective strategy in the off- line design of control systems by a number of practitioners. For example, Krishnakumar and Goldberg (1) and Bramlette and Cusin (2) have demonstrated how genetic optimization methods can be used to derive superior controller structures in aerospace applications in less time (in terms of function evaluations) than that

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 geneticalgorithm, 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 geneticalgorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective geneticalgorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability.

Traditionally, structural loads are measured using strain gages. A loads calibration test must be done before loads can be accurately measured. In one measurement method, a series of point loads is applied to the structure, and loads equations are derived via the least squares curve fitting algorithm using the strain gage responses to the applied point loads. However, many research structures are highly instrumented with strain gages, and the number and selection of gages used in a loads equation can be problematic. This paper presents an improved technique using a geneticalgorithm to choose the strain gages used in the loads equations. Also presented are a comparison of the geneticalgorithm performance with the current T-value technique and a variant known as the Best Step-down technique. Examples are shown using aerospace vehicle wings of high and low aspect ratio. In addition, a significant limitation in the current methods is revealed. The geneticalgorithm arrived at a comparable or superior set of gages with significantly less human effort, and could be applied in instances when the current methods could not.

Geneticalgorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard geneticalgorithm 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 geneticalgorithms 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 geneticalgorithms 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 geneticalgorithm.

A Quality of Service (QoS) multicast routing algorithm in IP/DWDM optical Internet is proposed in this paper, taking QoS requirement and cost into account. Due to the NP-hard nature of this problem, a QoS and cost optimized or sub-optimized multicast routing tree is constructed based on tabu-hierarchy geneticalgorithm with network load balance supported. Simulation results have shown that the proposed algorithm is both feasible and effective, and is advantageous over its counterpart based on the traditional geneticalgorithm.

Wang, Xingwei; Hou, Meijia; Yi, Xiushuang; Huang, Min

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 geneticalgorithm, 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.

We present results from a study comparing a recently developed coevolutionary geneticalgorithm (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.

Lohn, Jason D.; Kraus, William F.; Haith, Gary L.; Clancy, Daniel (Technical Monitor)

The present work demonstrates a geneticalgorithm approach to optimizing the effective material parameters of an acoustic metamaterial. The target device is an acoustic gradient index (GRIN) lens in air, which ideally possesses a maximized index of refraction, minimized frequency dependence of the material properties, and minimized acoustic impedance mismatch. Applying this algorithm results in complex designs with certain common features, and effective material properties that are better than those present in previous designs. After modifying the optimized unit cell designs to make them suitable for fabrication, a two-dimensional lens was built and experimentally tested. Its performance was in good agreement with simulations. Overall, the optimization approach was able to improve the refractive index but at the cost of increased frequency dependence. The optimal solutions found by the algorithm provide a numerical description of how the material parameters compete with one another and thus describes the level of performance achievable in the GRIN lens. PMID:23039548

Li, Dennis; Zigoneanu, Lucian; Popa, Bogdan-Ioan; Cummer, Steven A

Background Genomic sequence data cannot be fully appreciated in isolation. Comparative genomics – the practice of comparing genomic sequences from different species – plays an increasingly important role in understanding the genotypic differences between species that result in phenotypic differences as well as in revealing patterns of evolutionary relationships. One of the major challenges in comparative genomics is producing a high-quality alignment between two or more related genomic sequences. In recent years, a number of tools have been developed for aligning large genomic sequences. Most utilize heuristic strategies to identify a series of strong sequence similarities, which are then used as anchors to align the regions between the anchor points. The resulting alignment is globally correct, but in many cases is suboptimal locally. We describe a new program, GenAlignRefine, which improves the overall quality of global multiple alignments by using a geneticalgorithm to improve local regions of alignment. Regions of low quality are identified, realigned using the program T-Coffee, and then refined using a geneticalgorithm. Because a better COFFEE (Consistency based Objective Function For alignmEnt Evaluation) score generally reflects greater alignment quality, the algorithm searches for an alignment that yields a better COFFEE score. To improve the intrinsic slowness of the geneticalgorithm, GenAlignRefine was implemented as a parallel, cluster-based program. Results We tested the GenAlignRefine algorithm by running it on a Linux cluster to refine sequences from a simulation, as well as refine a multiple alignment of 15 Orthopoxvirus genomic sequences approximately 260,000 nucleotides in length that initially had been aligned by Multi-LAGAN. It took approximately 150 minutes for a 40-processor Linux cluster to optimize some 200 fuzzy (poorly aligned) regions of the orthopoxvirus alignment. Overall sequence identity increased only slightly; but significantly, this occurred at the same time that the overall alignment length decreased – through the removal of gaps – by approximately 200 gapped regions representing roughly 1,300 gaps. Conclusion We have implemented a geneticalgorithm in parallel mode to optimize multiple genomic sequence alignments initially generated by various alignment tools. Benchmarking experiments showed that the refinement algorithm improved genomic sequence alignments within a reasonable period of time.

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 geneticalgorithms may be employed to solve these problems. In fact, there are optimization problems where a geneticalgorithm/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. *

We propose a geneticalgorithm -- MckpGen -- for rate scaling and adaptive streaming of layered video streams from multiple sources in a bandwidth-constrained environment. A geneticalgorithm (GA) consists of several components: a representation scheme; a generator for creating an initial population; a crossover operator for producing offspring solutions from parents; a mutation operator to promote genetic diversity and a repair operator to ensure feasibility of solutions produced. We formulated the problem as a Multiple-Choice Knapsack Problem (MCKP), a variant of Knapsack Problem (KP) and a decision problem in combinatorial optimization. MCKP has many successful applications in fault tolerance, capital budgeting, resource allocation for conserving energy on mobile devices, etc. Geneticalgorithms have been used to solve NP-complete problems effectively, such as the KP, however, to the best of our knowledge, there is no GA for MCKP. We utilize a binary chromosome representation scheme for MCKP and design and implement the components, utilizing problem-specific knowledge for solving MCKP. In addition, for the repair operator, we propose two schemes (RepairSimple and RepairBRP). Results show that RepairBRP yields significantly better performance. We further show that the average fitness of the entire population converges towards the best fitness (optimal) value and compare the performance at various bit-rates.

Multiobjective optimization is clearly one of the most important classes of problems in science and engineering. The solution of real problem involved in multiobjective optimization must satisfy all optimization objectives simultaneously, and in general the solution is a set of indeterminacy points. The task of multiobjective optimization is to estimate the distribution of this solution set, then to find the satisfying solution in it. Many methods solving multiobjective optimization using geneticalgorithm have been proposed in recent twenty years. But these approaches tend to work negatively, causing that the population converges to small number of solutions due to the random genetic drift. To avoid this phenomenon, a multiobjective coevolutionary geneticalgorithm (MoCGA) for multiobjective optimization is proposed. The primary design goal of the proposed approach is to produce a reasonably good approximation of the true Pareto front of a problem. In the algorithms, each objective corresponds to a population. At each generation, these populations compete among themselves. An ecological population density competition equation is used for reference to describe the relation between multiple objectives and to direct the adjustment over the relation at individual and population levels. The proposed approach store the Pareto optimal point obtained along the evolutionary process into external set. The proposed approach is validated using Schaffer's test function f2 and it is compared with the Niched Pareto GA (nPGA). Simulation experiments prove that the algorithm has a better performance in finding the Pareto solutions, and the MoCGA can have advantages over the other algorithms under consideration in convergence to the Pareto-optimal front.

A hybrid geneticalgorithm is used to schedule tasks for 8 satellites, which can be modelled as a robot whose task is to retrieve objects from a two dimensional field. The objective is to find a schedule that maximizes the value of objects retrieved. Typical of the real-world tasks to which this corresponds is the scheduling of ground contacts for a communications satellite. An important feature of our application is that the amount of time available for running the scheduler is not necessarily known in advance. This requires that the scheduler produce reasonably good results after a short period but that it also continue to improve its results if allowed to run for a longer period. We satisfy this requirement by developing what we call a sustainable geneticalgorithm.

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 geneticalgorithm has been developed for finding the best placement for four actuators to produce an uncoupled pitch moment. The geneticalgorithm 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.

GeneticAlgorithms (GA) are a powerful search and optimization technique that can be applied to numerous problems. Unfortunately. GA relies on large numbers of fitness evaluations to determine the relative merits of various solutions to a problem. For problems requiring computationally intensive fitness evaluations this can make GA too expensive to use. We describe a hierarchical technique that we have created called Multi-Grid GeneticAlgorithms (MGGA). MGGA leverages the geometry of a problem space to build a hierarchy of increasingly smaller problem spaces. Optimizations over these smaller spaces are used to seed a population of solutions in a larger space. We explore how MGGA can be applied to several radiation shielding problems.

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which geneticalgorithms (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.

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which geneticalgorithms (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 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.

FRESIM is a microscopic time-stepping simulation model for freeway corridor traffic operations. To enable FRESIM to realistically simulate expressway traffic flow in Singapore, parameters that govern the movement of vehicles needed to be recalibrated for local traffic conditions. This paper presents the application of a geneticalgorithm as an optimization method for finding a suitable combination of FRESIM parameter values. The calibration is based on field data collected on weekdays over a 5.8 km segment of the Ayer Rajar Expressway. Independent calibrations have been made for evening peak and midday off-peak traffic. The results show that the geneticalgorithm is able to search for two sets of parameter values that enable FRESIM to produce 30-s loop-detector volume and speed (averaged across all lanes) closely matching the field data under two different traffic conditions. The two sets of parameter values are found to produce a consistently good match for data collected in different days.

There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Geneticalgorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using geneticalgorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs. PMID:24693235

In this paper, a new technology based on the geneticalgorithm and new image filter for recognizing road traffic sign from motion image captured by a CCD camera in a car was developed. In order to realize a real-time position recognition, the step geneticalgorithm with search region limits and image filter (denoted by SVF) were proposed. The geneticalgorithm

Geneticalgorithms play a significant role, as search techniques for handling com- plex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Geneticalgorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromo- somes, which represent search space solutions, with three operations:

Francisco Herrera; Manuel Lozano; José L. Verdegay

In this paper, an innovative design method of PID controller based on geneticalgorithm tuning parameters, VHDL description and FPGA implementation is proposed. The parameters of the PID controller are optimized by using geneticalgorithm. The intelligent PID controller based on geneticalgorithm is realized by using FPGA. During the realization process, functional modules of the system are divided based

Optimal tuning of Power System Stabilizers (PSSs) parameters using geneticalgorithm is presented in this paper. Selecting the parameters of power system stabilizers which simultaneously stabilize system oscillations is converted to a simple optimization problem which is solved by a geneticalgorithm. The advantage of GeneticAlgorithm (GA) technique for tuning the PSS parameters is that it is independent of

O. Abedinia; M. S. Naderi; A. Jalili; B. Khamenehpour

This paper presents genetic quantum algorithm and its associated evolutionary tools for the voltage and pattern design of piezoelectric actuator. Genetic quantum algorithms (GQA) is similar to geneticalgorithms (GA) maintain a population of individuals but each individual is composed of probabilistic quantum bits for preserving diversity. Also, instead of crossover or mutation, GQA use quantum gates (QG) to update

A new shield - double partiality shield is introduced. Shield structure is optimized according to geneticalgorithm. The partiality radius, length and diameter of hydraulic cylinders are all important parameters for shield structure. The author established multi-objective and multi-constraint geneticalgorithm, coded with the real number. She also established fitness functions, accounted with Matlab geneticalgorithm toolbox and gained parameters.

In this paper a new approach is presented for the approximate separation of two-dimensional all-zero polynomials. The proposed technique is based on the genetic search algorithm. The implemented geneticalgorithm computes the approximate one- dimensional coefficients, the absolute error and useful parameters of the geneticalgorithm, using the software package MATLAB. Three examples along with the simulations are presented to

In this paper we present a novel cost benefit operator that assists multi level geneticalgorithm searches. Through the use of the cost benefit operator, it is possible to dynamically constrain the search of the base level geneticalgorithm, to suit the user's requirements. Initially we review meta-evolutionary (multi-level geneticalgorithm) approaches. We note that the current literature has abundant

George G. Mitchell; Barry Mcmullin; James Decraene

In this article, we propose a new type of geneticalgorithm (GA), the forking GA (fGA), which divides the whole search space into subspaces, depending on the convergence status of the population and the solutions obtained so far. The fGA is intended to deal with multimodal problems that are difficult to solve using conventional GAs. We use a multi-population scheme

This paper presents a recursive least-squares (RLS)- model based active vibration control (AVC) system with geneticalgorithm (GA) self tuning. Tuning parameters include initial regression vectors, gain of RLS-model and gain of controller. The system employs a single-input single-output (SISO) control configuration, and is realised within the Matlab\\/Simulink environment. In the paper, the parameters of RLS-AVC system are tuned so

\\u000a Generating manipulator trajectories considering multiple objectives and obstacle avoidance is a non trivial optimization problem.\\u000a In this paper a multi-objective geneticalgorithm is proposed to address this problem. Multiple criteria are optimized up\\u000a to five simultaneous objectives. Simulations results are presented for robots with two and three degrees of freedom, considering\\u000a two and five objectives optimization. A subsequent analysis of

Eduardo José Solteiro Pires; José António Tenreiro Machado; Paulo B. De Moura Oliveira

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 geneticalgorithms. In the experiments discussed in this report, a simplified platform motion simulator was the fitness function for a geneticalgorithm. 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 geneticalgorithm tool. Section 3 details the representation and implementation of the task within the geneticalgorithm framework. Section 4 discusses the results. Section 5 concludes the report with a summary and suggestions for further research.

This paper deals with an application of a virus-evolutionary geneticalgorithm (VEGA) to hierarchical trajectory planning of a redundant manipulator. The hierarchical trajectory planning is composed of a trajectory generator and position generator. The position generator generates collision-free intermediate positions of the redundant manipulator. The trajectory generator generates a collision-free trajectory based on some intermediate positions sent from the position

Naoyuki KUBOTA; T. Arakawa; T. Fukuda; K. Shimojima

This paper describes the optimization of conductor size and the voltage regulator location and magnitude of long rural distribution lines. The optimization minimizes the lifetime cost of the lines, including capital costs and losses while observing voltage drop and operational constraints using a GeneticAlgorithm (GA). The GA optimization is applied to a real Single Wire Earth Return (SWER) network in regional Queensland and results are presented.

Wishart, Michael; Ledwich, Gerard; Ghosh, Arindam [Queensland University of Technology, Brisbane, Queensland (Australia); Ivanovich, Grujica [Ergon Energy, Toowoomba, Queensland (Australia)

Geneticalgorithms (GA) are applied for the optimization of the structure of metallic clusters by the calculation of the ground-state energies from a tight-binding (Hückel) Hamiltonian. The optimum topology or graph is searched by the use of the adjacency matrix A ij as a natural coding. The initial populations for N-atom clusters are generated from a representative group of fit

We use a geneticalgorithm to learn technical trading rules for the S&P 500 index using daily prices from 1928 to 1995. After transaction costs, the rules do not earn consistent excess returns over a simple buy-and-hold strategy in the out-of-sample test periods. The rules are able to identify periods to be in the index when daily returns are positive

This paper presents a theory of convergence for real-coded geneticalgorithms---GAs that usefloating-point or other high-cardinality codings in their chromosomes. The theory is consistent withthe theory of schemata and postulates that selection dominates early GA performance and restrictssubsequent search to intervals with above-average function value, dimension by dimension. Theseintervals may be further subdivided on the basis of their attraction under

Previous computational models of self-replication in cellular spaces have been manually designed, a very difficult and time-consuming process. This paper introduces the use of geneticalgorithms to discover automata rules that govern emergent self-replicating processes. Given dynamically evolving automata, identification of effective fitness functions for self-replicating structures is a difficult task, and we give one solution to this problem. A

This work studies the problem of CMOS operational am- plifiers (OpAmps) design optimisation. The synthesis of these amplifiers can be translated into a multiple-objective optimisation task, in which a large number of specifications has to be taken into account, i.e., GBW, area, power con- sumption and others. We introduce and apply the GeneticAlgorithm (4) (GA) optimisation technique to the

Abstract: A model,for highway,development,is pre- sented, which uses geographic information systems (GIS), geneticalgorithms (GA), and computer visualization (CV). GIS serves as a repository of geographic,information and enables spatial manipulations,and database management. GAs are used to optimize highway,alignments in a com- plex search space. CVis a technique used to convey the characteristics of alternative solutions, which can be the basis

Heat pumps offer economical alternatives of recovering heat from different sources for use in various industrial, commercial\\u000a and residential applications. In this study, single-stage air-source vapor compression heat pump system has been optimized\\u000a using geneticalgorithm (GA) and fuzzy logic (FL). The necessary thermodynamic properties for optimization were calculated\\u000a by FL. Thermodynamic properties obtained with FL were compared with actual

In an olfaction system (E-Nose) hardware implementation, outputs from the GA approach are used as inputs to an intelligent neural network system for biochemical detection and decision-making. In this paper we present a GeneticAlgorithm for measurement characterization with dynamic inputs. Input measurements are from a given range and are assumed in parallel from chemical-sensor array. An input multiplexer\\/controller\\/Analog-Digital converter

Deepak Gantla; Hoda S. Abdel-Aty-Zohdy; Robert L. Ewing

This paper introduces the usage of MATLAB Distributed Computing Engine(MDCE). The relationship between the volume of the data\\u000a transmitted and the transmission time is tested and the analysis of the data shows that there is a significant linear relationship\\u000a between the two. Then we give an implemen-tation plan of the parallel geneticalgorithm (PGA), and we also carried on the

It has been shown in past research that pseudo-random number generator (PRNG) quality can impact the performance of sim-ple geneticalgorithms (GAs). However, stan-dard empirical tests of random generator quality are not good predictors of when such impacts are likely to occur. In this paper, we introduce a new test of random genera-tor quality, tailored to speci c instances of

Mark M. Meysenburg; Daniel Hoelting; Duane Mcelvain; James A. Foster

Abstract—DARWIN is a ,tool that is able ,to synthesize CMOS opamps, on the basis of a geneticalgorithm. A ran- domly generated initial set of opamps,evolves to a set in which the topologies as well as the transistor sizes of the,opamps ,are adapted to the ,required performance ,specifications. Several design examples illustrate the behavior of DARWIN. I. INTRODUCTION The analog

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 geneticalgorithm 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 geneticalgorithm. 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 GeneticAlgorithm Library (1998), T. Cwik and G. Klimeck, Proc. of 1st NASA/DoD Workshop on Evolvable Hardware, IEEE (1999).

A document discusses a multi-objective, geneticalgorithm 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 geneticalgorithm. 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 geneticalgorithm 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.

Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

The design of an efficient machine learning process through self-adaptation is a great challenge. The goal of meta-learning is to build a self-adaptive learning system that is constantly adapting to its specific (and dynamic) environment. To that end, the meta-learning mechanism must improve its bias dynamically by updating the current learning strategy in accordance with its available experiences or meta-knowledge. We suggest using geneticalgorithms as the basis of an adaptive system. In this work, we propose a meta-learning system based on a combination of the a priori and a posteriori concepts. A priori refers to input information and knowledge available at the beginning in order to built and evolve one or more sets of parameters by exploiting the context of the system"s information. The self-learning component is based on geneticalgorithms and neural Darwinism. A posteriori refers to the implicit knowledge discovered by estimation of the future states of parameters and is also applied to the finding of optimal parameters values. The in-progress research presented here suggests a framework for the discovery of knowledge that can support human experts in their intelligence information assessment tasks. The conclusion presents avenues for further research in geneticalgorithms and their capability to learn to learn.

A geneticalgorithm (GA) was used to reduce cross section data for a hypothetical example consisting of 41 data points and for 10 cross sections on the Kootenai River. The number of data points for the Kootenai River cross sections ranged from about 500 to more than 2,500. The GA was applied to reduce the number of data points to a manageable dataset because most models and other software require fewer than 100 data points for management, manipulation, and analysis. Results indicated that the program successfully reduced the data. Fitness values from the geneticalgorithm were lower (better) than those in a previous study that used standard procedures of reducing the cross section data. On average, fitnesses were 29 percent lower, and several were about 50 percent lower. Results also showed that cross sections produced by the geneticalgorithm were representative of the original section and that near-optimal results could be obtained in a single run, even for large problems. Other data also can be reduced in a method similar to that for cross section data.

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 geneticalgorithm 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 geneticalgorithms give a significant improvement in performance over current techniques. We also show that this improvement is achieved without penalising any class of user.

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 geneticalgorithm 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.

While researching various gradient index glass families for superb color correction using ZEMAX1 optical design program, the authors found that certain solutions could only be found using the Hammer routine2. Hammer is a geneticalgorithm that breeds a particular lens configuration with variations of itself3. It is not intended to be a global search routine. Hammer is typically used after the best performance is obtained using the standard damped least squares (DLS) algorithm with the default merit function (MF) based on minimizing root mean square (RMS) spot size. Upon this discovery, the authors proceeded to explore the benefit of using the genetic Hammer algorithm on three different lens systems. To make the solution space more complicated, two axial gradient index (AGRIN) elements were used in each lens type; a bi- AGRIN cemented doublet; a bi-AGRIN air spaced triplet with CaF2 as the center element, and a double Gauss with four AGRIN elements and two CaF2 elements. AGRIN elements were used in each lens to provide a more complex solution space and to make optimization more difficult. After optimization, the performance of each lens was compared wiht the conventionally optimized counterpart using the default MF with a DLS algorithm. After this comparison was made, another trade study was done between the Hammer and DLS algorithms, but in this case, the optimization used a custom MF instead of the default MF. The authors believe this study shows the importance of MF construction over that of using the default RMS spot size metric. A significant improvement was obtained for all lenses with the default MF using the Hammer over the DLS technique, but that improvement was less obvious when a custom MF was used.

The paper analyzes the merits and drawbacks of the geneticalgorithm and BP neural network, combines with the improved geneticalgorithm and BP neural network to obtain a new algorithm. The new algorithm is used in the fault diagnosis of electro-hydraulic servo valve and justified its validity, accuracy and rapidity by experiment. The BP algorithm, the conventional GA-BP algorithm and

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 geneticalgorithm 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.

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 geneticalgorithm 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.

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 geneticalgorithm 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 geneticalgorithm 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.

We consider a high speed integrated services network, and investigate compare and contrast the performance of two algorithms for solving the Aggregated Bandwidth Allocation (BA) problem. The algorithms we focus our attention are: (1) a classical constrained optimisation (CCO) algorithm and (2) a constrained optimisation GeneticAlgorithm (GA). We adopt the Virtual Path (VP) concept for ATM (Asynchronous Transfer Mode)

Andreas Pitsillides; George Stylianou; Constantinos S. Pattichis; Y. Ahmet Sekercioglu; Athanasios V. Vasilakos

In this paper a hybrid metaheuristic for biclustering based on Scatter Search and GeneticAlgorithms 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 GeneticAlgorithms 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 geneticalgorithm recently published.

Nepomuceno, Juan A.; Troncoso, Alicia; Aguilar–Ruiz, Jesús S.

Java geneticalgorithm (JGA) is a flexible object-oriented framework for rapid prototyping of evolutionary algorithms. Even though JGA has proven to be flexible and efficient in practice, parallelization opens new avenues to the framework. Java grid-enabled geneticalgorithm (JG2A) is a new generation of JGA that exploits parallelism in geneticalgorithms in two ways: first, it allows the execution in

Andrés Bernal; Mauricio A. Ramírez; Harold Castro; Jose L. Walteros; Andrés L. Medaglia

A revolutionary new concept for solving the neutron spectrum unfolding problem using geneticalgorithms (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 novel theory of information acquisition-"compressive sampling" has been applied in this paper, and goes against the common wisdom in data acquisition of Shannon theorem. CS theory asserts that one can recover certain signals and images perfectly from far fewer samples or measurements than traditional methods use. This paper presents an improvement on geneticalgorithm instead of match pursuit algorithm in consideration of the enormous computational complexity on sparse decomposition. Then the whole image is divided into small blocks which can be processed by sparse decomposition, and an end to decomposition is determined by PSNR threshold adaptively. At last, the experiment results show that good performance on image reconstruction with less computational complexity has been achieved.

This paper presents a novel fuzzy clustering technique based on grouping geneticalgorithms (GGAs), which are a class of evolutionary algorithms especially modified to tackle grouping problems. Our approach hinges on a GGA devised for fuzzy clustering by means of a novel encoding of individuals (containing elements and clusters sections), a new fitness function (a superior modification of the Davies Bouldin index), specially tailored crossover and mutation operators, and the use of a scheme based on a local search and a parallelization process, inspired from an island-based model of evolution. The overall performance of our approach has been assessed over a number of synthetic and real fuzzy clustering problems with different objective functions and distance measures, from which it is concluded that the proposed approach shows excellent performance in all cases.

Real number geneticalgorithms (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.

The BP nerve network has been widely applied on fault diagnosis. The BP network due to adopt search arithmetic along grads drop, therefore there are some problems such as slow convergence rate and easily getting into local infinitesimal. The geneticalgorithms has the excellence of rapid searching rate. Therefore, auto-adapt geneticalgorithms is adopted to optimize the BP algorithms in

This paper presents a user-driven geneticalgorithm for directed graph drawing. An interactive framework is considered where users can focus the algorithm on regions of the drawing that need major improvement, or include domain knowledge as layout constraints. The paper describes how focus and user constraints are managed by the geneticalgorithm. The combination of user's skills with automatic tools

In order to overcome the defect of single-population geneticalgorithm which is easy to converge at a local optimal solution an improved dual-population algorithm is proposed and it has been put into the use of rectangle layout optimization. In the algorithm initial populations are initialized in different ways. Each population uses different genetic operators and different immigration operations throughout the

This paper presents an algorithm for optimal path planning for mobile robots using geneticalgorithms coupled with morphological image preprocessing of the terrain. Path planning in a given environment, being a NP-hard problem, is computationally demanding especially if exact or deterministic techniques are employed. This paves the way for the use of evolutionary computing techniques, such as geneticalgorithms (GAs),

Much research has been done in developing improved geneticalgorithms (GA's). Past research has focused on the improvement of operators and parameter settings and indicates that premature convergence is still the preeminent problem in GA's. This paper presents an improved geneticalgorithm based on migration and artificial selection (GAMAS). GAMAS is an algorithm whose architecture is specifically designed to confront

In order to overcome premature phenomenon of simple geneticalgorithms and inability to optimize algorithms with complex constraints, an improved geneticalgorithms based on some improved methods is presented in this paper and is applied in optimization design of frame structure by adopting adaptive crossover rate and mutation rate, adjusting population size, fitness and penalty function and elitist strategy in

Parallelized versions of geneticalgorithms (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. Geneticalgorithms 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. Geneticalgorithms 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 geneticalgorithm 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.

Long, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris

In this paper, the MPI master-slave parallel geneticalgorithm is implemented by analyzing the basic geneticalgorithm and parallel MPI program, and building a Linux cluster. This algorithm is used for the test of maximum value problems (Rosen brocks function) .And we acquire the factors influencing the master-slave parallel geneticalgorithm by deriving from the analysis of test data. The experimental data shows that the balanced hardware configuration and software design optimization can improve the performance of system in the complexity of the computing environment using the master-slave parallel geneticalgorithms.

Combination of many different classifiers can improve classification accuracy. Sugeno and choquet integrals with respect to the fuzzy measure possess many desired properties, so in this paper they are used to combine multiple neural network classifiers. However, it is difficult to determine fuzzy measures in real problems. In this paper, we present two methods, one is that we assign the degree of importance of each network based on how good these networks classify each class of the training data, the other is by geneticalgorithms (GAs), to obtain fuzzy measures, each taking into account the intuitive idea that each classifier always possesses different classification ability for each class. In the experiment, several databases in UCI repository are tested using these combination schemes and compared with C4.5. They are also applied to a multisensor fusion system for workpiece identification. Experimental results confirm the superiority of these presented methods.

A learning algorithm based on geneticalgorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.

Bornholdt, S. [Heidelberg Univ., (Germany). Inst., fuer Theoretische Physik; Graudenz, D. [Lawrence Berkeley Lab., CA (United States)

This paper compares two algorithms applied to the task allocation of multiple Unmanned Aerial Vehicles (UAVs) for an electronic warfare mission. The electronic warfare mission scenario is discussed and a review of both the geneticalgorithm and simulated ...

This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using a geneticalgorithm. 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.

Rahideh, A.; Korakianitis, T.; Ruiz, P.; Keeble, T.; Rothman, M. T.

A multidisciplinary optimization scheme of airborne radome is proposed. The optimization procedure takes into account the structural and the electromagnetic responses simultaneously. The structural analysis is performed with the finite element method using Patran/Nastran, while the electromagnetic analysis is carried out using the Plane Wave Spectrum and Surface Integration technique. The geneticalgorithm is employed for the multidisciplinary optimization process. The thicknesses of multilayer radome wall are optimized to maximize the overall transmission coefficient of the antenna-radome system under the constraint of the structural failure criteria. The proposed scheme and the optimization approach are successfully assessed with an illustrative numerical example.

This paper examines the applicability of geneticalgorithms 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.

This paper examines the applicability of geneticalgorithms (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.

Fermentation kinetics is important for optimizing control and up-scaling fermentation process. We studied submerged fermentation kinetics of Morchella. Applying the geneticAlgorithm in the Matlab software platform, we compared suitability of the Monod and Logistic models, both are commonly used in process of fungal growth, to describe Morchella growth kinetics. Meanwhile, we evaluated parameters involved in the models for Morchella growth, EPS production and substrate consumption. The results indicated that Logistic model fit better with the experimental data. The average error of this model was 5.8%. This kinetics model can be useful for optimizing and up-scaling fungal fermentation process. PMID:18998550

Geneticalgorithms 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

The general problem of optimization with arbitrary merit and constraint functions, which could be convex, concave, monotonic, or non-monotonic, is treated using stochastic methods. To improve the efficiency of the random search methods, a geneticalgorithm for the search phase and a discriminant function for the constraint-control phase were utilized. The validity of the technique is demonstrated by comparing the results to published test problem results. Numerical experimentation indicated that for cases where a quick near optimum solution is desired, a general, user-friendly optimization code can be developed without serious penalties in both total computer time and accuracy.

An array of antennas is usually used in long distance communication. The observation of celestial objects necessitates a large array of antennas, such as the Giant Metrewave Radio Telescope (GMRT). Optimizing this kind of array is very important when observing a high performance system. The geneticalgorithm (GA) is an optimization solution for these kinds of problems that reconfigures the position of antennas to increase the u-v coverage plane or decrease the sidelobe levels (SLLs). This paper presents how to optimize a correlator antenna array using the GA. A brief explanation about the GA and operators used in this paper (mutation and crossover) is provided. Then, the results of optimization are discussed. The results show that the GA provides efficient and optimum solutions among a pool of candidate solutions in order to achieve the desired array performance for the purposes of radio astronomy. The proposed algorithm is able to distribute the u-v plane more efficiently than GMRT with a more than 95% distribution ratio at snapshot, and to fill the u-v plane from a 20% to more than 68% filling ratio as the number of generations increases in the hour tracking observations. Finally, the algorithm is able to reduce the SLL to -21.75 dB.

Numerical experiments were conducted to find out the extent to which a GeneticAlgorithm (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.

\\u000a Different kinds of geneticalgorithms have been investigated for a parameter identification of a fermentation process. Altogether\\u000a eight realizations of geneticalgorithms have been presented - four of simple geneticalgorithms and four of multi-population\\u000a ones. Each of them is characterized with a different sequence of implementation of main genetic operators, namely selection,\\u000a crossover and mutation. A comparison of considered

This paper focuses on the introduction of a new evolutionary algorithm for data clustering, the Self-sizing Genome GeneticAlgorithm. It is akin to a messy GeneticAlgorithm and does not use a priori information about the number of clusters. A new recombination operator, gene-pooling, is introduced, while fitness is based on simultaneously maximizing intra-cluster homogeneity and inter-cluster separability. This algorithm

Ivan De Falco; Ernesto Tarantino; Antonio Della Cioppa; F. Gagliardi

This paper presents a novel multi-objective geneticalgorithm (MOGA) based on the NSGA-II algorithm, which uses metamodels to determine optimal sampling locations for installing pressure loggers in a water distribution system (WDS) when parameter uncertainty is considered. The new algorithm combines the multi-objective geneticalgorithm with adaptive neural networks (MOGA–ANN) to locate pressure loggers. The purpose of pressure logger installation

Recent advancement of technologies has now made it routine to obtain and compare gene orders within genomes. Rearrangements of gene orders by operations such as reversal and transposition are rare events that enable researchers to reconstruct deep evolutionary histories. An important application of genome rearrangement analysis is to infer gene orders of ancestral genomes, which is valuable for identifying patterns of evolution and for modeling the evolutionary processes. Among various available methods, parsimony-based methods (including GRAPPA and MGR) are the most widely used. Since the core algorithms of these methods are solvers for the so called median problem, providing efficient and accurate median solver has attracted lots of attention in this field. The "double-cut-and-join" (DCJ) model uses the single DCJ operation to account for all genome rearrangement events. Because mathematically it is much simpler than handling events directly, parsimony methods using DCJ median solvers has better speed and accuracy. However, the DCJ median problem is NP-hard and although several exact algorithms are available, they all have great difficulties when given genomes are distant. In this paper, we present a new algorithm that combines geneticalgorithm (GA) with genomic sorting to produce a new method which can solve the DCJ median problem in limited time and space, especially in large and distant datasets. Our experimental results show that this new GA-based method can find optimal or near optimal results for problems ranging from easy to very difficult. Compared to existing parsimony methods which may severely underestimate the true number of evolutionary events, the sorting-based approach can infer ancestral genomes which are much closer to their true ancestors. The code is available at http://phylo.cse.sc.edu. PMID:23658708

A nonlinear predictive control technique is developed to determine the optimal drying profile for a drying process. A complete nonlinear model of the baker's yeast drying process is used for predicting the future control actions. To minimize the difference between the model predictions and the desired trajectory throughout finite horizon, an objective function is described. The optimization problem is solved using a geneticalgorithm due to the successful overconventional optimization techniques in the applications of the complex optimization problems. The control scheme comprises a drying process, a nonlinear prediction model, an optimizer, and a genetic search block. The nonlinear predictive control method proposed in this paper is applied to the baker's yeast drying process. The results show significant enhancement of the manufacturing quality, considerable decrease of the energy consumption and drying time, obtained by the proposed nonlinear predictive control. PMID:17063940

This paper presents a new technique for segmenting thermographic images using a geneticalgorithm (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 geneticalgorithms. 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.

This research continued work that began under the support of NASA Grant NAG1-2119. The focus of this effort was to continue investigations of GeneticAlgorithm (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 geneticalgorithm, 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.

This paper describes the use of a multiobjective geneticalgorithm 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 geneticalgorithm—differential evolution.

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 geneticalgorithm 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.

In this paper a new geneticalgorithm called the Breeder GeneticAlgorithm(BGA) is introduced. The BGA is based on artificial selectionsimilar to that used by human breeders. A predictive model for the BGA ispresented which is derived from quantitative genetics. The model is used topredict the behavior of the BGA for simple test functions. Different mutationschemes are compared by computing

To control a secondary inverted pendulum, a kind of neural network control method is introduced, in which geneticalgorithm and neural network are mixed. The notion of using the multi-layer forward neural network as the representation method of the genetic searching technique is introduced, and the weighs of neural network are trained by geneticalgorithm. So the methods remains the

A variety of hybrid geneticalgorithms has been recently proposed to address the vehicle routing problem with time windows (VRPTW), a problem known to be NP-hard. However, very few genetic-based approaches exploit implicit knowledge provided by the structure of the intermediate solutions computed during the evolutionary process to explore the solution space. This paper presents a new hybrid geneticalgorithm

This paper presents a geneticalgorithm coordinated by fuzzy rule models to solve the vehicle routing problem with time windows. The fuzzy rule-based coordinators play distinct roles during the geneticalgorithm execution. The aim is to trade-off exploration and exploitation behavior for route and distance minimization. Experimental results using classic benchmark test instances suggest that the fuzzy coordinated genetic approach

This paper presents the use of geneticalgorithms for identification of Escherichia coli fed-batch fermentation process. Geneticalgorithms are a directed random search technique, based on the mechanics of natural selection and natural genetics, which can find the global optimal solution in complex multidimensional search space. The dynamic behavior of considered process has known nonlinear structure, described with a system

Olympia Roeva; Tania Pencheva; Bernd Hitzmann; Stoyan Tzonkov

In applications of the geneticalgorithms (GA) to problems of adaptation to changing environments, maintenance of the diversity\\u000a of the population is an essential requirement. Taking this point into consideration, the authors have proposed to utilize\\u000a the thermodynamical geneticalgorithm (TDGA) for the problems of adaptation to changing environments. The TDGA is a genetic\\u000a algorithm that uses a selection rule

This paper will consider the case for geneticalgorithm optimization in the development of an artificial neural network model. It will provide a methodological evaluation of reported investigations with respect to hydrological forecasting and prediction. The intention in such operations is to develop a superior modelling solution that will be: \\begin{itemize} more accurate in terms of output precision and model estimation skill; more tractable in terms of personal requirements and end-user control; and/or more robust in terms of conceptual and mechanical power with respect to adverse conditions. The geneticalgorithm optimization toolbox could be used to perform a number of specific roles or purposes and it is the harmonious and supportive relationship between neural networks and geneticalgorithms that will be highlighted and assessed. There are several neural network mechanisms and procedures that could be enhanced and potential benefits are possible at different stages in the design and construction of an operational hydrological model e.g. division of inputs; identification of structure; initialization of connection weights; calibration of connection weights; breeding operations between successful models; and output fusion associated with the development of ensemble solutions. Each set of opportunities will be discussed and evaluated. Two strategic questions will also be considered: [i] should optimization be conducted as a set of small individual procedures or as one large holistic operation; [ii] what specific function or set of weighted vectors should be optimized in a complex software product e.g. timings, volumes, or quintessential hydrological attributes related to the 'problem situation' - that might require the development flood forecasting, drought estimation, or record infilling applications. The paper will conclude with a consideration of hydrological forecasting solutions developed on the combined methodologies of co-operative co-evolution and operational specialization. The standard approach to neural-evolution is at the network level such that a population of working solutions is manipulated until the fittest member is found. SANE [Symbiotic Adaptive Neuro-Evolution]1 source code offers an alternative method based on co-operative co-evolution in which a population of hidden neurons is evolved. The task of each hidden neuron is to establish appropriate connections that will provide: [i] a functional solution and [ii] performance improvements. Each member of the population attempts to optimize one particular aspect of the overall modelling process and evolution can lead to several different forms of specialization. This method of adaptive evolution also facilitates the creation of symbiotic relationships in which individual members must co-operate with others - who must be present - to permit survival. 1http://www.cs.utexas.edu/users/nn/pages/software/abstracts.html#sane-c

This paper builds on the work of Meyer and Brill (1988) and subsequent work by Meyer et al. (1990, 1992) on the optimal location of a network of groundwater monitoring wells under conditions of uncertainty. We investigate a method of optimization using geneticalgorithms (GAs) which allows us to consider the two objectives of Meyer et al. (1992), maximizing reliability and minimizing contaminated area at the time of first detection, separately yet simultaneously. The GA-based solution method has the advantage of being able to generate both convex and nonconvex points of the trade-off curve, accommodate nonlinearities in the two objective functions, and not be restricted by the peculiarities of a weighted objective function. Furthermore, GAs have the ability to generate large portions of the trade-off curve in a single iteration and may be more efficient than methods that generate only a single point at a time. Four different codings of geneticalgorithms are investigated, and their performance in generating the multiobjective trade-off curve is evaluated for the groundwater monitoring problem using an example data set. The GA formulations are compared with each other and also with simulated annealing on both performance and computational intensity. Simulated annealing relies on a weighted objective function which can find only a single point along the trade-off curve for each iteration, while all of the multiple-objective GA formulations are able to find a larger number of convex and nonconvex points of trade-off curve in a single iteration. Each iteration of simulated annealing is approximately five times faster than an iteration of the geneticalgorithm, but several simulated annealing iterations are required to generate a trade-off curve. GAs are able to find a larger number of nondominated points on the trade-off curve, while simulated annealing finds fewer points but with a wider range of objective function values. None of the GA formulations demonstrated the ability to generate the entire trade-off curve in a single iteration. Through manipulation of GA parameters certain sections of the trade-off curve can be targeted for better performance, but as performance improves at one section it suffers at another. Run times for all GA formulations were similar in magnitude.

Cieniawski, Scott E.; Eheart, J. Wayland; Ranjithan, S.

We present a geneticalgorithm that deals with document clustering. This algorithm calculates an approximation of the optimum k value, and solves the best grouping of the documents into these k clusters. We have evaluated this algorithm with sets of documents that are the output of a query in a search engine. The experiments show that, most of the times,

Arantza Casillas; Mayte Teresa González De Lena; Raquel Martínez

Geneticalgorithms or GAs are adaptive search algorithms. These algorithms are based upon the principles of evolution and natural selection. GAs are adept at searching large, non-linear search spaces and efficiently determining near optimal solutions in reasonable time frames by simulating biological evolution. We propose a model based on neural network for predicting pest attacks. This model takes a different

Geneticalgorithms (GA's) were used to find optimal sets of parameters for an active contour model (ACM) algorithm that segments breast lesions in mammography images. These parameters, which are typically determined empirically, are used in an energy function that is minimized by the ACM algorithm when producing a segmentation contour. Using manually segmented contours supplied by experienced radiologists, GA techniques

Yuan Xu; Scott Neu; Chester J. Ornes; Janis F. Owens; Jack Sklansky; Daniel J. Valentino

A new method of geneticalgorithm (GA) optimized the extended Kalman particle filter (EKPF) is proposed in this paper. The algorithm of extended Kalman particle filter is a suboptimal filtering algorithm with good performance for target tracking and non-linear tracking problem. In the implementation of the extended Kalman particle filter, a re-sampling scheme is used to decrease the degeneracy phenomenon

A new method based on a fuzzy mutated geneticalgorithm for optimal reconfiguration of radial distribution systems (RDS) is presented. The proposed algorithm overcomes the combinatorial nature of the reconfiguration problem and deals with noncontinuous multi-objective optimization. The attractive features of the algorithm are: preservation of radial property of the network without islanding any load point by an elegant coding

GeneticAlgorithm (GA), one of the artificial intelligence algorithms, performs much better than the other algorithms for the document clustering. However, it has problem known as the premature convergence occurrence. So, Fuzzy Logic based GA (FLGA) was proposed to solve it. Nevertheless, it has still weakness such as the parameter dependence problem. In order to overcome this problem, the Multi-Objective

Taking the parameters of the Titania humidity sensor equivalent circuit model as an optimizing object, this paper proposes an optimization model to improve equivalent circuits based on geneticalgorithms. Elitist strategy is added into the selection option of the algorithms, and fitness function is suitably adjusted. Thus, the operating efficiency and accuracy of the algorithms are enhanced, and a fitting

This paper investigates the application of geneticalgorithms to PA linearization using digital pre-distortion with narrowband feedback. An algorithm is presented that adapts a polynomial pre-distortion function to minimize adjacent channel emissions. The paper describes the implementation of this algorithm and compares its performance with a random search technique. Results show nearly optimal performance in a limited number of iterations,

Multi-objective geneticalgorithms (GAs) are used for Pareto approach optimization of thermodynamic cycle of ideal turbojet engines. On this behalf, a new diversity preserving algorithm is proposed to enhance the performance of multi-objective evolutionary algorithms (MOEAs) in optimization problems with more than two objective functions. The important conflicting thermodynamic objectives that have been considered in this work are, namely, specific

K. Atashkari; N. Nariman-Zadeh; A. Pilechia; A. Jamalia

RNA inverse folding is a computational technology for designing RNA sequences which fold into a user-specified secondary structure. Although pseudoknots are functionally important motifs in RNA structures, less reports concerning the inverse folding of pseudoknotted RNAs have been done compared to those for pseudoknot-free RNA design. In this paper, we present a new version of our multi-objective geneticalgorithm (MOGA), MODENA, which we have previously proposed for pseudoknot-free RNA inverse folding. In the new version of MODENA, (i) a new crossover operator is implemented and (ii) pseudoknot prediction methods, IPknot and HotKnots, are used to evaluate the designed RNA sequences, allowing us to perform the inverse folding of pseudoknotted RNAs. The new version of MODENA with the new crossover operator was benchmarked with a dataset composed of natural pseudoknotted RNA secondary structures, and we found that MODENA can successfully design more pseudoknotted RNAs compared to the other pseudoknot design algorithm. In addition, a sequence constraint function newly implemented in the new version of MODENA was tested by designing RNA sequences which fold into the pseudoknotted structure of a hepatitis delta virus ribozyme; as a result, we successfully designed eight RNA sequences. The new version of MODENA is downloadable from http://rna.eit.hirosaki-u.ac.jp/modena/. PMID:22558001

RNA inverse folding is a computational technology for designing RNA sequences which fold into a user-specified secondary structure. Although pseudoknots are functionally important motifs in RNA structures, less reports concerning the inverse folding of pseudoknotted RNAs have been done compared to those for pseudoknot-free RNA design. In this paper, we present a new version of our multi-objective geneticalgorithm (MOGA), MODENA, which we have previously proposed for pseudoknot-free RNA inverse folding. In the new version of MODENA, (i) a new crossover operator is implemented and (ii) pseudoknot prediction methods, IPknot and HotKnots, are used to evaluate the designed RNA sequences, allowing us to perform the inverse folding of pseudoknotted RNAs. The new version of MODENA with the new crossover operator was benchmarked with a dataset composed of natural pseudoknotted RNA secondary structures, and we found that MODENA can successfully design more pseudoknotted RNAs compared to the other pseudoknot design algorithm. In addition, a sequence constraint function newly implemented in the new version of MODENA was tested by designing RNA sequences which fold into the pseudoknotted structure of a hepatitis delta virus ribozyme; as a result, we successfully designed eight RNA sequences. The new version of MODENA is downloadable from http://rna.eit.hirosaki-u.ac.jp/modena/.

In this study, the growth medium temperature in a batch bioreactor was controlled at the set point by using fuzzy model-based control method. Fuzzy control parameters which are membership functions and relation matrix were found using geneticalgorithm. Heat input given from the immersed heater and the cooling water flow rate were selected as the manipulated variables in order to control the growth medium temperature in the bioreactor. Controller performance was tested in the face of different types of input variables. To eliminate the noise on the temperature measurements, first-order filter was used in the control algorithm. The achievement of the temperature control was analyzed in terms of both microorganism concentration which was reached at the end of the stationary phase and the performance criteria of Integral of the Absolute Error. It was concluded that the cooling flow rate was suitable as manipulated variable with regard to microorganism concentration. On the other hand, performance of the controller was satisfactory when the heat input given from the immersed heater was manipulated variable. PMID:24037514

Ahio?lu, Suna; Altinten, Ayla; Ertunç, Suna; Erdo?an, Sebahat; Hapo?lu, Hale

Very thin and small (45 mm×35 mm×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 geneticalgorithm (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.

Sizing a pump stacking used in an aircraft lubrication system is a challenging task. The combination of several pumps, in parallel and in a single casing, must deliver specified oil flow rates, on a variable number of circuits, and under given flight conditions. Furthermore, the optimal assembly has to minimize overall dimensions, weight and cost. This optimization problem involves a large space search, continuous and discrete variables and multi-objectives. GeneticAlgorithms (GA)--stochastic search methods that mimic the metaphor of natural biological evolution--seem well suited to solve that kind of problems. A new GA is proposed. The efficiency of this GA is first demonstrated in solving various mathematical test-cases and then applied to the industrial problem.

We present a geneticalgorithm (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. PMID:24351274

Wu, S Q; Ji, M; Wang, C Z; Nguyen, M C; Zhao, X; Umemoto, K; Wentzcovitch, R M; Ho, K M

Storage ring lattice design is a highly constrained multiobjective optimization problem. The objectives can include lattice functions or derived quantities like emittance, brightness, or luminosity while simultaneously fulfilling constraints such as linear stability of the lattice. In this paper we explore the use of multiobjective geneticalgorithms (MOGA) to find globally optimized lattice settings in a storage ring. Using the Advanced Light Source (ALS) for illustration, three examples of MOGA are shown and analyzed—(i) using three fit parameters to optimize the straight section betatron function and the natural emittance, (ii) using three fit parameters to optimize the photon brightness of bending magnet and insertion device source points in the lattice and (iii) a six parameter fit creating alternating high and low horizontal betatron functions in subsequent straight sections while still minimizing the natural emittance. Making use of one of the main benefits of MOGA, we also study the trade-offs in the optimization objectives between sets of optimal solutions.

We describe a framework in which a geneticalgorithm (GA) and a static activated sludge (AS) treatment plant design model (WRC AS model) are used to identify low cost activated sludge designs that meet specified effluent limits (e.g. for BOD, N, and P). Once the user has chosen a particular process (Bardenpho, Biodenipho, UCT or SBR), this approach allows the parameterizations for each AS unit process to be optimized systematically and simultaneously. The approach is demonstrated for a wastewater treatment plant design problem and the GA-based performance is compared to that of a classical nonlinear optimization approach. The use of GAs for multiobjective problems such as AS design is demonstrated and their application for reliability-based design and alternative generation is discussed. PMID:12046573

Doby, T A; Loughlin, D H; de los Reyes, F L; Ducoste, J J

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 geneticalgorithm. 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.

Evolutionary computation techniques, like geneticalgorithms, 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.

Low Energy Electron Diffraction (LEED) is one of the most powerful experimental techniques for surface structure analysis but until now only a trial-and-error approach has been successful. So far, fitting procedures developed to optimize structural and nonstructural parameters—by minimization of the R-factor—have had a fairly small convergence radius, suitable only for local optimization. However, the identification of the global minimum among the several local minima is essential for complex surface structures. Global optimization methods have been applied to LEED structure determination, but they still require starting from structures that are relatively close to the correct one, in order to find the final structure. For complex systems, the number of trial structures and the resulting computation time increase so rapidly that the task of finding the correct model becomes impractical using the present methodologies. In this work we propose a new search method, based on GeneticAlgorithms, which is able to determine the correct structural model starting from completely random structures. This method—called here NGA-LEED for Novel GeneticAlgorithm for LEED—utilizes bond lengths and symmetry criteria to select reasonable trial structures before performing LEED calculations. This allows a reduction of the parameter space and, consequently of the calculation time, by several orders of magnitude. A refinement of the parameters by least squares fit of simulated annealing is performed only at some intermediate stages and in the final step. The method was successfully tested for two systems, Ag(1?1?1)(4 × 4)-O and Au(1?1?0)-(1 × 2), both in theory versus theory and in theory versus experiment comparisons. Details of the implementation as well as the results for these two systems are presented.

Viana, M. L.; dos Reis, D. D.; Soares, E. A.; Van Hove, M. A.; Moritz, W.; de Carvalho, V. E.

As an effective and noninvasive therapeutic modality for tumor treatment, high-intensity focused ultrasound (HIFU) has attracted attention from both physicians and patients. New generations of HIFU systems with the ability to electrically steer the HIFU focus using phased array transducers have been under development. The presence of side and grating lobes may cause undesired thermal accumulation at the interface of the coupling medium (i.e. water) and skin, or in the intervening tissue. Although sparse randomly distributed piston elements could reduce the amplitude of grating lobes, there are theoretically no grating lobes with the use of concave elements in the new phased array HIFU. A new HIFU transmission strategy is proposed in this study, firing a number of but not all elements for a certain period and then changing to another group for the next firing sequence. The advantages are: 1) the asymmetric position of active elements may reduce the side lobes, and 2) each element has some resting time during the entire HIFU ablation (up to several hours for some clinical applications) so that the decreasing efficiency of the transducer due to thermal accumulation is minimized. Geneticalgorithm was used for selecting randomly distributed elements in a HIFU array. Amplitudes of the first side lobes at the focal plane were used as the fitness value in the optimization. Overall, it is suggested that the proposed new strategy could reduce the side lobe and the consequent side-effects, and the geneticalgorithm is effective in selecting those randomly distributed elements in a HIFU array.

Low Energy Electron Diffraction (LEED) is one of the most powerful experimental techniques for surface structure analysis but until now only a trial-and-error approach has been successful. So far, fitting procedures developed to optimize structural and nonstructural parameters-by minimization of the R-factor-have had a fairly small convergence radius, suitable only for local optimization. However, the identification of the global minimum among the several local minima is essential for complex surface structures. Global optimization methods have been applied to LEED structure determination, but they still require starting from structures that are relatively close to the correct one, in order to find the final structure. For complex systems, the number of trial structures and the resulting computation time increase so rapidly that the task of finding the correct model becomes impractical using the present methodologies. In this work we propose a new search method, based on GeneticAlgorithms, which is able to determine the correct structural model starting from completely random structures. This method-called here NGA-LEED for Novel GeneticAlgorithm for LEED-utilizes bond lengths and symmetry criteria to select reasonable trial structures before performing LEED calculations. This allows a reduction of the parameter space and, consequently of the calculation time, by several orders of magnitude. A refinement of the parameters by least squares fit of simulated annealing is performed only at some intermediate stages and in the final step. The method was successfully tested for two systems, Ag(1?1?1)(4 × 4)-O and Au(1?1?0)-(1 × 2), both in theory versus theory and in theory versus experiment comparisons. Details of the implementation as well as the results for these two systems are presented. PMID:24824047

Viana, M L; dos Reis, D D; Soares, E A; Van Hove, M A; Moritz, W; de Carvalho, V E

A propeller performance analysis program has been developed and integrated into a GeneticAlgorithm for design optimization. The design tool will produce optimal propeller geometries for a given goal, which includes performance and/or acoustic signature. A vortex lattice model is used for the propeller performance analysis and a subsonic compact source model is used for the acoustic signature determination. Compressibility effects are taken into account with the implementation of Prandtl-Glauert domain stretching. Viscous effects are considered with a simple Reynolds number based model to account for the effects of viscosity in the spanwise direction. An empirical flow separation model developed from experimental lift and drag coefficient data of a NACA 0012 airfoil is included. The propeller geometry is generated using a recently introduced Class/Shape function methodology to allow for efficient use of a wide design space. Optimizing the angle of attack, the chord, the sweep and the local airfoil sections, produced blades with favorable tradeoffs between single and multiple point optimizations of propeller performance and acoustic noise signatures. Optimizations using a binary encoded IMPROVE(c) GeneticAlgorithm (GA) and a real encoded GA were obtained after optimization runs with some premature convergence. The newly developed real encoded GA was used to obtain the majority of the results which produced generally better convergence characteristics when compared to the binary encoded GA. The optimization trade-offs show that single point optimized propellers have favorable performance, but circulation distributions were less smooth when compared to dual point or multiobjective optimizations. Some of the single point optimizations generated propellers with proplets which show a loading shift to the blade tip region. When noise is included into the objective functions some propellers indicate a circulation shift to the inboard sections of the propeller as well as a reduction in propeller diameter. In addition the propeller number was increased in some optimizations to reduce the acoustic blade signature.

This paper continues our investigation of genetic pre-distortion algorithms to power amplifier (PA) linearization. In previous work, we reported simulation results of an adaptive algorithm that requires only a measure of out-of-band emission. Compared to traditional algorithms that require wideband feedback, the proposed algorithm is implemented using narrowband feedback, affording a large cost savings in ADC components. In our current

In this paper we perform a comparison among FSS–EBNA, a randomized, population-based and evolutionary algorithm, and two genetic and other two sequential search approaches in the well-known feature subset selection (FSS) problem. In FSS–EBNA, the FSS problem, stated as a search problem, uses the estimation of Bayesian network algorithm (EBNA) search engine, an algorithm within the estimation of distribution algorithm

Combining the ability of apperception and counteractive to environment of agent with search method of geneticalgorithm, an improved multi-agent geneticalgorithm (MAGA) is advanced. It ensures diversity of population and improves local search ability of geneticalgorithm by simulating competition, cooperate and self-study of different agents using neighboring cross operator, aberrance operator and self-learning operator of agent. The algorithm is applied to the optimal planning for the waste treatment system of Urumqi, Xinjiang. Results demonstrate an improved performance in finding the global minimum when water quality requirements have been fulfilled. The result demonstrates nicer performance and factual value of MAGA.

A new method for aerodynamic shape optimization using a geneticalgorithm 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 geneticalgorithm is easy to implement and extremely reliable, being relatively insensitive to design space noise.

Solving optimization problems with multiple objectives under uncertainty is generally a very difficult task. Evolutionary algorithms, particularly geneticalgorithms, have shown to be effective in solving this type of complex problems. In this paper, we develop a simulation-based multi-objective geneticalgorithm (SMOGA) procedure to solve the build-operate-transfer (BOT) network design problem with multiple objectives under demand uncertainty. The SMOGA procedure

A kind of improve geneticalgorithm for identifying multi-variables nonlinear boiler model of 300 MW power unit is introduced. In the algorithm, floating-point coding, rank-based selection, elitist reservation and grouping method are used, and the premature convergence is restrained, and the searching ability is improved. The geneticalgorithm-based model identification MATLAB program is designed and the model parameters can be

Geneticalgorithms (GA) are applied for the optimization of the structure of metallic clusters by the calculation of the ground-state energies from a tight-binding (Hückel) Hamiltonian. The optimum topology or graph is searched by the use of the adjacency matrix Aij as a natural coding. The initial populations for N-atom clusters are generated from a representative group of fit cluster structures having N-1 atoms by the addition of random connections or hoppings between the Nth atom and the rest of the cluster atoms (AiN=0 or 1). The diversity of geometries is enlarged by 20% with fully random structures. Several crossover strategies are proposed for the genetic evolution that combine the ``parent'' clusters while trying to preserve or transmit the physical characteristics of the parents' topologies. The performance of the different procedures is tested. For N<=13, the present GA yield topological structures that are in agreement with previous geometry optimizations performed using an enumerative search (N<=9) or simulated annealing Monte Carlo (10<=N<=13) methods. Limitations and extensions for N>=14 are discussed.

Economic load dispatch is one of the optimization problems in power systems. This paper presents an improved geneticalgorithm for economic load dispatch with valve-point loadings. New crossover and mutation operations are introduced. The solutions of the economic load dispatch with valve-point loadings under three cases are solved by the improved geneticalgorithm. Test results are given and compared with

Based on the movement model of a waterjet propelled craft, an autopilot control system with adjustable control parameters for a waterjet propelled craft is designed by fuzzy control and geneticalgorithms. The normal fuzzy controller is improved by the modified factor and the correction, and uses geneticalgorithms to optimize all parameters to get the global solution. The controller can

In this paper, the resource-constrained project scheduling problem with multiple execution modes for each activity is explored. This paper aims to find a schedule of activities such that the makespan of the schedule is minimized subject to the precedence and resource constraints. We present a two-phase genetic local search algorithm that combines the geneticalgorithm and the local search method

In this paper an accurate method is presented for determining of the device sizes in a RF circuit based on geneticalgorithm (GA). HSPICE RF simulation is used for evaluating of the fitness of the circuit specifications per every iteration of the GA. Also an example for a LNA is presented for evaluating of non-dominated sorting geneticalgorithm (NSGA-II) as

Mojtaba Behzad Fallahpour; Kamran Delfan Hemmati; Ali Pourmohammad; Abbas Golmakani

A parallel geneticalgorithm (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 geneticalgorithm 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

Brian R. Jones; William A. Crossley; Anastasios S. Lyrintzis

Search space is an infinite set in decimal geneticalgorithms (GA). In optimization problems where decimal geneticalgorithms are used, the stop criteria such as the maximum number of iterations or cost function having no more difference than a specified value may not guarantee that the computed solution is equal to the real solution. The proposed stop criteria has been

This paper presents a geneticalgorithmic approach to the shortest path (SP) routing problem. Variable-length chromosomes (strings) and their genes (parameters) have been used for encoding the problem. The crossover operation exchanges partial chromosomes (partial routes) at positionally independent crossing sites and the mutation operation maintains the genetic diversity of the population. The proposed algorithm can cure all the infeasible

This paper describes an initial study into the optimisation of the shape of a wave energy collector using a geneticalgorithm. The study investigates three descriptions of the surface geometry of a surging device, which are both compatible with panel-based hydrodynamic analysis software and form a suitable representation in the geneticalgorithm. The analysis has been simplified by considering only

This paper presents a performance study of a parallel, coarse-grained, multiple-deme GeneticAlgorithm (GA) with adaptive mutation. The effect of varying migration period and number of subpopulations upon the GA is evaluated. Using common unimodal and multimodal objective functions, this study measures the convergence velocity and solution quality for the proposed geneticalgorithm. In this paper, we briefly survey previous

GeneticAlgorithms that are used for learning as one way to control the combinational explosion associated with the generation of new rules are discussed. The GeneticAlgorithm approach tends to work best when it can be applied to a domain independent knowledge representation. Applications to real time control in space systems are discussed.

The shortcoming of the standard geneticalgorithm is analysed. An improved geneticalgorithm with modified mutation operator and adaptive probabilities of crossover and mutation is proposed. Simulation experiments have been carried and the results show that the modifications are very effective. In this paper, a planning method of cast for steelmaking continuous casting production scheduling in CIMS is also studied.

Four compounds within a set of ligands for the benzodiazepine receptors are characterized by their electron density maps at different resolution levels and reconstructed from calculated structure factors. The resulting complex three-dimensional density maps are first simplified into connected graphs using topological analysis. Then, an original geneticalgorithm method, GAGS (GeneticAlgorithm for Graph Similarity search), is developed and implemented

In the geneticalgorithm (GA), maintenance of the diversity of the population is an important issue to enhance its optimization and adaptation ability. The authors have proposed the thermodynamical geneticalgorithm (TDGA), which can maintain the diversity explicitly and systematically by evaluating the entropy and the free energy of the population. In adaptation to changing environment, the maintenance of the

This study proposes an improved geneticalgorithm (GA) to derive solutions for facility layouts that are to have inner walls and passages. The proposed algorithm models the layout of facilities on gene structures. These gene structures consist of a four-segmented chromosome. Improved solutions are produced by employing genetic operations known as selection, crossover, inversion, mutation, and refinement of these genes

This study deals with exergy estimation of petroleum using geneticalgorithm (GA) approach. The exergy estimation is carried out based on the gross domestic product (GDP) and the percentage of vehicle ownership figures in Turkey. GeneticAlgorithm EXergy Production and Consumption (GAPEX) is developed. During the estimation of petroleum exergy, the GA is combined with time-series approach. For exergy consumption,

Harun Kemal Ozturk; Halim Ceylan; Arif Hepbasli; Zafer Utlu

This investigation developed a MATLAB program, based on geneticalgorithms that generated an optimal (shortest distance) path plan for a mobile robot to visit all of the specified waypoints without colliding with the known obstacles. The designed geneticalgorithm path planner was shown to accomplish this task and produce superior results when compared against a full search path planner. Next,

In this paper, a novel geneticalgorithm application is proposed for adaptive power and subcarrier allocation in multi-user Orthogonal Frequency Division Multiplexing (OFDM) systems. To test the application, a simple geneticalgorithm was implemented in MATLAB language. With the goal of minimizing the overall transmit power while ensuring the fulfillment of each user's rate and bit error rate (BER) requirements,

The geneticalgorithm can be applied to selecting theoretical probability distributions so as to be rep- resentative for observed data. Two aspects are con- sidered here: Using the geneticalgorithm, one can decide which one of some dierent families of prob- ability distributions is best suited, and parameters can be estimated.

In this paper, an intelligent controller of the DC (Direct current) Motor drive is designed using fuzzy logic-geneticalgorithms optimization. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are improved by geneticalgorithms optimization model. Computer MATLAB work space demonstrate that

Geneticalgorithms (GA) are designed to search for, discover and emphasize good solutions by applying selection and crossover techniques, inspired by nature, to supply solutions to engineering systems. Geneticalgorithms operate on pieces of information like nature does on genes in the course of evolution. All individuals of one generation are evaluated by a fitness function. This paper presents an

Linda Murphy; Hoda S. Abdel-Aty-Zohdy; M. Hashem-Sherif

The method of using geneticalgorithm to optimize a fuzzy neural network (FNN) has been introduced in many papers, but most of these articles adopt different methods. This paper identifies the parameters of the controlled plant using a neural network; then it modifies the parameters with the geneticalgorithm for optimization. Finally, it modulates and optimizes the membership function and

This study describes the use of geneticalgorithms (GAs) for operating standard HVAC systems (HVAC—heating, ventilation and air conditioning) in order to optimize performance, primarily with regard to power saving. Geneticalgorithms were introduced as an instrument for solving optimization problems. Analytic optimization procedures are widely used in other fields of engineering, but they are difficult to operate within HVAC

Stepped-Frequency waveform (SFW) is a very important type of high range resolution radar signal. A novel processing method for SFW based on geneticalgorithm and CLEAN technique is proposed in this paper. The parameters (i.e., position, amplitude and velocity) of a point scatterer can be found by global searching with geneticalgorithm. And an iterative CLEAN processing is used to

This paper presents an approach for the design of fuzzy logic power system stabilizers using geneticalgorithms. In the proposed fuzzy expert system, speed deviation and its derivative have been selected as fuzzy inputs. In this approach the parameters of the fuzzy logic controllers have been tuned using geneticalgorithm. Incorporation of GA in the design of fuzzy logic power

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 GeneticAlgorithm. The intensity estimation technique SIEGA (Storm Intensity Estimation using GeneticAlgorithm) uses only 9 simple statistical variables based on TMI observations and does not require any subjective input except the center of

C. M. Kishtawal; Falguni Patadia; Randhir Singh; Sujit Basu; M. S. Narayanan; P. C. Joshi

In an effort to optimize river-flow training structures, a study is undertaken to explore the utility of geneticalgorithms. The study includes the development of a numerical procedure for optimization of a two-dimensional hydrofoil; the optimization of shape is performed using a geneticalgorithm. A formula utilizing two Bezier splines for the construction of the foil shape is introduced. The

The design and optimization of a coupled multicomponent distillation system is a non-linear and mul- tivariable problem. The complexity of this kind of problem results in high solving difficulty. This paper addresses the application of geneticalgorithms to the optimization of intensified distillation systems for quaternary distillations. We used a multiobjective geneticalgorithm with restrictions coupled to the Aspen Plus

A geneticalgorithm is used to design a monopole loaded with a modified folded dipole so that it radiates uniform power over the hemisphere. Each of the wires that make up the antenna are given a range of possible lengths. The geneticalgorithm randomly selects a sample population of possible antenna configurations from the total population of all configurations. The

In this paper subcarrier and power allocation to each user at base-station maximizes the user data rates, subject to constraints on total power and bit error rate. First, each subchannel is assigned to the user with best channel-to-noise ratio for the channel, with random power distributed by water filling algorithm. The Gao's (2006) subcarrier allocation algorithm was used to calculate

Yenumula B. Reddy; Nandigam Gajendar; Portia Taylor; Damian Madden

The enormous variety of substances which may be added to forage in order to manipulate and improve the ensilage process presents an empirical, combinatorial optimization problem of great complexity. To investigate the utility of geneticalgorithms for designing effective silage additive combinations, a series of small-scale proof of principle silage experiments were performed with fresh ryegrass. Having established that significant biochemical changes occur over an ensilage period as short as 2 days, we performed a series of experiments in which we used 50 silage additive combinations (prepared by using eight bacterial and other additives, each of which was added at six different levels, including zero [i.e., no additive]). The decrease in pH, the increase in lactate concentration, and the free amino acid concentration were measured after 2 days and used to calculate a “fitness” value that indicated the quality of the silage (compared to a control silage made without additives). This analysis also included a “cost” element to account for different total additive levels. In the initial experiment additive levels were selected randomly, but subsequently a geneticalgorithm program was used to suggest new additive combinations based on the fitness values determined in the preceding experiments. The result was very efficient selection for silages in which large decreases in pH and high levels of lactate occurred along with low levels of free amino acids. During the series of five experiments, each of which comprised 50 treatments, there was a steady increase in the amount of lactate that accumulated; the best treatment combination was that used in the last experiment, which produced 4.6 times more lactate than the untreated silage. The additive combinations that were found to yield the highest fitness values in the final (fifth) experiment were assessed to determine a range of biochemical and microbiological quality parameters during full-term silage fermentation. We found that these combinations compared favorably both with uninoculated silage and with a commercial silage additive. The evolutionary computing methods described here are a convenient and efficient approach for designing silage additives.

Davies, Zoe S.; Gilbert, Richard J.; Merry, Roger J.; Kell, Douglas B.; Theodorou, Michael K.; Griffith, Gareth W.

We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a geneticalgorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography. Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms.

Page, Andrew J.; Keane, Thomas M.; Naughton, Thomas J.

A novel hybrid cerebellar model articulation controller (CMAC) and online adaptive geneticalgorithm (GA) controller is introduced to control two Brushless DC motor (BLDCM) which applied in a biped robot. GeneticAlgorithm simulates the random learning among the individuals of a group, and CMAC simulates the self-learning of an individual. To validate the ability and superiority of the novel algorithm, experiments have been done in MATLAB/SIMULINK. Analysis among GA, hybrid GA-CMAC and CMAC feed-forward control is also given. The results prove that the torque ripple of the coordinated control system is eliminated by using the hybrid GA-CMAC algorithm.

Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. geneticalgorithms). The focus of this paper is on optimizing the design of geneticalgorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a geneticalgorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the geneticalgorithm to find the global optimal solution, performing significantly better than other search methods, including geneticalgorithms that implement fixed mutation rates.

In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures. PMID:24718686

Real-time evolvable systems are possible with a hardware implementation of GeneticAlgorithms (GA). We report the design of an IP core that implements a general purpose GA engine which has been successfully synthesized and verified on a Xilinx Virtex II Pro FPGA Device (XC2VP30). The placed and routed IP core has an area utilization of only 13% and clock speed of 50MHz. The GA core can be customized in terms of the population size, number of generations, cross-over and mutation rates, and the random number generator seed. The GA engine can be tailored to a given application by interfacing with the application specific fitness evaluation module as well as the required storage memory (to store the current and new populations). The core is soft in nature i.e., a gate-level netlist is provided which can be readily integrated with the user's system. The GA IP core can be readily used in FPGA based platforms for space and military applications (for e.g., surveillance, target tracking). The main advantages of the IP core are its programmability, small footprint, and low power consumption. Examples of concept systems in sensing and surveillance domains will be presented.

In this study, an optimization problem on the robot arm machining is formulated and solved by using geneticalgorithms (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.

Moving Target (MT) environments for computer systems provide security through diversity by changing various system properties that are explicitly defined in the computer configuration. Temporal diversity can be achieved by making periodic configuration changes; however in an infrastructure of multiple similarly purposed computers diversity must also be spatial, ensuring multiple computers do not simultaneously share the same configuration and potential vulnerabilities. Given the number of possible changes and their potential interdependencies discovering computer configurations that are secure, functional, and diverse is challenging. This paper describes how a GeneticAlgorithm (GA) can be employed to find temporally and spatially diverse secure computer configurations. In the proposed approach a computer configuration is modeled as a chromosome, where an individual configuration setting is a trait or allele. The GA operates by combining multiple chromosomes (configurations) which are tested for feasibility and ranked based on performance which will be measured as resistance to attack. The result of successive iterations of the GA are secure configurations that are diverse due to the crossover and mutation processes. Simulations results will demonstrate this approach can provide at MT environment for a large infrastructure of similarly purposed computers by discovering temporally and spatially diverse secure configurations.

Mott et al. [1] describe the automatic design of optimal microfluidic components based on performance criteria. The approach constructs a complex component by adding geometric features, such a grooves of various shapes, to a microchannel. The net transport produced by each of these features in isolation was pre-computed and stored as an ``advection map'' for that feature, and the complex flow through a composite geometry that combines these basic features was calculated rapidly by applying the corresponding maps in sequence. An exhaustive search of feature combinations produced optimized mixer designs of moderate size and complexity. In the current work, a geneticalgorithm replaces the exhaustive search of Ref. [1], enabling the optimization of much more complex components with far more degrees of freedom. New metrics for characterizing surface delivery and sample dispersion (i.e., the spreading of a sample plug within the pressure-driven flow) are developed, and the software is applied to design new components that optimize surface delivery and that minimize sample dispersion. [1]. Mott, D.R., Howell, P.B, Golden, J.P., Kaplan, C.R., Ligler, F.S., and Oran, E.S., Lab on a Chip, Vol. 6, No. 4, 2006, pp. 540-549.

Mott, David; Obenschain, Keith; Howell, Peter; Golden, Joel

Using geneticalgorithm (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.

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 geneticalgorithms, 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.

Diveux, T.; Sebastian, P.; Bernard, D.; Puiggali, J. R.; Grandidier, J. Y.

Stereolithography is the most popular RP process in which intricate models are directly constructed from a CAD package by polymerizing a plastic monomer. The application range is still limited, because dimensional accuracy is still inferior to that of conventional machining process. The ultimate dimensional accuracy of a part built on a layer-by-layer basis depends on shrinkage which depend on many factors such as layer thickness, hatch spacing, hatch style, hatch over cure and fill cure depth. The influence of the above factors on shrinkage in X and Y directions fit to the nonlinear pattern. A particular combination of process variables that would result same shrinkage rate in both directions would enable to predict shrinkage allowance to be provided on a part and hence the CAD model could be constructed including shrinkage allowance. In this concern, the objective of the present work is set as determination of process parameters to have same shrinkage rate in both X and Y directions. A geneticalgorithm (GA) is proposed to find optimal process parameters for the above objective. This approach is an analytical approach with experimental sample data and has great potential to predict process parameters for better dimensional accuracy in stereolithography process.

Quantum mechanical simulations of carrier transport in Si require an accurate model of the complicated Si bandstructure. Tight-binding models are an attractive method of choice since they bear the full electronic structure symmetry within them and can discretize a realistic device on an atomic scale. However, tight-binding models are not simple to parameterize and characterize. This work addresses two issues: (1) the need for an automated fitting procedure that maps tight-binding orbital interaction energies to physical observables such as effective masses and band edges, and (2) the capabilities and accuracy of the nearest and second-nearest neighbor tight-binding sp3s* models with respect to carrier transport in indirect bandgap materials. A geneticalgorithm approach is used to fit orbital interaction energies of these tight-binding models in a nine- and 20-dimensional global optimization problem for Si. A second-nearest neighbor sp3s* parameter set that fits all relevant conduction and valence band properties with a high degree of accuracy is presented. No such global fit was found for the nearest neighbor sp3s* model and two sets, one heavily weighed for electron properties and the other for hole properties, are presented. Bandstructure properties relevant for electron and hole transport in Si derived from these three sets are compared with the seminal Vogl et al. [Journal of the Physics and Chemistry of Solids 44, 365 (1983)] parameters.

Klimeck, Gerhard; Bowen, R. Chris; Boykin, Timothy B.; Salazar-Lazaro, Carlos; Cwik, Thomas A.; Stoica, Adrian

The fetal heart rate (FHR) is monitored on a paper strip (cardiotocogram) during labour to assess fetal health. If necessary, clinicians can intervene and assist with a prompt delivery of the baby. Data-driven computerized FHR analysis could help clinicians in the decision-making process. However, selecting the best computerized FHR features that relate to labour outcome is a pressing research problem. The objective of this study is to apply geneticalgorithms (GA) as a feature selection method to select the best feature subset from 64 FHR features and to integrate these best features to recognize unfavourable FHR patterns. The GA was trained on 404 cases and tested on 106 cases (both balanced datasets) using three classifiers, respectively. Regularization methods and backward selection were used to optimize the GA. Reasonable classification performance is shown on the testing set for the best feature subset (Cohen's kappa values of 0.45 to 0.49 using different classifiers). This is, to our knowledge, the first time that a feature selection method for FHR analysis has been developed on a database of this size. This study indicates that different FHR features, when integrated, can show good performance in predicting labour outcome. It also gives the importance of each feature, which will be a valuable reference point for further studies. PMID:24854596

Xu, Liang; Redman, Christopher W G; Payne, Stephen J; Georgieva, Antoniya

Automation of DC photoinjector designs using a geneticalgorithm (GA) based optimization is an accepted practice in accelerator physics. Allowing the gun cavity field profile shape to be varied can extend the utility of this optimization methodology to superconducting and normal conducting radio frequency (SRF/RF) gun based injectors. Finding optimal field and cavity geometry configurations can provide guidance for cavity design choices and verify existing designs. We have considered two approaches for varying the electric field profile. The first is to determine the optimal field profile shape that should be used independent of the cavity geometry, and the other is to vary the geometry of the gun cavity structure to produce an optimal field profile. The first method can provide a theoretical optimal and can illuminate where possible gains can be made in field shaping. The second method can produce more realistically achievable designs that can be compared to existing designs. In this paper, we discuss the design and implementation for these two methods for generating field profiles for SRF/RF guns in a GA based injector optimization scheme and provide preliminary results.

Geneticalgorithms (GA) are optimization techniques that are based on the mechanics of evolution and natural selection. They take advantage of the power of cumulative selection, in which successive incremental improvements in a solution structure become the basis for continued development. A GA is an iterative procedure that maintains a 'population' of 'organisms' (candidate solutions). Through successive 'generations' (iterations) the population as a whole improves in simulation of Darwin's 'survival of the fittest'. GA's have been shown to be successful where noise significantly reduces the ability of other search techniques to work effectively. Satellite altimetry provides useful information about oceanographic phenomena. It provides rapid global coverage of the oceans and is not as severely hampered by cloud cover as infrared imagery. Despite these and other benefits, several factors lead to significant difficulty in interpretation. The GA approach to the improved interpretation of satellite data involves the representation of the ocean surface model as a string of parameters or coefficients from the model. The GA searches in parallel, a population of such representations (organisms) to obtain the individual that is best suited to 'survive', that is, the fittest as measured with respect to some 'fitness' function. The fittest organism is the one that best represents the ocean surface model with respect to the altimeter data.

Pathogens resistant to available drug therapies are a pressing global health problem. Short, cationic peptides represent a novel class of agents that have lower rates of drug resistance than derivatives of current antibiotics. Previously, we created a software system utilizing artificial neural networks that were trained on quantitative structure-activity relationship descriptors calculated for a total of 1400 synthetic peptides for which antibacterial activity was determined. Using the trained system, we correctly identified additional peptides with activity of 94% accuracy; active peptides were 47 of the top rated 50 peptides chosen from an in silico library of nearly 100,000 sequences. Here, we report a method of generating candidate peptide sequences using the heuristic evolutionary programming method of geneticalgorithms (GA), which provided a large (19-fold) improvement in identification of novel antibacterial peptides. Approximately 0.50% of peptides evaluated during the GA method were classified as highly active, while only 0.026% of the nearly 100,000 sequences we previously screened were classified as highly active. A selection of these peptides was tested in vitro and activities reported here. While GA significantly improves the possibility of identifying candidate peptides, we encountered important pitfalls to this method that should be considered when using GA. PMID:20942839

Fjell, Christopher D; Jenssen, Håvard; Cheung, Warren A; Hancock, Robert E W; Cherkasov, Artem

An improved geneticalgorithm (GA) formulation for pipe network optimization has been developed. The new GA uses variable power scaling of the fitness function. The exponent introduced into the fitness function is increased in magnitude as the GA computer run proceeds. In addition to the more commonly used bitwise mutation operator, an adjacency or creeping mutation operator is introduced. Finally, Gray codes rather than binary codes are used to represent the set of decision variables which make up the pipe network design. Results are presented comparing the performance of the traditional or simple GA formulation and the improved GA formulation for the New York City tunnels problem. The case study results indicate the improved GA performs significantly better than the simple GA. In addition, the improved GA performs better than previously used traditional optimization methods such as linear, dynamic, and nonlinear programming methods and an enumerative search method. The improved GA found a solution for the New York tunnels problem which is the lowest-cost feasible discrete size solution yet presented in the literature.

Dandy, Graeme C.; Simpson, Angus R.; Murphy, Laurence J.

The geneticalgorithm (GA) is a relatively new technique that implements the principles nature uses in biological evolution in order to optimize a multidimensional nonlinear problem. The GA works especially well for problems with a large number of local extrema, where traditional methods (such as conjugate gradient, steepest descent, and others) fail or, at best, underperform. The field of accelerator physics, among others, abounds with problems which lend themselves to optimization via GAs. In this paper, we report on the successful application of GAs in several problems related to the existing CEBAF facility, the proposed MEIC at Jefferson Lab, and a radio frequency (RF) gun based injector. These encouraging results are a step forward in optimizing accelerator design and provide an impetus for application of GAs to other problems in the field. To that end, we discuss the details of the GAs used, including a newly devised enhancement, which leads to improved convergence to the optimum and make recommendations for future GA developments and accelerator applications.

Hofler, Alicia [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States); Terzic, Balsa [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States) and Old Dominion University, Norfolk, VA (United States); Kramer, Matthew [University of California, Berkeley, CA (United States); Zvezdin, Anton [Stony Brook University, Stony Brook, NY (United States); Morozov, Vasiliy [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States); Roblin, Yves [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States); Lin, Fanglei [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States); Jarvis, Colin [Macalester College, Saint Paul, MN (United States)

The novel application of the geneticalgorithm (GA) for parameter identification of hydraulically actuated manipulators is currently under investigation. One common characteristic of such systems is high hydraulic compliance, which has considerable effect on the behavior of the system. Identification of hydraulic compliance is beneficial in the following ways: (1) it can be used towards developing an exact model of the system; (2) it can be incorporated into a fault/hazard diagnosis system; and (3) it can be used for improving control strategies. In this paper, the GA is applied to identify the hydrauic compliance. This is demonstrated with the basic hydraulic circuit used in heavy-duty manipulation. The ability of the GA to handle nonlinear functions, finding the global solution, as well as using accumulated information to prune the search space, makes it a good candidate for use in the identification of hydraulic system parameters. It was shown that it can successfully identify the hydraulic compliance; this was demonstrated with the basic hydraulic actuation used in heavy-duty manipulation.

Wan, F.; Sepehri, N.; Kristinsson, K.; Dumont, G. A. M.; Lawrence, P. D.

The industrial use of various kinds of rubber-like (hyper-elastic) material is rapidly increasing and growing in importance, especially in automobiles, trains, and machinery(1). In the past, rubber engineers and designers have predicted the behavior of rubber-like materials using analytic methods for limited problems or approximate methods for general problems. Yet, with the progress of digital computers, finite element methods(2), represented by the Mooney-Rivlin model, are now widely used to analyze hyper-elastic as well as isotropic materials. The conventional method used to evaluate the properties of rubber-like materials is the least square method (LSM), however, this method has a low precision and involves a tedious pre-solving process. Accordingly, this study proposes a simple yet powerful method for estimating the properties of rubber-like materials using a successive zooming geneticalgorithm (SZGA). The proposed method results in dependable and precise rubber-like properties for various Mooney-Rivlin models based on simply changing the objective function. To demonstrate the effectiveness of the proposed method, it is compared with Haines & Wilson's method (LSM) and other commercial packages.

Kwon, Young-Doo; Kwon, Hyun-Wook; Kim, Wha-Jung; Yeo, Sim-Dong

This article presents the application of the geneticalgorithm to the optimum detailed design of reinforced concrete frames based on Indian Standard specifications. The objective function is the total cost of the frame which includes the cost of concrete, formwork and reinforcing steel for individual members of the frame. In order for the optimum design to be directly constructible without any further modifications, aspects such as available standard reinforcement bar diameters, spacing requirements of reinforcing bars, modular sizes of members, architectural requirements on member sizes and other practical requirements in addition to relevant codal provisions are incorporated into the optimum design model. The produced optimum design satisfies the strength, serviceability, ductility, durability and other constraints related to good design and detailing practice. The detailing of reinforcements in the beam members is carried out as a sub-level optimization problem. This strategy helps to reduce the size of the optimization problem and saves computational time. The proposed method is demonstrated through several example problems and the optimum results obtained are compared with those in the available literature. It is concluded that the proposed optimum design model can be adopted in design offices as it yields rational, reliable, economical, time-saving and practical designs.

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.

The application of domain decomposition geneticalgorithms to the design of frequency selective surfaces (FSSs) is discussed. The analysis of FSS screens is briefly reviewed, along with a method of accelerating their characterization with a rational Krylov model order reduction technique. Using this technique, a hybrid geneticalgorithm (which is a type of domain decomposition geneticalgorithm that incorporates a

The capabilities of geneticalgorithms as a non-calculus based, global search method make them potentially useful in the conceptual design of rotor systems. Coupling reasonably simple analysis tools to the geneticalgorithm was accomplished, and the resulting program was used to generate designs for rotor systems to match requirements similar to those of both an existing helicopter and a proposed helicopter design. This provides a comparison with the existing design and also provides insight into the potential of geneticalgorithms in design of new rotors.

Crossley, William A.; Wells, Valana L.; Laananen, David H.

Optimizing complex engineering problems may demand large computational efforts because of the use of numerical models. Global optimization can be established through the use of evolutionary algorithms, but may demand a prohibitive amount of computational time. In order to reduce the computational time, we incorporate in the global optimization procedures a physics-based fast coarse model. This paper presents a two-level

Guillaume Crevecoeur; Peter Sergeant; Luc Dupre; Rik Van de Walle

As a commonly used technique in data preprocessing, feature selection selects a subset of informa- tive attributes or variables to build models describing data. By removing redundant and irrelevant or noise features, feature selection can improve the predictive accuracy and the com- prehensibility of the predictors or classifiers. Many feature selection algorithms with different selection criteria has been introduced by

Feng Tan; Xuezheng Fu; Yanqing Zhang; Anu G. Bourgeois