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1

Genetic Algorithms  

Microsoft Academic Search

In this chapter we describe the basics of Genetic Algorithms and how they can be used to train Artificial Neural Networks.\\u000a Supervised training of Multilayer Perceptrons for classification problems is considered. We also explain how the Genetic Algorithm\\u000a can be hybridized with other algorithms and present two hybrids between it and two classical algorithms for the neural network\\u000a training: Backpropagation

Enrique Alba; Francisco Chicano

2

Genetic Algorithms  

Microsoft Academic Search

\\u000a Genetic algorithms (GAs) have become popular as a means of solving hard combinatorial optimization problems. The first part\\u000a of this chapter briefly traces their history, explains the basic concepts and discusses some of their theoretical aspects.\\u000a It also references a number of sources for further research into their applications. The second part concentrates on the detailed\\u000a implementation of a GA.

Colin R. Reeves

3

Augmented Compact Genetic Algorithm  

Microsoft Academic Search

\\u000a An augmented compact genetic algorithm (acGA) is presented in this paper. It exhibits all the desirable characteristics of compact genetic algorithm (cGA). While\\u000a the selection strategy of cGA is similar to (steady-state) tournament selection with replacement (TSR), the proposed algorithm\\u000a employs a strategy akin to tournament selection without replacement (TS\\/R). The latter is a common feature of genetic algorithms\\u000a (GAs)

Chang Wook Ahn; Rudrapatna S. Ramakrishna

2003-01-01

4

Where Genetic Algorithms Excel  

Microsoft Academic Search

We analyze the performance of a genetic algorithm (GA) we call Culling, and a variety of other algorithms, on a problem we refer to as the Additive Search Problem (ASP). We show that the problem of learning the Ising perceptron is reducible to a noisy version of ASP. Noisy ASP is the first problem we are aware of where a

Eric B. Baum; Dan Boneh; Charles Garrett

2001-01-01

5

Exam timetabling using genetic algorithm  

Microsoft Academic Search

In this paper we present a case study concerning the exam timetabling problem we faced, and its genetic algorithm based solution. Several variations of the algorithm as well as the influence of algorithm parameters are analyzed.

M. Cuupic; Marin Golub; Domagoj Jakobovic

2009-01-01

6

The compact genetic algorithm  

Microsoft Academic Search

This paper introduces the compact genetic algorithm (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

1999-01-01

7

The compact genetic algorithm  

Microsoft Academic Search

This paper introduces the “compact genetic algorithm” (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

G. R. Harik; Fernando G. Lobo; D. E. Goldberg

1998-01-01

8

Genetic algorithms for numerical optimization  

Microsoft Academic Search

Genetic algorithms (GAs) are stochastic adaptive algorithms whose search method is based on simulation of natural genetic inheritance and Darwinian striving for survival. They can be used to find approximate solutions to numerical optimization problems in cases where finding the exact optimum is prohibitively expensive, or where no algorithm is known. However, such applications can encounter problems that sometimes delay,

Zbigniew Michalewicz; Cezary Z. Janikow

1991-01-01

9

Process scheduling using genetic algorithms  

Microsoft Academic Search

This paper presents a genetic algorithm using a matrix genome encoding to schedule distributed tasks, represented by a directed acyclic graph, on processors in order to minimize the maximum task finishing time. Our experimental results show that this algorithm provides better scheduling results than list scheduling with insertion; and dominant sequence clustering heuristics. Our algorithm generates good schedules even in

Pai-Chou Wang; Willard Korfhage

1995-01-01

10

Deceptiveness and Genetic Algorithm Dynamics  

Microsoft Academic Search

We address deceptiveness, one of at least four reasons genetic algorithms 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 genetic algorithm selection recombination as

Gunar E. Liepins; Michael D. Vose

1990-01-01

11

Noise Analysis Compact Genetic Algorithm  

Microsoft Academic Search

\\u000a This paper proposes the Noise Analysis compact Genetic Algorithm (NAcGA). This algorithm integrates a noise analysis component\\u000a within a compact structure. This fact makes the proposed algorithm appealing for those real-world applications characterized\\u000a by the necessity of a high performance optimizer despite severe hardware limitations. The noise analysis component adaptively\\u000a assigns the amount of fitness evaluations to be performed in

Ferrante Neri; Ernesto Mininno; Tommi Kärkkäinen

2010-01-01

12

Improved immune genetic algorithm for JSP  

Microsoft Academic Search

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

Quanyong Ju; Jianying Zhu

2008-01-01

13

Genetic Algorithms in Analytical Chemistry  

Microsoft Academic Search

Genetic algorithms (GA's) are search algorithms that imitate nature with their Darwinian survival of the fittest approach. They are well suited for searching among a large number of possibilities for solutions because they exploit knowledge contained in a population of initial solutions to generate new and potentially better solutions. GA's have several advantages over conventional search techniques. First, GA's consider

Barry K. Lavine; Anthony J. Moores

1999-01-01

14

Genetic algorithms in engineering electromagnetics  

Microsoft Academic Search

This paper presents a tutorial and overview of genetic algorithms 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

J. Michael Johnson; V. Rahmat-Samii

1997-01-01

15

A Genetic Engineering Approach to Genetic Algorithms  

Microsoft Academic Search

We present an extension to the standard genetic algorithm (GA), which is based on con- cepts of genetic engineering. The motivation is to discover useful and harmful genetic materials and then execute an evolutionary process in such a way that the population becomes increasingly composed of useful genetic material and increasingly free of the harmful genetic material. Compared to the

John S. Gero; Vladimir A. Kazakov

2001-01-01

16

A Genetic Engineering Approach to Genetic Algorithms  

Microsoft Academic Search

We present an extension to the standard genetic algorithm (GA), which is based on concepts of genetic engineering. The motivation is to discover useful and harmful genetic materials and then execute an evolutionary process in such a way that the population becomes increasingly composed of useful genetic material and increasingly free of the harmful genetic material. Compared to the standard

John S. Gero; Vladimir Kazakov

2006-01-01

17

Deceptiveness and genetic algorithm dynamics  

SciTech Connect

We address deceptiveness, one of at least four reasons genetic algorithms 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 genetic algorithm selection recombination as the interleaving of linear and quadratic operators. Spectral analysis of the underlying matrices allows us to draw preliminary conclusions about fixed points and their stability. We also obtain an explicit formula relating the nonuniform Walsh transform to the dynamics of genetic search. 21 refs.

Liepins, G.E. (Oak Ridge National Lab., TN (USA)); Vose, M.D. (Tennessee Univ., Knoxville, TN (USA))

1990-01-01

18

The Proportional Genetic Algorithm: Gene Expression in a Genetic Algorithm  

Microsoft Academic Search

We introduce a genetic algorithm (GA) with a new representationmethod which we call the proportional GA (PGA). The PGA is a multi-character GAthat relies on the existence or non-existence of genes to determine the informationthat is expressed. The information represented by a PGA individual depends onlyon what is present on the individual and not on the order in which it

Annie S. Wu; Ivan I. Garibay

2002-01-01

19

Ordering Genetic Algorithms and Deception  

Microsoft Academic Search

This paper considers deception in the context of ordering genetic algorithms (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

Hillol Kargupta; Kalyanmoy Deb; David E. Goldberg

1992-01-01

20

Rendezvous maneuvers using Genetic Algorithm  

NASA Astrophysics Data System (ADS)

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 genetic algorithm 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.

2013-10-01

21

Innovative genetic algorithms for chemoinformatics  

Microsoft Academic Search

In this paper, we report on the development of a genetic algorithm (GA) for pattern recognition analysis of multivariate chemical data. The GA identifies feature subsets that optimize the separation of the classes in a plot of the two or three largest principal components of the data. Because principal components maximize variance, the bulk of the information encoded by the

B. K Lavine; C. E Davidson; A. J Moores

2002-01-01

22

Examination of Hypercube Implementations of Genetic Algorithms.  

National Technical Information Service (NTIS)

Genetic algorithms are stochastic search algorithms which model natural adaptive systems. In support of the development of a genetic search package for AFIT's iPSC/2 Hypercube, this study focused on two problem areas associated with hypercube implementati...

A. Dymek

1992-01-01

23

Genetic Algorithms and Protein Folding  

Microsoft Academic Search

Contents1 Evolutionary Computation (introduction)1.1 Methodology1.1.1 Genetic Algorithms1.1.2 Evolution Strategy1.2 Applications1.2.1 Protein Folding Simulation by Force Field Optimisation1.2.1.1 Representation Formalism1.2.1.2 Fitness Function1.2.1.3 Conformational Energy1.2.1.4 Genetic Operators1.2.1.5 Ab initio Prediction Results1.2.1.6 Side Chain Placement1.2.2 Multi-Criteria Optimisation of Protein Conformations1.2.2.1 Vector Fitness Function1.2.2.2 Specialised Genetic...

Steffen Schulze-Kremer; Westfälische Strasse

1996-01-01

24

Fault tolerant cellular Genetic Algorithm  

Microsoft Academic Search

This paper presents a cellular Genetic Algorithm (cGA) which aims at realizing a fault tolerant platform based on the inherent ability of cGAs to deal with Single Hard Errors (SHE) that could permanently affect the operation of a system. To attain this objective it is indispensable to control the parameters of the cGA which directly affect the efficiency and accuracy

Alicia Morales-reyes; Evangelos F. Stefatos; Ahmet T. Erdogan; Tughrul Arslan

2008-01-01

25

A genetic engineering approach to genetic algorithms.  

PubMed

We present an extension to the standard genetic algorithm (GA), which is based on concepts of genetic engineering. The motivation is to discover useful and harmful genetic materials and then execute an evolutionary process in such a way that the population becomes increasingly composed of useful genetic material and increasingly free of the harmful genetic material. Compared to the standard GA, it provides some computational advantages as well as a tool for automatic generation of hierarchical genetic representations specifically tailored to suit certain classes of problems. PMID:11290285

Gero, J S; Kazakov, V

2001-01-01

26

Elitism-based compact genetic algorithms  

Microsoft Academic Search

This paper describes two elitism-based compact genetic algorithms (cGAs)-persistent elitist compact genetic algorithm (pe-cGA), and nonpersistent elitist compact genetic algorithm (ne-cGA). The aim is to design efficient cGAs by treating them as estimation of distribution algorithms (EDAs) for solving difficult optimization problems without compromising on memory and computation costs. The idea is to deal with issues connected with lack of

Chang Wook Ahn; Rudrapatna S. Ramakrishna

2003-01-01

27

Genetic motion search algorithm for video compression  

Microsoft Academic Search

A new approach to block-based motion estimation for video compression, called the genetic motion search (GMS) algorithm, is introduced. It makes use of a natural processing concept called genetic algorithm (GA). In contrast to existing fast algorithms, which rely on the assumption that the matching error decreases monotonically as the search point moves closer to the global optimum, the GMS

Keith Hung-Kei Chow; Ming L. Liou

1993-01-01

28

Learning Intelligent Genetic Algorithms Using Japanese Nonograms  

ERIC Educational Resources Information Center

|An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…

Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen

2012-01-01

29

Theoretical Investigation of a Parallel Genetic Algorithm.  

National Technical Information Service (NTIS)

In the past few years the limitations of uniprocessor computing systems and the increasing availability of multiprocessors have led to investigations of parallel genetic algorithms. One algorithm in particular, called PGA, consists of a set of communicati...

C. C. Pettey M. R. Leuze

1989-01-01

30

Genetic Programming Operators Applied to Genetic Algorithms  

Microsoft Academic Search

Like other learning paradigms, the performance of the genetic algorithms (GAs) is dependent on the parameter choice, on the problem representation, and on the fitness landscape. Accordingly, a GA can show good or weak results even when applied on the same problem. Following this idea, the crossover operator plays an important role, and its study is the object of the

Dana Vrajitoru

1999-01-01

31

Research on Routing Selection Algorithm Based on Genetic Algorithm  

NASA Astrophysics Data System (ADS)

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 genetic algorithm.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.

Gao, Guohong; Zhang, Baojian; Li, Xueyong; Lv, Jinna

32

Incremental multiple objective genetic algorithms.  

PubMed

This paper presents a new genetic algorithm approach to multiobjective optimization problems--incremental multiple objective genetic algorithms (IMOGA). Different from conventional MOGA methods, it takes each objective into consideration incrementally. The whole evolution is divided into as many phases as the number of objectives, and one more objective is considered in each phase. Each phase is composed of two stages. First, an independent population is evolved to optimize one specific objective. Second, the better-performing individuals from the single-objecive population evolved in the above stage and the multiobjective population evolved in the last phase are joined together by the operation of integration. The resulting population then becomes an initial multiobjective population, to which a multiobjective evolution based on the incremented objective set is applied. The experiment results show that, in most problems, the performance of IMOGA is better than that of three other MOGAs, NSGA-II, SPEA, and PAES. IMOGA can find more solutions during the same time span, and the quality of solutions is better. PMID:15484906

Chen, Qian; Guan, Sheng-Uei

2004-06-01

33

Genetic Algorithms for Protein Folding Simulations  

Microsoft Academic Search

Genetic algorithms methods utilize the same optimization procedures as natural genetic evolution, in which a population is gradually improved by selection. We have developed a genetic algorithm search procedure suitable for use in protein folding simulations. A population of conformations of the polypeptide chain is maintained, and conformations are changed bx mutation, in the form of conventional Monte Carlo steps,

Ron Unger; John Moult

1993-01-01

34

Hybrid Parallelization of a Compact Genetic Algorithm  

Microsoft Academic Search

Genetic algorithms (GAs) are stochastic optimization heuristics in which searches in solution space are carried out by imitating the population genetics stated in Darwin's theory of evolution. We have focused this work on compact genetic algorithms (cGAs), which unlike standard GAs do not manage a population of solutions but only mimics its existence. We study several approaches that can be

José Ignacio Hidalgo; Manuel Prieto; Juan Lanchares; Ranieri Baraglia; Francisco Tirado; Oscar Garnica

2003-01-01

35

Automatic facial feature extraction by genetic algorithms  

Microsoft Academic Search

An automatic facial feature extraction algorithm is presented. The algorithm is composed of two main stages: the face region estimation stage and the feature extraction stage. In the face region estimation stage, a second-chance region growing method is adopted to estimate the face region of a target image. In the feature extraction stage, genetic search algorithms are applied to extract

Chun-Hung Lin; Ja-Ling Wu

1999-01-01

36

Genetic Algorithms in Engineering and Computer Science  

Microsoft Academic Search

Contents 13 Parallel Genetic Algorithms for Optimisation in CFD : : : : : : : : 1 13.1 INTRODUCTION : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 13.2 CFD ANALYSIS FOR AEROSPACE DESIGN : : : :

Edited J. P'eriaux; G. Winter; John Wiley Sons; Thomas Back

1995-01-01

37

Genetic algorithm for CNN template learning  

Microsoft Academic Search

A learning algorithm for space invariant cellular neural networks (CNNs) is described. Learning is formulated as an optimization problem. Exploration of any specified domain of stable CNNs is possible by the current approach. Templates are derived using a genetic optimization algorithm. Details of the algorithm are discussed and several application results are shown. Using this algorithm, propagation-type and gray-scale-output CNNs

Tibor Kozek; Tamas Roska; L. O. Chua

1993-01-01

38

Genetic algorithm and thin-film design  

NASA Astrophysics Data System (ADS)

Genetic algorithms, developed by J. H. Holland, are randomized search algorithms which mimic the mechanics of natural selection and natural genetics. In this work a genetic algorithm was applied to optimization of sophisticated thin-film systems. It was found to be an efficient tool rapidly converging to the target as defined by the merit function. The algorithm is introduced, discussed, and illustrated for two applications: a polarization-preserving coating of a penta-roof prism for a long-grange optical sight and a phase light splitter--beam divider/combiner for bi-directional fringe-counting interferometers.

Rabinovitch, Kopel; Toker, Gregory

1994-09-01

39

Scaling Genetic Algorithms Using MapReduce  

Microsoft Academic Search

Genetic algorithms(GAs) are increasingly being applied to large scale problems. The tra- ditional MPI-based parallel GAs do not scale very well. MapReduce is a powerful abstraction developed by Google for making scalable and fault tolerant applications. In this paper, we mould genetic algorithms into the the MapReduce model. We describe the algorithm design and implementation of GAs on Hadoop, the

Abhishek Verma; Xavier Llorà; David E. Goldberg; Roy H. Campbell

2009-01-01

40

Extended Selection Mechanisms in Genetic Algorithms  

Microsoft Academic Search

Common selection mechanisms used in EvolutionaryAlgorithms are combined to formsome generalized variants of selection. Theseare applied to a Genetic Algorithm and aresubject to an experimental comparison. Thefeature of extinctiveness as introduced inEvolution Strategies is identified to be themain reason for a considerable speedup ofthe search in case of unimodal objective functions.1 IntroductionGenetic Algorithms (GAs) [Hol75] and EvolutionStrategies (ESs) [Rec73, Sch81

Thomas Bäck; Frank Hoffmeister

1991-01-01

41

Genetic algorithm solution of groundwater management models  

Microsoft Academic Search

Groundwater simulation models have been incorporated into a genetic algorithm to solve three groundwater management problems: maximum pumping from an aquifer; minimum cost water supply development; and minimum cost aquifer remediation. The results show that genetic algorithms can effectively and efficiently be used to obtain globally (or, at least near globally) optimal solutions to these groundwater management problems. The formulation

Daene C. McKinney; Min-Der Lin

1994-01-01

42

Enhancing Data Selection Using Genetic Algorithm  

Microsoft Academic Search

Genetic algorithms are becoming increasingly valuable in solving large-scale, realistic, difficult problems, and selecting replica with multiple selection criteria - availability, security and time- is one of these problems. In this paper, a rank based elitist clustering Genetic Algorithm is proposed named RRWSGA, which alleviates the problem of being trapped in local clustering centroids using k-mean. Simulation results show that

Omar Al Jadaan; W. Abdulal; M. A. Hameed; A. Jabas

2010-01-01

43

Integrated optical devices design by genetic algorithm  

Microsoft Academic Search

In this work, we use multiple scattering in conjunction with a genetic algorithm to reliably determine the optimized photonic-crystal-based structure able to perform a specific optical task. The genetic algorithm operates on a population of candidate structures to produce new candidates with better performance in an iterative process. The potential of this approach is illustrated by designing a spot size

L. Sanchis; A. Kansson; D. López-Zanón; J. Bravo-Abad; José Sánchez-Dehesa

2004-01-01

44

A genetic algorithm for facility layout  

Microsoft Academic Search

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 genetic algorithm is presented for obtaining efficient layouts. The different aspects involved in the design of efficient genetic algorithms are discussed in detail. It is shown that the population

G. SURESH; V. V. VINOD; S. SAHU

1995-01-01

45

Interactive genetic algorithms with large population size  

Microsoft Academic Search

Interactive genetic algorithms (IGAs) are effective methods to solve an optimization problem with implicit indices. Whereas it requires direct evaluation of user for each individual and the fact limits the population size for user fatigue problem. While, in general to solve many problems with genetic algorithm, it is desirable to maintain the population size as large as possible. To break

Dunwei Gong; Jie Yuan; Xiaoping Ma

2008-01-01

46

Solar cell parameter extraction using genetic algorithms  

Microsoft Academic Search

In this paper, a technique based on genetic algorithms is proposed for improving the accuracy of solar cell parameters extracted using conventional techniques. The approach is based on formulating the parameter extraction as a search and optimization problem. Current-voltage data used were generated by simulating a two-diode solar cell model of specified parameters. The genetic algorithm search range that simulates

Joseph A. Jervase; Hadj Bourdoucen; Ali Al-Lawati

2001-01-01

47

Compact Genetic Algorithms Based on Mutation  

Microsoft Academic Search

(Abstract)Compact Genetic Algorithm(CGA) requires a small amount of memory, but it is apt to premature stagnate. This paper proposes a Mutation-Based Compact Genetic Algorithm(MBCGA) by introducing the mutation operator into CGA, thus MBCGA mimics all the main genetic operators in natural evolution, then local search is strengthened and premature stagnation can be avoided. Experimental results show that the MBCGA generally

LIN Tu-sheng; LIAO Liang

2008-01-01

48

Multi-Objective Genetic Local Search Algorithm  

Microsoft Academic Search

Proposes a hybrid algorithm for finding a set of non-dominated solutions of a multi-objective optimization problem. In the proposed algorithm, a local search procedure is applied to each solution (i.e. to each individual) generated by genetic operations. The aim of the proposed algorithm is not to determine a single final solution but to try to find all the non-dominated solutions

Hisao Ishibuchi; Tadahiko Murata

1996-01-01

49

Genetic Algorithm in Vulnerability Evaluation  

Microsoft Academic Search

Seismic hazard analysis and vulnerability evaluation are two parts of seismic risk assessment. The form of the vulnerability evaluation procedure in HAZUS99, which is widely adopted in US and world wide, is taken as reference. A hybrid approach integrated the Simplex with the Generic Algorithm is adopted to inverse the parameters of capacity curve and fragility curve from the earthquake

Zheng-ru Tao; Xia-xin Tao

2009-01-01

50

Genetic algorithms-based unipolar IPA model  

NASA Astrophysics Data System (ADS)

A new method, using genetic algorithms, for constructing a redundant unipolar interconnection weight matrix of the Interpattern Association model is presented. The global searching features of the genetic algorithms are adopted to help us avoiding too complicate procedures to complete a link search of using exhaustive search method. The upper bond and the lower bond of the searching links are adapted from the results of the maximum mode and the minimum mode of the RIPA model respectively. Computer simulation results show that the proposed genetic algorithms method not only has the features of accurate of constructing the IWM, but also has better network performance.

Uang, Chii-Maw; Yang, Yuan-Hsiu; Jiang, Ching-Fen

2002-01-01

51

Genetic algorithms and the immune system  

SciTech Connect

Using genetic algorithm techniques we introduce a model to examine the hypothesis that antibody and T cell receptor genes evolved so as to encode the information needed to recognize schemas that characterize common pathogens. We have implemented the algorithm on the Connection Machine for 16,384 64-bit antigens and 512 64-bit antibodies. 8 refs.

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

1990-01-01

52

Genetic Algorithms and the Immune System  

Microsoft Academic Search

Using genetic algorithm techniques we introduce a model to examine the hypothesis that antibody and T cell receptor genes evolved so as to encode the information needed to recognize schemas that characterize common pathogens. We have implemented the algorithm on the Connection Machine for 16,384 64-bit antigens and 512 64-bit antibodies.

Stephanie Forrest; Alan S. Perelson

1990-01-01

53

A Cellular Genetic Algorithm for Multiobjective Optimization  

Microsoft Academic Search

This paper introduces a new cellular genetic algorithm for solving multiobjective contin- uous optimization problems. Our approach is characterized by using an external archive to store non-dominated solutions and a feedback mechanism in which solutions from this archive randomly replaces existing individuals in the population after each iteration. The result is a simple and elitist algorithm called MOCell. Our proposal

A. J. Nebro; J. J. Durillo; F. Luna; B. Dorronsoro; E. Alba

54

Selection in Massively Parallel Genetic Algorithms  

Microsoft Academic Search

The availability of massively parallel computers makes it possible to applygenetic algorithms to large populations and very complex applications. Amongthese applications are studies of natural evolution in the emerging field of artificiallife, which place special demands on the genetic algorithm. In this paper,we characterize the difference between panmictic and local selection\\/matingschemes in terms of diversity of alleles, diversity of genotypes,

Robert J. Collins; David R. Jefferson

1991-01-01

55

Real options approach to evaluating genetic algorithms  

Microsoft Academic Search

The real options technique has emerged as an evaluation tool for investment under uncertainty. It explicitly recognizes future decisions, and the exercise strategy is based on the optimal decisions in future periods. This paper employs the optimal stopping policy derived from real options approach to analyze and evaluate genetic algorithms, specifically for the new branches namely Estimation of Distribution Algorithms

Sunisa Rimcharoen; Daricha Sutivong; Prabhas Chongstitvatana

2009-01-01

56

Genetic algorithms and supernovae type Ia analysis  

NASA Astrophysics Data System (ADS)

We introduce genetic algorithms 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) ? PDE/?DE. Specifically, we will give a brief introduction to the genetic algorithms along with some simple examples to illustrate their advantages and finally we will apply them to the supernovae type Ia data. We find that genetic algorithms 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, genetic algorithms 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

2009-05-01

57

Massive parallelization of the compact genetic algorithm  

Microsoft Academic Search

This paper presents an architecture which is suitable for a massive parallelization of the compact genetic algorithm. The resulting scheme has three major advantages. First, it has low synchronization costs. Second, it is fault tolerant, and third, it is scalable.

Fernando G. Lobo; Claudio F. Lima; Hugo Mártires

58

Genetic Algorithm Approaches for Actuator Placement.  

National Technical Information Service (NTIS)

This research investigated genetic algorithm 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 focu...

W. A. Crossley

2000-01-01

59

Is the Genetic Algorithm a Cooperative Learner.  

National Technical Information Service (NTIS)

This paper begins to explore an analogy between the usual competitive learning metaphor presented in the genetic algorithm (GA) literature and the cooperative learning metaphor discussed by Clearwater, Huberman, and Hogg. In a blackboard cooperative learn...

H. G. Cobb

1995-01-01

60

Genetic algorithms at UC Davis/LLNL  

SciTech Connect

A tutorial introduction to genetic algorithms is given. This brief tutorial should serve the purpose of introducing the subject to the novice. The tutorial is followed by a brief commentary on the term project reports that follow.

Vemuri, V.R. [comp.

1993-12-31

61

A weight based compact genetic algorithm  

Microsoft Academic Search

In order to improve the performance of the compact Genetic Algorithm (cGA) to solve difficult optimization problems, an improved cGA which named as the weight based compact Genetic Algorithm (wcGA) is proposed. In the wcGA, S individuals are generated from the probability vector in each generation, when the winner competing with the other S-1 individuals to update the probability vector,

Qing-bin Zhang; Ti-hua Wu; Bo Liu

2009-01-01

62

Adaptive sensor fusion using genetic algorithms  

SciTech Connect

Past attempts at sensor fusion have used some form of Boolean logic to combine the sensor information. As an alteniative, an adaptive ``fuzzy`` sensor fusion technique is described in this paper. This technique exploits the robust capabilities of fuzzy logic in the decision process as well as the optimization features of the genetic algorithm. This paper presents a brief background on fuzzy logic and genetic algorithms and how they are used in an online implementation of adaptive sensor fusion.

Fitzgerald, D.S.; Adams, D.G.

1994-08-01

63

Optical tomography using a genetic algorithm  

NASA Astrophysics Data System (ADS)

A new tomographic image reconstruction method is proposed that uses a genetic algorithm (GA), a robust optimization algorithm based on the genetic principle of natural selection. For the purpose of description, a simple axisymmetric reference density field is reconstructed from its interferometric projection by the developed GA-based tomography. This preliminary investigation shows a promising potential of the GA-based tomography to overcome the problems associated with other existing tomographic methods, particularly for limited projections.

Kihm, Ken D.; Lyons, Donald P.

1996-09-01

64

Refined genetic algorithm -- Economic dispatch example  

SciTech Connect

A genetic-based algorithm is used to solve an economic dispatch (ED) problem. The algorithm utilizes payoff information of perspective solutions to evaluate optimality. Thus, the constraints of classical LaGrangian techniques on unit curves are eliminated. Using an economic dispatch problem as a basis for comparison, several different techniques which enhance program efficiency and accuracy, such as mutation prediction, elitism, interval approximation and penalty factors, are explored. Two unique genetic algorithms are also compared. The results are verified for a sample problem using a classical technique.

Sheble, G.B.; Brittig, K. [Iowa State Univ., Ames, IA (United States)

1995-02-01

65

A Greedy Genetic Algorithm for the Quadratic Assignment Problem  

Microsoft Academic Search

The Quadratic Assignment Problem (QAP) is one of the classical combinatorial optimizationproblems and is known for its diverse applications. In this paper, we suggest a genetic algorithm for theQAP and report its computational behavior. The genetic algorithm incorporates many greedyprinciples in its design and, hence, is called the greedy genetic algorithm. The ideas we incorporate inthe greedy genetic algorithm include

Ravindra K. Ahuja; James B. Orlin

1997-01-01

66

Evolving evolutionary algorithms using linear genetic programming.  

PubMed

A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems. PMID:16156929

Oltean, Mihai

2005-01-01

67

Effective degrees of freedom in genetic algorithms  

Microsoft Academic Search

An evolution equation for a population of strings evolving under the genetic operators, selection, mutation, and crossover, is derived. The corresponding equation describing the evolution of schemata is found by performing an exact coarse graining of this equation. In particular, exact expressions for schema reconstruction are derived that allow for a critical appraisal of the ``building-block hypothesis'' of genetic algorithms.

C. R. Stephens; H. Waelbroeck

1998-01-01

68

Cognitive radio resource allocation based on coupled chaotic genetic algorithm  

NASA Astrophysics Data System (ADS)

A coupled chaotic genetic algorithm for cognitive radio resource allocation which is based on genetic algorithm and coupled Logistic map is proposed. A fitness function for cognitive radio resource allocation is provided. Simulations are conducted for cognitive radio resource allocation by using the coupled chaotic genetic algorithm, simple genetic algorithm and dynamic allocation algorithm respectively. The simulation results show that, compared with simple genetic and dynamic allocation algorithm, coupled chaotic genetic algorithm reduces the total transmission power and bit error rate in cognitive radio system, and has faster convergence speed.

Zu, Yun-Xiao; Zhou, Jie; Zeng, Chang-Chang

2010-11-01

69

Scalability problems of simple genetic algorithms.  

PubMed

Scalable evolutionary computation has become an intensively studied research topic in recent years. The issue of scalability is predominant in any field of algorithmic design, but it became particularly relevant for the design of competent genetic algorithms once the scalability problems of simple genetic algorithms were understood. Here we present some of the work that has aided in getting a clear insight in the scalability problems of simple genetic algorithms. Particularly, we discuss the important issue of building block mixing. We show how the need for mixing places a boundary in the GA parameter space that, together with the boundary from the schema theorem, delimits the region where the GA converges reliably to the optimum in problems of bounded difficulty. This region shrinks rapidly with increasing problem size unless the building blocks are tightly linked in the problem coding structure. In addition, we look at how straightforward extensions of the simple genetic algorithm-namely elitism, niching, and restricted mating are not significantly improving the scalability problems. PMID:10578026

Thierens, D

1999-01-01

70

Comparative study of immune algorithm and genetic algorithm in thinned rectangular array synthesizing  

NASA Astrophysics Data System (ADS)

Immune algorithm is applied in pattern synthesizing of thinned array. Against the structural characteristic of rectangular plane thinned array, pattern function with form of Kronecker product is used in order to reduce the amount of the computation and improve the speed of operation. Immune algorithm is improved through the use of adaptive clone and Gauss Variation in order to overcome blind search of the algorithm. As a result searching efficiency is advanced. Immune algorithm and genetic algorithm are respectively used in side-lobe optimizing of rectangular thinned array. The result which using immune algorithm is obviously superior than which using genetic algorithm immune algorithm can reduce the side-lobe of rectangular thinned array, which show the flexibility and effectiveness of the immune algorithm and Immune algorithm has a better global convergence than genetic algorithm.

Zhang, Jianhua; Pang, Weizheng

2010-08-01

71

A Combined Nelder-Mead Simplex and Genetic Algorithm  

Microsoft Academic Search

It is usually said that genetic algorithm should be used when nothing else works. In practice, genetic algorithm are very often used for large sized global optimization problems, but are not very efficient for local optimization problems. The Nelder-Mead simplex algorithm has some common characteristics with genetic algorithm, but it can only find a local optimum close to the starting

Nicolas Durand

72

Hybrid genetic algorithm research and its application in problem optimization  

Microsoft Academic Search

There is a lot of research in genetic algorithm about structural optimization. But as far as the large multi-goal program is concerned, it limits the application of genetic algorithm 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 genetic algorithm

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

2004-01-01

73

Equilibrium stellar systems with genetic algorithms  

NASA Astrophysics Data System (ADS)

In 1979, M Schwarzschild showed that it is possible to build an equilibrium triaxial stellar system. However, the linear programmation used to that goal was not able to determine the uniqueness of the solution, nor even if that solution was the optimum one. Genetic algorithms are ideal tools to find a solution to this problem. In this work, we use a genetic algorithm to reproduce an equilibrium spherical stellar system from a suitable set of predefined orbits, obtaining the best solution attainable with the provided set. FULL TEXT IN SPANISH

Gularte, E.; Carpintero, D. D.

74

Genetic algorithm for neural networks optimization  

NASA Astrophysics Data System (ADS)

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 Genetic Algorithm, 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 Genetic Algorithm were programmed using MATLAB®.

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

2004-11-01

75

Solar cell parameter extraction using genetic algorithms  

NASA Astrophysics Data System (ADS)

In this paper, a technique based on genetic algorithms is proposed for improving the accuracy of solar cell parameters extracted using conventional techniques. The approach is based on formulating the parameter extraction as a search and optimization problem. Current-voltage data used were generated by simulating a two-diode solar cell model of specified parameters. The genetic algorithm search range that simulates the error in the extracted parameters was varied from ±5 to ±100% of the specified parameter values. Results obtained show that for a simulated error of ±5% in the solar cell model values, the deviation of the extracted parameters varied from 0.1 to 1% of the specified values. Even with a simulated error of as high as ±100%, the resulting deviation only varied from 2 to 36%. The performance of this technique is also shown to surpass the quasi-Newton method, a calculus-based search and optimization algorithm.

Jervase, Joseph A.; Bourdoucen, Hadj; Al-Lawati, Ali

2001-11-01

76

Genetic algorithms in truss topological optimization  

Microsoft Academic Search

The present paper describes the use of a stochastic search procedure that is the basis of genetic algorithms, in developing near-optimal topologies of load-bearing truss structures. The problem addressed is one wherein the structural geometry is created from a specification of load conditions and available support points in the design space. The development of this geometry must satisfy kinematic stability

P. Hajela; E. Lee

1995-01-01

77

Multiobjective optimization of trusses using genetic algorithms  

Microsoft Academic Search

In this paper we propose the use of the genetic algorithm (GA) as a tool to solve multiobjective optimization problems in structures. Using the concept of min–max optimum, a new GA-based multiobjective optimization technique is proposed and two truss design problems are solved using it. The results produced by this new approach are compared to those produced by other mathematical

Carlos A. Coello Coello; Alan D. Christiansen

2000-01-01

78

Using Genetic Algorithms for Concept Learning  

Microsoft Academic Search

In this paper, we explore the use of genetic algorithms (GAs) as a key element in the design and implementation of robust concept learning systems. We describe and evaluate a GA-based system called GABIL that continually learns and refines concept classification rules from its interaction with the environment. The use of GAs is motivated by recent studies showing the effects

Kenneth A. De Jong; William M. Spears; Diana F. Gordon

1993-01-01

79

Genetic Linkage Analysis Algorithms and Their Implementation  

Microsoft Academic Search

Linkage analysis is a well established method for studying the rela- tionship between the pattern of the occurrence of a given biological trait such as a disease and the inheritance pattern of certain genes in a given family. In this paper we give some improved algorithms for the genetic linkage analysis. In particular we offer an MTBDD based version of

Anna Ingólfsdóttir; Daniel Gudbjartsson

2005-01-01

80

Design space exploration using the genetic algorithm  

Microsoft Academic Search

A typical VLSI layout problem involves the simultaneous optimization of a number of competing criteria. Rather than generating a single compromise solution, some recent approaches explicitly explores the design space and outputs a set of alternative solutions, thereby providing explicit information on the possible tradeoffs. This paper discuss the use of genetic algorithms (GAs) for design space exploration and propose

Henrdk Esbensen; Ernest S. Kuh

1996-01-01

81

Saving Resources with Plagues in Genetic Algorithms.  

National Technical Information Service (NTIS)

The population size of genetic algorithms (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

2004-01-01

82

Multicriteria inventory classification using a genetic algorithm  

Microsoft Academic Search

One of the application areas of genetic algorithms is parameter optimization. This paper addresses the problem of optimizing a set of parameters that represent the weights of criteria, where the sum of all weights is 1. A chromosome represents the values of the weights, possibly along with some cut-off points. A new crossover operation, called continuous uniform crossover, is proposed,

H. Altay Guvenir; Erdal Erel

1998-01-01

83

Genetic algorithm for optimization of optical systems  

Microsoft Academic Search

In this paper we described a blind optimization technique with an in-expensive electronics for optical systems to maximize the output signal. Deformable mirror is the main optical element used in the system to correct the wavefront and increase the output signal. The mirror is controlled by genetic algorithms through the computer microphone port and two PCI-Express cards.

Mohammad R. N. Avanaki; S. A. Hojjatoleslami; R. Ebrahimpour; H. Sarmadi; A. G. H. Podoleanu

2010-01-01

84

Hybrid Genetic Algorithms for Feature Selection  

Microsoft Academic Search

This paper proposes a novel hybrid genetic algorithm for feature selection. Local search operations are devised and embedded in hybrid GAs to fine-tune the search. The operations are parameterized in terms of their fine-tuning power, and their effectiveness and timing requirements are analyzed and compared. The hybridization technique produces two desirable effects: a significant improvement in the final performance and

Il-Seok Oh; Jin-Seon Lee; Byung-Ro Moon

2004-01-01

85

Training Feedforward Neural Networks Using Genetic Algorithms  

Microsoft Academic Search

Multilayered feedforward neural networks possess a number of properties which make them particu­ larly suited to complex pattern classification prob­ lems. However, their application to some real- world problems has been hampered by the lack of a training algonthm which reliably finds a nearly globally optimal set of weights in a relatively short time. Genetic algorithms are a class of

David J. Montana; Lawrence Davis

1989-01-01

86

Feature subset selection using a genetic algorithm  

Microsoft Academic Search

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 genetic algorithm to select such subsets, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features

J. Yang; V. Honavar

1998-01-01

87

Probabilistic reliability optimization using hybrid genetic algorithms  

Microsoft Academic Search

In this paper transmission power system structure optimization is performed via a minimal spanning tree based encoded fuzzy logic self-controlled hybrid genetic algorithm (GA). During the redundancy optimization of the power system network a binary encoded GA is used for a modified transmission network expansion problem, finding the optimal power line type with respect to the net present value (NPV)

A. Gaun; G. Rechberger; H. Renner

2010-01-01

88

Tuning fuzzy logic controllers by genetic algorithms  

Microsoft Academic Search

The performance of a fuzzy logic controller depends on its control rules and membership functions. Hence, it is very important to adjust these parameters to the process to be controlled. A method is presented for tuning fuzzy control rules by genetic algorithms to make the fuzzy logic control systems behave as closely as possible to the operator or expert behavior

Francisco Herrera; Manuel Lozano; José L. Verdegay

1995-01-01

89

A genetic algorithm for inverse radiation problems  

Microsoft Academic Search

An inverse radiation analysis for simultaneous estimation of the single scattering albedo, the optical thickness and the phase function, from the knowledge of the exit radiation intensities is presented. A genetic algorithm is adopted as the optimizer to search the parameters of the radiation system. The study shows that the single scattering albedo and the optical thickness can be estimated

C. Y. Yang

1997-01-01

90

Optimal synthesis of mechanisms with genetic algorithms  

Microsoft Academic Search

This paper deals with solution methods of optimal synthesis of planar mechanisms. A searching procedure is defined which applies genetic algorithms based on evolutionary techniques and the type of goal function. Problems of synthesis of four-bar planar mechanisms are used to test the method, showing that solutions are accurate and valid for all cases. The possibility of extending the method

J. A. Cabrera; A. Simon; M. Prado

2002-01-01

91

Optimal Power Flow by Enhanced Genetic Algorithm  

Microsoft Academic Search

This paper presents an enhanced genetic algorithm for the solution of the optimal power flow with both continuous and discrete control variables. The continuous control variables modeled are unit active power outputs and generator-bus voltage magnitudes, while the discrete ones are transformer-tap settings and switchable shunt devices. A number of functional operating constraints, such as branch flow limits, load bus

A. G. Bakirtzis; P. N. Biskas; C. E. Zoumas; V. Petridis

2002-01-01

92

Optimal power flow by enhanced genetic algorithm  

Microsoft Academic Search

This paper presents an enhanced genetic algorithm (EGA) for the solution of the optimal power flow (OPF) with both continuous and discrete control variables. The continuous control variables modeled are unit active power outputs and generator-bus voltage magnitudes, while the discrete ones are transformer-tap settings and switchable shunt devices. A number of functional operating constraints, such as branch flow limits,

Anastasios G. Bakirtzis; Pandel N. Biskas; Christoforos E. Zoumas; Vasilios Petridis

2002-01-01

93

Implementing continuous improvement using genetic algorithms  

Microsoft Academic Search

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 genetic algorithms (GA) can be used for designing quality management systems for keeping the

Petter Øgland

94

Genetic algorithms for spectral pattern recognition  

Microsoft Academic Search

The development of a genetic algorithm (GA) for pattern recognition analysis of spectral data is reported. The GA identifies a set of features (wavelengths) that optimize the separation of the classes in a plot of the two or three largest principal components of the data. Because principal components maximize variance, the bulk of the information encoded by the selected features

B. K. Lavine; C. E. Davidson; A. J. Moores

2002-01-01

95

Alternative flight route generator by genetic algorithms  

Microsoft Academic Search

This paper presents a new air traffic route generator based on genetic algorithms. Due to traffic growth, direct (and near direct) routes are becoming increasingly congested and there is a real need for spreading traffic on new alternative routes. Those routes have to be different from several operational criteria and must not generate too much extra distance compared to the

Sofiane Oussedik; Daniel Delahaye; Marc Schoenauer

2000-01-01

96

Flights Alternative Routes Generator by Genetic Algorithms  

Microsoft Academic Search

This paper present a new Air Traffic routes gen- erator based on Genetic Algorithms. Due to the traffic growth, direct (and near direct) routes are more and more congested and there is a real need for spreading the traffic on new alternative routes. Those routes have to be differ- ent from several operational criteria and must not gen- erate too

Daniel Delahaye; Marc Schoenauer

2000-01-01

97

Facilities layout design by genetic algorithms  

Microsoft Academic Search

Genetic algorithms (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,

R. Tavakkoli-Moghaddain; E. Shayan

1998-01-01

98

A genetic algorithm for facility layout problems  

Microsoft Academic Search

This paper is concerned with the application of the technique of genetic algorithms 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

K. L. Mak; Y. S. Wong; F. T. S. Chan

1998-01-01

99

Genetic algorithms based on an intelligent controller  

Microsoft Academic Search

Genetic algorithms (GAs) have been proven as robust search procedures. Numerous researchers have established the validity of GAs in optimization, machine learning and control applications. This paper presents a new intelligent control scheme using the robust sear h feature of GAs incorporating the basic idea of self-tuning regulators. The proposed controller utilized GAs to search for the changes of system

Pataya Dangprasert; Vichit Avatchanakorn

1996-01-01

100

GADF - Genetic Algorithms for distribution fitting  

Microsoft Academic Search

Distribution fitting is a widely recurring problem in different fields such as telecommunication, finance and economics, sociology, physics, etc. Standard methods often require solving difficult equations systems or investments in specialized software. The paper presents a new approach to distribution fitting that exploits Genetic Algorithms in order to simultaneously identify the distribution type and tune its parameters by exploiting a

Valentina Colla; Gianluca Nastasi; Nicola Matarese

2010-01-01

101

Genetic algorithms for MRI magnet design  

Microsoft Academic Search

Continuing advances in the field of parallel computing have allowed nonlinear optimization techniques to be applied to many problems previously considered too computationally demanding. We describe a general magnet design software package, CamGASP, which uses genetic algorithms (GAs) for the design of large whole-body MRI systems. The method of GAS allows a population of many designs to evolve with a

Nicholas R. Shaw; Richard E. Ansorge

2002-01-01

102

Concurrent assembly planning with genetic algorithms  

Microsoft Academic Search

This work investigates the application of genetic algorithm (GA)-based search techniques to concurrent assembly planning, where product design and assembly process planning are performed in parallel, and the evaluation of a design configuration is influenced by the performance of its related assembly process. Several types of GAs and an exhaustive combinatorial approach are compared, in terms of reliability and speed

Nicola Senin; Roberto Groppetti; David R Wallace

2000-01-01

103

Intelligent sales forecasting engine using genetic algorithms  

Microsoft Academic Search

Times series techniques have been extensively used for Sales forecasting. Research has established that a combination forecast works better than a single forecast. Our research attempts to design an Intelligent Forecasting Engine which will use a combination forecasting technique. This design is based on use of Genetic Algorithms, for selecting the best methods to combine for forecasting. Early results demonstrate

M. Vijayalakshmi; Bernard Menezes; Rohit Menon; Aniket Divecha; Rajesh Ravindran; Kamal Mehta

2010-01-01

104

LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS  

Microsoft Academic Search

Genetic Algorithms are used to learn navigation and collision avoidance behaviors for robots. The learning is performed under simulation, and the resulting behaviors are then used to control the actual robot. The approach to learning behaviors for robots described here reflects a particular methodology for learning via a simulation model. The motivation is that making mistakes on real systems may

ALAN C. SCHULTZ

1994-01-01

105

Understanding Interactions among Genetic Algorithm Parameters  

Microsoft Academic Search

Genetic algorithms (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

Kalyanmoy Deb; Samir Agrawal

1998-01-01

106

Genetic Algorithms for Multiple-Choice Problems  

NASA Astrophysics Data System (ADS)

This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.

Aickelin, Uwe

2010-04-01

107

A family of genetic algorithms for the pallet loading problem  

Microsoft Academic Search

This paper is concerned with a family of genetic algorithms for the pallet loading problem. Our algorithms differ from previous applications of genetic algorithms to two-dimensional packing problems in that our coding contains all the information needed to produce the packing it represents, rather than relying on a packing algorithm to decode each individual solution. We experiment with traditional one-dimensional

Edward A. Herbert; Kathryn A. Dowsland

1996-01-01

108

FPGA Implementation of a Cellular Compact Genetic Algorithm  

Microsoft Academic Search

This paper presents a cellular compact genetic algorithm (CCGA) for evolvable and adaptive hardware. The CCGA has cellular-like structure which is suitable for hardware implementation. The CCGA is developed from compact genetic algorithm (CGA) and parallel estimation of distribution algorithm (EDA). The concept and algorithm of the CCGA are presented. The standard test functions are selected to measure the effectiveness

Y. Jewajinda; P. Chongstitvatana

2008-01-01

109

Production scheduling and rescheduling with genetic algorithms.  

PubMed

A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed at reasonable run-time costs. PMID:10199993

Bierwirth, C; Mattfeld, D C

1999-01-01

110

Genetic algorithm optimization of atomic clusters  

SciTech Connect

The authors have been using genetic algorithms 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

1996-12-31

111

Optimization of Low Thrust Spacecraft Trajectories Using a Genetic Algorithm.  

National Technical Information Service (NTIS)

This thesis concerns the use of genetic algorithms in the optimization of the trajectories of low thrust spacecraft. Genetic algorithms are programming tools which use the principles of biological evolution and adaptation to optimize processes. These algo...

J. C. Eisenreich

1998-01-01

112

Genetic Algorithms Compared to Other Techniques for Pipe Optimization  

Microsoft Academic Search

The genetic algorithm technique is a relatively new optimization tech- nique. In this paper we present a methodology for optimizing pipe networks using genetic algorithms. Unknown decision variables are coded as binary strings. We investigate a three-operator genetic algorithm comprising reproduction, crossover, and mutation. Results are compared with the techniques of complete enumeration and nonlinear programming. We apply the optimization

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

1994-01-01

113

A greedy genetic algorithm for the quadratic assignment problem  

Microsoft Academic Search

The Quadratic Assignment Problem (QAP) is one of the classical combinatorial optimization problems and is known for its diverse applications. In this paper, we suggest a genetic algorithm for the QAP and report its computational behavior. The genetic algorithm incorporates many greedy principles in its design and, hence, we refer to it as a greedy genetic algorithm. The ideas we

Ravindra K. Ahuja; James B. Orlin; Ashish Tiwari

2000-01-01

114

A Genetic Algorithm for the Multidimensional Knapsack Problem  

Microsoft Academic Search

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

P. C. Chu; J. E. Beasley

1998-01-01

115

GAVaPS - A Genetic Algorithm with Varying Population Size  

Microsoft Academic Search

The size of the population can be critical in many applications of genetic algorithms. If the population size is too small, the genetic algorithm may converge too quickly; if it is too large, the genetic algorithm may waste computational resources; the waiting time for an improvement might be too long. We propose an adaptive method for maintaining variable population size,

Jaroslaw Arabas; Zbigniew Michalewicz; Jan J. Mulawka

1994-01-01

116

GENETIC ALGORITHMS: A Search Technique Applied to Behavior Analysis  

Microsoft Academic Search

Genetic algorithms are powerful generalized search techniques. This paper shows that genetic algorithms can solve a difficult class of problems in general systems theory quickly and efficiently. Genetic algorithms appear to be ideally suited to solving the combinatorially complex problem of behavior analysis. The search space of behavior analysis experiences exponential growth as a function of the number of variables.

MARY K. DOBRANSKY; MARK J. WIERMAN

1996-01-01

117

Fuzzy logic genetic algorithm for hypercompression  

NASA Astrophysics Data System (ADS)

In this presentation, a fuzzy logic adaptive genetic algorithm (FLAGA) software engine is applied to hypercompression pre- processing. The FLAGA has a genetic algorithm (GA)-engine, tunable by fuzzy-logic rules. As a result, basic GA-engine operations, such as spanning, crossover, and mutation, have tunable rates, according to progress in the convergence process. Since the rates of these operations are not fixed but optimized in real-time, FLAGA convergence speed is at least one-order-of-magnitude higher than equivalent speed for a standard GA. In this paper, we present theoretical analysis and simulation results for this specific fuzzy logic application, as well as further considerations related to the application of FLAGA to video imaging and edge-extraction ATR (automatic target recognition).

Jannson, Tomasz; Kostrzewski, Andrew A.; Ternovskiy, Igor V.; Kim, Dai H.

1997-10-01

118

Predicting mining activity with parallel genetic algorithms  

USGS Publications Warehouse

We explore several different techniques in our quest to improve the overall model performance of a genetic algorithm calibrated probabilistic cellular automata. We use the Kappa statistic to measure correlation between ground truth data and data predicted by the model. Within the genetic algorithm, 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.

2005-01-01

119

Massive Multimodality, Deception, and Genetic Algorithms  

Microsoft Academic Search

This paper considers the use of genetic algorithms (GAs) for the solution of problems that are bothaverage-sense misleading (deceptive) and massively multimodal. An archetypical multimodal-deceptiveproblem, here called a bipolar deceptive problem, is defined and two generalized constructions of suchproblems are reviewed, one using reflected trap functions and one using low-order Walsh coefficients;sufficient conditions for bipolar deception are also reviewed. The

David E. Goldberg; Kalyanmoy Deb; Jeffrey Horn

1992-01-01

120

Selective Breeding in a Multiobjective Genetic Algorithm  

Microsoft Academic Search

This paper describes an investigation of the efficacy of various elitist selection strategies in a multiobjective Genetic\\u000a Algorithm implementation, with parents being selected both from the current population and from the archive record of nondominated\\u000a solutions encountered during search. It is concluded that, because the multiobjective optimization process naturally maintains\\u000a diversity in the population, it is possible to improve the

Geoffrey T. Parks; I. Miller

1998-01-01

121

Environmental Optimization: Applications of Genetic Algorithms  

Microsoft Academic Search

The genetic algorithm (GA) has found wide acceptance in many fields, ranging from economics through engineering. In the environmental\\u000a sciences, some disciplines are using GAs regularly as a tool to solve typical problems; while in other areas, they have hardly\\u000a been assessed for use in research projects. The key to using GAs in environmental sciences is to pose the problem

Sue Ellen Haupt

122

Designing Wastewater Collection Systems Using Genetic Algorithms  

Microsoft Academic Search

This paper presents procedures that automate the design of wastewater collection systems. The application of these procedures\\u000a in determining the optimal configuration of pipeline networks design is described. Genetic algorithms (GA) are used to identify\\u000a good feasible pipeline networks. GA use a number of operators including, reproduction, crossover and mutation to solve complex\\u000a search and optimization problems. A number of

Lou Y. Liang; Russell G. Thompson; David M. Young

2000-01-01

123

Complex motion measurement using genetic algorithm  

NASA Astrophysics Data System (ADS)

Genetic algorithm (GA) is an optimization technique that provides an untraditional approach to deal with many nonlinear, complicated problems. The notion of motion measurement using genetic algorithm arises from the fact that the motion measurement is virtually an optimization process based on some criterions. In the paper, we propose a complex motion measurement method using genetic algorithm based on block-matching criterion. The following three problems are mainly discussed and solved in the paper: (1) apply an adaptive method to modify the control parameters of GA that are critical to itself, and offer an elitism strategy at the same time (2) derive an evaluate function of motion measurement for GA based on block-matching technique (3) employ hill-climbing (HC) method hybridly to assist GA's search for the global optimal solution. Some other related problems are also discussed. At the end of paper, experiments result is listed. We employ six motion parameters for measurement in our experiments. Experiments result shows that the performance of our GA is good. The GA can find the object motion accurately and rapidly.

Shen, Jianjun; Tu, Dan; Shen, Zhenkang

1997-12-01

124

Optical flow optimization using parallel genetic algorithm  

NASA Astrophysics Data System (ADS)

A new approach to optimize the parameters of a gradient-based optical flow model using a parallel genetic algorithm (GA) is proposed. The main characteristics of the optical flow algorithm are its bio-inspiration and robustness against contrast, static patterns and noise, besides working consistently with several optical illusions where other algorithms fail. This model depends on many parameters which conform the number of channels, the orientations required, the length and shape of the kernel functions used in the convolution stage, among many more. The GA is used to find a set of parameters which improve the accuracy of the optical flow on inputs where the ground-truth data is available. This set of parameters helps to understand which of them are better suited for each type of inputs and can be used to estimate the parameters of the optical flow algorithm when used with videos that share similar characteristics. The proposed implementation takes into account the embarrassingly parallel nature of the GA and uses the OpenMP Application Programming Interface (API) to speedup the process of estimating an optimal set of parameters. The information obtained in this work can be used to dynamically reconfigure systems, with potential applications in robotics, medical imaging and tracking.

Zavala-Romero, Olmo; Botella, Guillermo; Meyer-Bäse, Anke; Meyer Base, Uwe

2011-05-01

125

Optimal hydrogenerator governor tuning with a genetic algorithm  

SciTech Connect

Many techniques exist for developing optimal controllers. This paper investigates genetic algorithms as a means of finding optimal solutions over a parameter space. In particular, the genetic algorithm is applied to optimal tuning of a governor for a hydrogenerator plant. Analog and digital simulation methods are compared for use in conjunction with the genetic algorithm optimization process. It is shown that analog plant simulation provides advantages in speed over digital plant simulation. This speed advantage makes application of the genetic algorithm in an actual plant environment feasible. Furthermore, the genetic algorithm is shown to possess the ability to reject plant noise and other system anomalies in its search for optimizing solutions.

Lansberry, J.E. (Illinois Univ., Urbana, IL (United States). Dept. of Electrical and Computer Engineering); Wozniak, L.; Goldberg, D.E. (Illinois Univ., Urbana, IL (United States). Dept. of General Engineering)

1992-12-01

126

Path planning for a mobile robot using genetic algorithms  

Microsoft Academic Search

This paper presents a new algorithm for global path planning to a goal for a mobile robot using Genetic Algorithm (GA). A genetic algorithm is used to find the optimal path for a mobile robot to move in a static environment expressed by a map with nodes and links. Locations of target and obstacles to find an optimal path are

Gihan NAGIB; W. Gharieb

2004-01-01

127

A Micro-Genetic Algorithm for Multiobjective Optimization  

Microsoft Academic Search

In this paper, a new evolutionary multiobjective optimization algorithm is proposed. The approach is based on a micro genetic algorithm (micro-GA) which is a genetic algorithm with a very small population (four individuals were used in our experiment) and a reinitialization process. We use three forms of elitism and a memory to generate the initial population of the micro-GA. Our

Carlos Coello Coello Coello; Gregorio Toscano Pulido

2001-01-01

128

On the applicability of genetic algorithms to protein folding  

Microsoft Academic Search

Discusses the protein folding problem and suggests the use of genetic algorithms for protein folding simulations. The issues of protein energy functions, search algorithms, and folding pathways are discussed. The authors review the current approaches to the protein folding problem, point out the limitations of the approaches, and present the genetic algorithm method, which is based on viewing evolution as

Ron Unger; J. Moult

1993-01-01

129

Immune Genetic Algorithm for Vehicle Routing Problem with Time Windows  

Microsoft Academic Search

Focused on the VRPTW (vehicle routing problem with time windows) and based on SGA (simple genetic algorithm), this paper employs a new IGA (immune genetic algorithm) to solve the VRPTW through using immune operator. This algorithm based on the global searching method of SGA, and using the diversity preservation strategy of antibodies in biology immunity mechanism, the method greatly improves

Jia Ma; Hao Zou; Li-Qun Gao; Dan Li

2006-01-01

130

Trajectory planning of redundant manipulators using genetic algorithms  

NASA Astrophysics Data System (ADS)

The trajectory planning of redundant robots is an important area of research and efficient optimization algorithms are needed. This paper presents a new technique that combines the closed-loop pseudoinverse method with genetic algorithms. The results are compared with a genetic algorithm that adopts the direct kinematics. In both cases the trajectory planning is formulated as an optimization problem with constraints.

da Graça Marcos, Maria; Tenreiro Machado, J. A.; Azevedo-Perdicoúlis, T.-P.

2009-07-01

131

Modeling of Genetic Algorithms with a Finite Population.  

National Technical Information Service (NTIS)

Cross-competition between non-overlapping building blocks can strongly influence the performance of evolutionary algorithms. The choice of the selection scheme can have a strong influence on the performance of a genetic algorithm. This paper describes a n...

C. H. M. Van Kemenade

1997-01-01

132

Saving Resources with Plagues in Genetic Algorithms  

SciTech Connect

The population size of genetic algorithms (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 genetic algorithms. 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

2004-06-15

133

Genetic algorithms for modelling and optimisation  

NASA Astrophysics Data System (ADS)

Genetic algorithms (GAs) are a heuristic search and optimisation technique inspired by natural evolution. They have been successfully applied to a wide range of real-world problems of significant complexity. This paper is intended as an introduction to GAs aimed at immunologists and mathematicians interested in immunology. We describe how to construct a GA and the main strands of GA theory before speculatively identifying possible applications of GAs to the study of immunology. An illustrative example of using a GA for a medical optimal control problem is provided. The paper also includes a brief account of the related area of artificial immune systems.

McCall, John

2005-12-01

134

Parallel Genetic Algorithm for Alpha Spectra Fitting  

NASA Astrophysics Data System (ADS)

We present a performance study of alpha-particle spectra fitting using parallel Genetic Algorithm (GA). The method uses a two-step approach. In the first step we run parallel GA to find an initial solution for the second step, in which we use Levenberg-Marquardt (LM) method for a precise final fit. GA is a high resources-demanding method, so we use a Beowulf cluster for parallel simulation. The relationship between simulation time (and parallel efficiency) and processors number is studied using several alpha spectra, with the aim of obtaining a method to estimate the optimal processors number that must be used in a simulation.

García-Orellana, Carlos J.; Rubio-Montero, Pilar; González-Velasco, Horacio

2005-01-01

135

Modeling of Nonlinear Systems using Genetic Algorithm  

NASA Astrophysics Data System (ADS)

In this paper, a newly modeling system by using Genetic Algorithm (GA) is proposed. The GA is an evolutionary computational method that simulates the mechanisms of heredity or evolution of living things, and it is utilized in optimization and in searching for optimized solutions. Most process systems have nonlinearities, so it is necessary to anticipate exactly such systems. However, it is difficult to make a suitable model for nonlinear systems, because most nonlinear systems have a complex structure. Therefore the newly proposed method of modeling for nonlinear systems uses GA. Then, according to the newly proposed scheme, the optimal structure and parameters of the nonlinear model are automatically generated.

Hayashi, Kayoko; Yamamoto, Toru; Kawada, Kazuo

136

Some issues of designing genetic algorithms for traveling salesman problems  

Microsoft Academic Search

This paper demonstrates that a robust genetic algorithm for the traveling salesman problem (TSP) should preserve and add good edges efficiently, and at the same time, maintain the population diversity well. We analyzed the strengths and limitations of several well-known genetic operators for TSPs by the experiments. To evaluate these factors, we propose a new genetic algorithm integrating two genetic

Huai-kuang Tsai; Jinn-moon Yang; Yuan-fan Tsai; Cheng-yan Kao

2004-01-01

137

Genetic algorithm and particle swarm optimization combined with Powell method  

NASA Astrophysics Data System (ADS)

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 genetic algorithm - Genetic Algorithm 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

2013-10-01

138

Genetic Algorithms for Genetic Neural Nets.  

National Technical Information Service (NTIS)

In contrast to most synthetic neural nets, biological neural networks have a strong component of genetic determination which acts before and during experiential learning. Three broad levels of phenomena are present: long-term evolution, involving crossove...

D. H. Sharp J. Reinitz E. Mjolsness

1991-01-01

139

Research on Algorithm of Conflict Detection and Resolution in Free Flight Based on Genetic Algorithms  

Microsoft Academic Search

The research background of free flight is discussed, the substance of the conflict detection and resolution in free flight based on genetic algorithms is studied, and a mathematical model is constructed in the paper. The emulation results of applications show that the genetic algorithms is suitable and positive to detect and resolute the conflict in free flight. The flight courses

Hongping Shu; Cao Liang; Zhenming Xu; Liu Kui

2009-01-01

140

Birefringent filter design by use of a modified genetic algorithm.  

PubMed

A modified genetic algorithm is proposed for the optimization of fiber birefringent filters. The orientation angles and the element lengths are determined by the genetic algorithm to minimize the sidelobe levels of the filters. Being different from the normal genetic algorithm, the algorithm proposed reduces the problem space of the birefringent filter design to achieve faster speed and better performance. The design of 4-, 8-, and 14-section birefringent filters with an improved sidelobe suppression ratio is realized. A 4-section birefringent filter designed with the algorithm is experimentally realized. PMID:16761031

Wen, Mengtao; Yao, Jianping

2006-06-10

141

Instrument design and optimization using genetic algorithms  

NASA Astrophysics Data System (ADS)

This article describes the design of highly complex physical instruments by using a canonical genetic algorithm (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 genetic algorithm, 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.

Hölzel, Robert; Bentley, Phillip M.; Fouquet, Peter

2006-10-01

142

Adaptive sensor tasking using genetic algorithms  

NASA Astrophysics Data System (ADS)

Today's battlefield environment contains a large number of sensors, and sensor types, onboard multiple platforms. The set of sensor types includes SAR, EO/IR, GMTI, AMTI, HSI, MSI, and video, and for each sensor type there may be multiple sensing modalities to select from. In an attempt to maximize sensor performance, today's sensors employ either static tasking approaches or require an operator to manually change sensor tasking operations. In a highly dynamic environment this leads to a situation whereby the sensors become less effective as the sensing environments deviates from the assumed conditions. Through a Phase I SBIR effort we developed a system architecture and a common tasking approach for solving the sensor tasking problem for a multiple sensor mix. As part of our sensor tasking effort we developed a genetic algorithm based task scheduling approach and demonstrated the ability to automatically task and schedule sensors in an end-to-end closed loop simulation. Our approach allows for multiple sensors as well as system and sensor constraints. This provides a solid foundation for our future efforts including incorporation of other sensor types. This paper will describe our approach for scheduling using genetic algorithms to solve the sensor tasking problem in the presence of resource constraints and required task linkage. We will conclude with a discussion of results for a sample problem and of the path forward.

Shea, Peter J.; Kirk, Joe; Welchons, Dave

2007-04-01

143

Automatic Design of Hierarchical Fuzzy Controllers Using Genetic Algorithms  

Microsoft Academic Search

An automatic design method for hierarchical fuzzy controllers using genetic algorithms isproposed. A reorder operator for the genetic algorithm is introduced. We applied the methodto the problem of controlling an autonomous vehicle with the task to reach a given locationand avoiding obstacles on the way.Keywords: Fuzzy Control, Genetic Algorithms, Autonomous Vehicle1 IntroductionDesign of linguistic variables and rules of a rule-based

Frank Hoffmann; Gerd Pfister

1994-01-01

144

Real-Coded Genetic Algorithms Based on Mathematical Morphology  

Microsoft Academic Search

The goal of this work is to propose a general-purpose crossover operator for real-coded genetic algorithms that is able to\\u000a avoid the major problems found in this kind of approach such as the premature convergence to local optima, the weakness of\\u000a genetic algorithms in local fine-tuning and the use of real-coded genetic algorithms instead of the traditional binary-coded\\u000a problems. Mathematical

Dolores Barrios; Daniel Manrique; Jaime Porras; Juan Rios

2000-01-01

145

Optimal support arrangement of piping systems using genetic algorithm  

SciTech Connect

The support arrangement is one of the important factors in the design of piping systems. Much time is required to decide the arrangement of the supports. The authors applied a genetic algorithm to find the optimum support arrangement for piping systems. Examples are provided to illustrate the effectiveness of the genetic algorithm. Good results are obtained when applying the genetic algorithm to the actual designing of the piping system.

Chiba, T.; Okado, S.; Fujii, I.; Itami, K. [Ishikawajima-Harima Heavy Industries Co., Ltd., Yokohama (Japan). Nuclear Power Div.; Hara, F. [Science Univ. of Tokyo (Japan)

1996-11-01

146

Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions  

Microsoft Academic Search

The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms, that dynamically adjust selected control parameters or genetic operators during the evolution have been built. Their objective is to offer the most appropriate exploration and exploitation behaviour to avoid the premature conver- gence problem and improve the final results. One of

Francisco Herrera; Manuel Lozano

2003-01-01

147

BASIC - A genetic algorithm for engineering problems solution  

Microsoft Academic Search

This paper introduces in details a genetic algorithm-called BASIC, which is designed to take advantage of well known genetic schemes so as to be able to deal with numerous optimization problems. BASIC GA follows all common steps of the genetic algorithms. It involves real representation schemes for both real and integer variables. Three biased selection schemes for reproduction; four for

Elisaveta G. Shopova; Natasha G. Vaklieva-bancheva

2006-01-01

148

A New Challenge for Compression Algorithms: Genetic Sequences.  

ERIC Educational Resources Information Center

|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…

Grumbach, Stephane; Tahi, Fariza

1994-01-01

149

Genetic engineering versus natural evolution: Genetic algorithms with deterministic operators  

Microsoft Academic Search

Genetic algorithms (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

Lech Józwiak; Adam Postula

2002-01-01

150

Boiler-turbine control system design using a genetic algorithm  

SciTech Connect

This paper discusses the application of a genetic algorithm to control system design for a boiler-turbine plant. In particular the authors study the ability of the genetic algorithm to develop a proportional-integral (PI) controller and a state feedback controller for a non-linear multi-input/multi-output (MIMO) plant model. The plant model is presented along with a discussion of the inherent difficulties in such controller development. A sketch of the genetic algorithm (GA) is presented and its strategy as a method of control system design is discussed. Results are presented for two different control systems that have been designed with the genetic algorithm.

Dimeo, R.; Lee, K.Y. [Pennsylvania State Univ., University Park, PA (United States). Dept. of Electrical Engineering

1995-12-01

151

Multidisciplinary design optimization using genetic algorithms  

NASA Astrophysics Data System (ADS)

Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared with efficient gradient methods. Applicaiton of GA is underway for a cost optimization study for a launch-vehicle fuel-tank and structural design of a wing.

Unal, Resit

1994-12-01

152

A genetic algorithm for solving supply chain network design model  

NASA Astrophysics Data System (ADS)

Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.

Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.

2013-09-01

153

Clinical pathways scheduling using hybrid genetic algorithm.  

PubMed

In order to improve the standard of management in hospitals and effectively control the cost of clinical treatments, this research primarily focuses on optimizing the scheduling of clinical pathways (CPs). A mathematical model for CP scheduling is constructed, and the hybrid genetic algorithm (HGA, combining a genetic algorithm with particle swarm optimization) is proposed for solving this problem so as to distribute medical resources and schedule the treatments of patients reasonably and effectively. The optimal search space can be further enlarged by introducing a new mutation mechanism, which allows a more satisfactory solution to be found. In particular, the relative patient waiting time and relative time efficiency are used as measure indexes, which are more scientific and effective than the usual indexes of absolute time and absolute time efficiency. After comparing absolute waiting time, relative waiting time, utilization of absolute waiting time, and utilization of relative waiting time waiting respectively, the conclusion can confidently be drawn that task scheduling obviously enhances patients' time efficiency, reduces time wastage and therefore promotes patient satisfaction with medical processes. Moreover, the patients can to a certain degree move away from their usual passive role in medical processes by using this scheduling system. In order to further validate the rationality and validity of the proposed method, the heuristic rules for CP scheduling are also tested using the same case. The results demonstrate that the proposed HGA achieves superior performance, in terms of precision, over those heuristic rules for CP scheduling. Therefore, we utilize HGA to optimize CP scheduling, thus providing a decision-making mechanism for medical staff and enhancing the efficiency of medical processes. This research has both theoretical and practical significance for electronic CP management, in particular. PMID:23576080

Du, Gang; Jiang, Zhibin; Yao, Yang; Diao, Xiaodi

2013-04-11

154

Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms  

Microsoft Academic Search

This paper presents a methodology for using heuristic search methods to optimise cancer chemotherapy. Specifically, two evolutionary algorithms - Population Based Incremental Learn- ing (PBIL), which is an Estimation of Distribution Algo- rithm (EDA), and Genetic Algorithms (GAs) have been ap- plied to the problem of finding effective chemotherapeutic treatments. To our knowledge, EDAs have been applied to fewer real

Andrei Petrovski; Siddhartha Shakya; John Mccall

2006-01-01

155

A Decision Tree Scoring Model Based on Genetic Algorithm and K-Means Algorithm  

Microsoft Academic Search

Credit scoring has been regarded as a critical topic and studied extensively in the finance field. Many artificial intelligence techniques have been used to solve credit scoring. The paper is to build a classification model based on a decision tree by learning historical data. Clustering algorithm and genetic algorithm are combined to further improve the accuracy of this credit scoring

Defu Zhang; S. C. H. Leung; Zhimei Ye

2008-01-01

156

A Performance Analysis of Compressed Compact Genetic Algorithm  

Microsoft Academic Search

Compressed compact genetic algorithm (c2GA) is an algorithm that utilizes the compressed chromo- some encoding and compact genetic algorithm (cGA). The advantage of c2GA is to reduce the memory us- age by representing population as a probability vec- tor. In this paper, we analyze the performance in term of robustness of c2GA. Since the compression and decompression strategy employ two

Orawan Watchanupaporn

2006-01-01

157

Research on compact genetic algorithm in continuous domain  

Microsoft Academic Search

Compact genetic algorithm (CGA) is a successful probability-based evolutionary algorithm which performs equivalent to the order-one behavior of the simple genetic algorithm (SGA) with uniform crossover. However, this equivalence only applies for binary encoded problems. To extend the basic concept of CGA to continuous domain, an improved CGA is proposed in this paper. We established a continuous CGA (cCGA) model

Guojun Shi; Qingsheng Ren

2008-01-01

158

Integrated discrete and configuration optimization of trusses using genetic algorithms  

Microsoft Academic Search

The application of genetic algorithms to integrated discrete and configuration optimization of trusses is presented. It is mathematically formulated as a constrained nonlinear optimization problem with a mix of discrete sizing and continuous configuration variables. The components of genetic algorithms are described. The discrete sizing variables are treated by constructing mapping relationships between binary digit strings and discrete values by

Shyue-Jian Wu; Pei-Tse Chow

1995-01-01

159

Improving flexibility and efficiency by adding parallelism to genetic algorithms  

Microsoft Academic Search

In this paper we develop a study on several types of parallel genetic algorithms (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 genetic algorithms. Afterwards, this study is extended to consider a

Enrique Alba; José M. Troya

2002-01-01

160

State assignment of finite state machines using a genetic algorithm  

Microsoft Academic Search

The use of Genetic Algorithms for the generation of optimal state assignments forsynchronous finite state machines (FSM) is proposed. Results are presented to show that in allexamples attempted the resulting state assignments are better than or at least as good as thoseproduced by SPECTRAL, NOVA and MUSTANG and also closed partition assignments. Onaverage the genetic algorithm produced assignments with 33%

A. E. A. Almaini; J. F. Miller; P. Thomson; S. Billina

1995-01-01

161

A "Hands on" Strategy for Teaching Genetic Algorithms to Undergraduates  

ERIC Educational Resources Information Center

|Genetic algorithms (GAs) are a problem solving strategy that uses stochastic search. Since their introduction (Holland, 1975), GAs have proven to be particularly useful for solving problems that are "intractable" using classical methods. The language of genetic algorithms (GAs) is heavily laced with biological metaphors from evolutionary…

Venables, Anne; Tan, Grace

2007-01-01

162

Genetic algorithm optimization for aerospace electromagnetic design and analysis  

Microsoft Academic Search

This paper provides a tutorial overview of a new approach to optimization for aerospace electromagnetics known as the Genetic Algorithm. Genetic Algorithm (GA) optimizers are robust, stochastic search methods modeled on the concepts of natural selection and evolution. The relationship between traditional optimization techniques and GA is discussed and the details of GA optimization implementation are explored. The tutorial overview

J. Michael Johnson; Yahya Rahmat-Samii

1996-01-01

163

Genetic algorithm based scheduling in a dynamic manufacturing environment  

Microsoft Academic Search

The application of adaptive optimization methods to production scheduling has recently become a research topic of broad interest. Genetic algorithm approaches to scheduling concentrate on static problems, whereas real world scheduling tends to be dynamic. The paper briefly outlines the application of a genetic algorithm to the dynamic job shop problem. In a second step the job shop is regarded

Christian Bierwirth; Herbert Kopfer; Dirk C. Mattfeld; Ivo Rixen

1995-01-01

164

Design of Analog Integrated Circuits by Using Genetic Algorithm  

Microsoft Academic Search

This paper applies the Artificial Intelligence technique called Genetic Algorithm (GA) to perform analog integrated circuits design, synthesis and optimization in order to reduce the development time and increase precision of this kind of circuits. In this work an accurate method to determine the device sizes in an analog integrated circuit on the basis of genetic algorithm is presented. To

A. Jafari; M. Zekri; S. Sadri; A. R. Mallahzadeh

2010-01-01

165

Multiobjective Genetic Algorithms with Application to Control Engineering Problems  

Microsoft Academic Search

Constraint handling with genetic algorithms is then developed from a decision making perspective and characterized, with application to control system design in mind. Related genetic algorithm issues, such as the ability to maintain diverse solutions along the trade-off surface and responsiveness to on-line changes in decision policy, are also considered. The application of the multiobjective GA to three realistic problems

Carlos Manuel Mira Da Fonseca

1995-01-01

166

Optimal design of plant lighting system by genetic algorithms  

Microsoft Academic Search

A genetic algorithm technique is developed for the optimal design of a supplemental lighting system for greenhouse crop production. The approach uses the evolutionary parallel search capabilities of genetic algorithms to design the pattern layout of the lamps (luminaires), their mounting heights and their wattages. The total number and the exact positions of luminaires are not predefined (even though possible

Konstantinos P. Ferentinos; L. D. Albright

2005-01-01

167

SVPCGA: Selection on virtual population based compact genetic algorithm  

Microsoft Academic Search

This paper describes a novel virtual population based truncation selection operator that extends our previously proposed virtual population based tournament selection operator. Moreover, two extensions of compact genetic algorithm (CGA) that make use of virtual population based selection operators are presented in this paper: one is the tournament selection on virtual population based compact genetic algorithm (SVPCGA-TO); the other is

Yi Hong; Sam Kwong; Hanli Wang; Zhihui Xie; Qingsheng Ren

2008-01-01

168

An Architecture for Massive Parallelization of the Compact Genetic Algorithm  

Microsoft Academic Search

This paper presents an architecture which is suitable for a massive parallelization of the compact genetic algorithm. The approach is scalable, has low synchronization costs, and is fault tolerant. The pa- per argues that the benets that can be obtained with the proposed methodology is potentially higher than those obtained with traditional parallel genetic algorithms.

Fernando G. Lobo; Cláudio F. Lima; Hugo Martires

2004-01-01

169

Accelerated Image Halftoning Technique Using Improved Genetic Algorithm  

Microsoft Academic Search

SUMMARY This paper presents an accelerated image halftoning technique using an improved genetic algorithm with tiny populations. The algorithm is based on a new coopera- tive model for genetic operators in GA. Two kinds of opera- tors are used in parallel to produce offspring: (i) SRM (Self- Reproduction with Mutation) to introduce diversity by means of Adaptive Dynamic-Block (ADB) mutation

Hernan AGUIRRE; Kiyoshi TANAKA; Tatsuo SUGIMURA

2000-01-01

170

Optimizing the construction relations of chiral slab via genetic algorithm  

Microsoft Academic Search

A novel method of optimizing the continuous variable constitutive relation of multi-layered chiral slab based on genetic algorithm was discussed in this paper. This method derived continuous variable constitutive relations automatically, then calculated the reflection coefficient by wave-splitting method. Furthermore the genetic algorithm was used to search the maximum absorbability of the chiral slab with certain thickness

Y. C. Gao; Jian Hua Xu; De Shuang Zhao; Shu Zhang Liu

2000-01-01

171

Application of genetic algorithm for optimization gasoline fractions blending compounding  

Microsoft Academic Search

The paper deals with the application of a genetic algorithm for the solution of the task of gasoline fraction blending compounding, keeping the given conditions on octane numbers and the amount of given types of commodity gasolines. The principle of coding solutions is described. Several results of experiments on the determination of the effective genetic algorithm configuration are given.

J. A. Burgher; V. S. Vyshemirskij; N. A. Sokolova

2002-01-01

172

A Test of Genetic Algorithms in Relevance Feedback.  

ERIC Educational Resources Information Center

|Discussion of information retrieval, query optimization techniques, and relevance feedback focuses on genetic algorithms, which are derived from artificial intelligence techniques. Describes an evaluation of different genetic algorithms 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

2002-01-01

173

Generating test patterns for VLSI circuits using a genetic algorithm  

Microsoft Academic Search

The authors present the development of a technique that uses genetic algorithms for the generation of test patterns that detect single stuck-at faults in combinational VLSI circuits. As the genetic algorithm evolves, an efficient set of test patterns are produced, by searching the solution space for patterns that detect the highest number of remaining faults in the fault list.

M. J. O'Dare; T. Arslan

1994-01-01

174

Design of matching transformers for UHF band power splitters using modified genetic algorithms  

Microsoft Academic Search

In this work, a matching transmission line for a 1:4 power splitter in the UHF band is designed using modified genetic algorithms (GA). Some viewpoints regarding an improvement of the continuous genetic algorithm as well as the conventional genetic algorithm, namely the discrete genetic algorithm, is presented. Several different techniques are presented for improving the discrete\\/continuous genetic algorithm.

A. Varahram; J. Rashed-Mohassel; K. Mafinezhad

2003-01-01

175

Stochastic reservoir modeling using simulated annealing and genetic algorithms  

SciTech Connect

This paper discusses and compares three different algorithms based on combinatorial optimization schemes for generating stochastic permeability fields. The algorithms are not restricted to generating Gaussian random fields and have the potential to accomplish geologic realism by combining data from many different sources. The authors have introduced a ``heat-bath`` algorithm for simulated annealing (SA) as an alternative to the commonly used ``Metropolis`` algorithm and a new stochastic modeling technique based on the ``genetic`` algorithm. The authors applied these algorithms to a set of outcrop and tracer flow data and examined the associated uncertainties in predictions. All three algorithms reproduce the major features of permeability distribution and fluid flow data. For relatively small problems, the Metropolis algorithm is the fastest. For larger problems, the heat-bath algorithm is at least as fast and often faster than the Metropolis algorithm with significant potential for parallelization. The performance of the genetic algorithm is highly dependent on the choice of population size and probabilities of crossover, update, and mutation.

Sen, M.K.; Stoffa, P.L.; Lake, L.W.; Pope, G.A. [Univ. of Texas, Austin, TX (United States); Datta-Gupta, A. [Texas A and M Univ., College Station, TX (United States)

1995-03-01

176

Adaptive hydrogenerator governor tuning with a genetic algorithm  

SciTech Connect

An important aspect of a good control algorithm is robustness with respect to changing plant parameters. Adaptive control strategies attempt to address the robustness issue by optimizing control parameters as changes occur in the plant. This paper investigates the genetic algorithm as one possible means of adaptively optimizing the gains of a proportional-plus-integral hydrogenerator governor. Previous work has shown that the genetic algorithm can effectively optimize the control parameters for a fixed plant. An enhanced version of the genetic algorithm, using the notions of diploidy and dominance, can be used to address the robustness issue. Here, changes in the conduit time constant as well as load constant are considered. It is shown that the genetic algorithm can effectively follow changes in the plant parameters, producing optimal control parameters in an adaptive environment.

Lansberry, J.E.; Wozniak, L. (Univ. of Illinois, Urbana, IL (United States))

1994-03-01

177

A Genetic Algorithm for Solving Job-shop Scheduling Problems using the Parameter-free Genetic Algorithm  

NASA Astrophysics Data System (ADS)

A new genetic algorithm is proposed for solving job-shop scheduling problems where the total number of search points is limited. The objective of the problem is to minimize the makespan. The solution is represented by an operation sequence, i.e., a permutation of operations. The proposed algorithm is based on the framework of the parameter-free genetic algorithm. It encodes a permutation using random keys into a chromosome. A schedule is derived from a permutation using a hybrid scheduling (HS), and the parameter of HS is also encoded in a chromosome. Experiments using benchmark problems show that the proposed algorithm outperforms the previously proposed algorithms, genetic algorithm by Shi et al. and the improved local search by Nakano et al., for large-scale problems under the constraint of limited number of search points.

Matsui, Shouichi; Watanabe, Isamu; Tokoro, Ken-Ichi

178

Random Volumetric MRI Trajectories via Genetic Algorithms  

PubMed Central

A pseudorandom, velocity-insensitive, volumetric k-space sampling trajectory is designed for use with balanced steady-state magnetic resonance imaging. Individual arcs are designed independently and do not fit together in the way that multishot spiral, radial or echo-planar trajectories do. Previously, it was shown that second-order cone optimization problems can be defined for each arc independent of the others, that nulling of zeroth and higher moments can be encoded as constraints, and that individual arcs can be optimized in seconds. For use in steady-state imaging, sampling duty cycles are predicted to exceed 95 percent. Using such pseudorandom trajectories, aliasing caused by under-sampling manifests itself as incoherent noise. In this paper, a genetic algorithm (GA) is formulated and numerically evaluated. A large set of arcs is designed using previous methods, and the GA choses particular fit subsets of a given size, corresponding to a desired acquisition time. Numerical simulations of 1 second acquisitions show good detail and acceptable noise for large-volume imaging with 32 coils.

Curtis, Andrew Thomas; Anand, Christopher Kumar

2008-01-01

179

Crack Identification of Plates Using Genetic Algorithm  

NASA Astrophysics Data System (ADS)

In this paper, a method for identifying of a crack in a plate that uses a genetic algorithm (GA) based on changes in natural frequencies is presented. To calculate the natural frequencies of the cracked plates, a FEM (Finite Element Method) program, which is based on the BFM (Bogner, Fox and Schmidt) model, is developed since the accuracy of the forward solver is important. In the analysis, two types of cracks, i.e., internal and edge cracks are considered. To identify the crack location and the depth from frequency measurements, the width and position of the crack in a plate are coded into a fixed-length binary digit string. Using GA, the square sum of residuals between the measured data and the calculated data is minimized in the identification process and thus the crack is identified. To avoid a high calculation cost, the response surface method (RSM) is also adopted in the minimizing process. The combination of GA and RSM makes the identification more effective and robust. The applicability of the proposed method is confirmed by the results of numerical simulation.

Horibe, Tadashi; Watanabe, Kensuke

180

PM3(tm) parameterization using genetic algorithms  

SciTech Connect

PM3(tm) has great potential in studying transition metals because of its speed and applicability to large complexes. However, its parameterization is not yet available for all 30 d-block metals. In this research, genetic algorithms (GAs) were evaluated for the development of PM3(tm) parameters for technetium (Tc). Initial Tc parameters were obtained by interpolation of parameters for the metals flanking it in the periodic table--molybdenum and ruthenium. Prototypical Tc compounds were chosen from the Cambridge Structural Database. The sensitivities of the 21 PM3(tm) parameters were tested using different methods and their impact on molecular geometry assessed. The fitness criterion was based on the root mean square (rms) of the distance matrix between calculated and crystal structures. The GA-optimized parameters improved the calculated structural accuracy by more than 50% versus interpolated parameters. In addition, structural prediction (bond lengths within 0.04 {angstrom}, bond angles within 2{degree}, dihedral angles with 4{degree}) with the GA-developed parameters for Tc is competitive with those already available in PM3(tm) and with that expected from high-level ab initio calculations, but in a fraction of the time.

Cundari, T.R.; Deng, J.; Fu, W.

2000-03-05

181

Improved classification accuracy by feature extraction using genetic algorithms  

NASA Astrophysics Data System (ADS)

A feature extraction algorithm has been developed for the purposes of improving classification accuracy. The algorithm uses a genetic algorithm / hill-climber hybrid to generate a set of linearly recombined features, which may be of reduced dimensionality compared with the original set. The genetic algorithm performs the global exploration, and a hill climber explores local neighborhoods. Hybridizing the genetic algorithm with a hill climber improves both the rate of convergence, and the final overall cost function value; it also reduces the sensitivity of the genetic algorithm to parameter selection. The genetic algorithm includes the operators: crossover, mutation, and deletion / reactivation - the last of these effects dimensionality reduction. The feature extractor is supervised, and is capable of deriving a separate feature space for each tissue (which are reintegrated during classification). A non-anatomical digital phantom was developed as a gold standard for testing purposes. In tests with the phantom, and with images of multiple sclerosis patients, classification with feature extractor derived features yielded lower error rates than using standard pulse sequences, and with features derived using principal components analysis. Using the multiple sclerosis patient data, the algorithm resulted in a mean 31% reduction in classification error of pure tissues.

Patriarche, Julia; Manduca, Armando; Erickson, Bradley J.

2003-05-01

182

Genetic algorithms for genetic neural nets. Research report  

SciTech Connect

In contrast to most synthetic neural nets, biological neural networks have a strong component of genetic determination which acts before and during experiential learning. Three broad levels of phenomena are present: long-term evolution, involving crossover as well as point mutation; a developmental process mapping genetic information to a set of cells and their internal states of gene expression (genotype to phenotype); and the subsequent synaptogenesis. We describe a very simple mathematical idealization of these three levels which combines the crossover search method of genetic algorithms with the developmental models used in our previous work on 'genetic' or 'recursively generated' artificial neural nets and elaborated into a connectionist model of biological development. Despite incorporating all three levels (evolution on genes; development of cells; synapse formation) the model may actually be far cheaper to compute with than a comparable search directly in synaptic weight space.

Sharp, D.H.; Reinitz, J.; Mjolsness, E.

1991-01-01

183

Genetic Local Search Algorithms for the Travelling Salesman Problem  

Microsoft Academic Search

We briefly review previous attempts to generate near-optimal solutions of the Traveling Salesman Problem by applying Genetic Algorithms. Following the lines of Johnson [1990] we discuss some possibilities for speeding up classical Local Search algorithms by casting them into a genetic frame. In an experimental study two such approaches, viz. Genetic Local Search with 2-Opt neighbourhoods and Lin-Kernighan neighbourhoods, respectively,

Nico L. J. Ulder; Emile H. L. Aarts; Hans-jürgen Bandelt; Peter J. M. Van Laarhoven; Erwin Pesch

1990-01-01

184

A Linkage Learning Genetic Algorithm with Linkage Matrix  

Microsoft Academic Search

The goal of linkage learning, or building block identification, is the creation of a more effective Genetic Algorithm (GA). This paper proposes a new Linkage Learning Genetic Algorithms, named m-LLGA. With the linkage learning module and the linkage-based genetic operation, m-LLGA is not only able to learn and record the linkage information among genes without any prior knowledge of the

ZUO Guo-yu; GONG Dao-xiong; RUAN Xiao-gang

185

Optimization and improvement of Genetic Algorithms solving Traveling Salesman Problem  

Microsoft Academic Search

Traveling salesman problem (TSP) is a typical NP-complete problem, of which the search space increases with the number of cities. Genetic algorithm (GA) is an efficient optimization algorithm characterized with explicit parallelism and robustness, applicable to TSP. In this paper, we compare the performance of the existing GAs in searching the solution for TSP and find a superior combination of

Liping Zhang; Min Yao; Nenggan Zheng

2009-01-01

186

Genetic algorithm based path planning for a mobile robot  

Microsoft Academic Search

In this paper, a novel genetic algorithm based approach to path planning of a mobile robot is proposed. The major characteristic of the proposed algorithm is that the chromosome has a variable length. The location target and obstacles are included to find a path for a mobile robot in an environment that is a 2D workplace discretized into a grid

Jianping Tu; Simon X. Yangt

2003-01-01

187

Application of genetic algorithm to bridge construction management  

Microsoft Academic Search

Genetic algorithms (GA) have attracted attention in recent years as a method of solving combinatorial optimization problems. This research is an application of these algorithms to the problem of determining the laying sequence for a continuous girder reinforced concrete floor system. If the number of design variables and the number of combinations are large, it is often impossible to obtain

Y. Natsuaki; H. Furuta; S. Mukandai; K. Yasuda

1995-01-01

188

Multiobjective Optimization using a Micro-Genetic Algorithm  

Microsoft Academic Search

In this paper, we propose a micro genetic algorithm with three forms of elitism for multiobjective optimization. We show how this relatively simple algorithm coupled with an external file and a diversity approach based on geographical distribution can generate efficiently the Pareto fronts of several difficult test functions (both constrained and unconstrained). A metric based on the average distance to

Carlos A. Coello Coello

189

Genetic algorithms and their statistical applications: an introduction  

Microsoft Academic Search

Genetic algorithms (GA) are stochastic optimization tools that work on “Darwinian” models of population biology and are capable of solving for near-optimal solution for multivariable functions without the usual mathematical requirements of strict continuity, differentiability, convexity and other properties. The algorithm begins by choosing a large number of candidate solutions which propagate themselves through a “selection criteria” and are changed

Sangit Chatterjee; Matthew Laudato; Lucy A. Lynch

1996-01-01

190

Distributed genetic algorithm for Gaussian mixture model based speaker identification  

Microsoft Academic Search

This paper presents a novel algorithm for reducing the computational complexity of identifying a speaker within a Gaussian mixture speaker model (GMM) framework. We have combined distributed genetic algorithm (DGA) and the Markov random field (MRF) to avoid typical local minima for speaker vector quantization. To improve the computation efficiency, only unstable chromosomes corresponding to speaker data parts are evolved.

Shung-yung Lung

2003-01-01

191

Parallel Genetic Algorithms for multi-objective rule mining  

Microsoft Academic Search

In this work, we study a general model (association rule) to discover and describe associations between items in large databases. The association rule problem is modeled as a multi-objective combinatorial optimization problem. We propose to solve it using a cooperative evolution- ary algorithm based on genetic algorithms. Therefore, specific mechanisms (mutation and crossover operators, elitism,...) have been designed and a

Mohamed Khabzaoui; El-Ghazali Talbi

2005-01-01

192

A New Migration Model For Distributed Genetic Algorithms  

Microsoft Academic Search

Genetic algorithms are heuristic search algorithm used in science, engineering and many other areas. They are powerful but slow because of their evolutionary nature that mimics the natural selection process. The quality of the solutions delivered depends on the population size, causing larger demand on processing power. Parallel and distributed processing techniques resolve this issue by allocating subpopulations to a

Taisir Eldos

2006-01-01

193

iECGA: integer extended compact genetic algorithm  

Microsoft Academic Search

Extended compact genetic algorithm (ECGA) is an algo- rithm that can solve hard problems in the binary domain. ECGA is reliable and accurate because of the capability of detecting building blocks, but certain difficulties are encoun- tered when we directly apply ECGA to problems in the inte- ger domain. In this paper, we propose a new algorithm that extends ECGA,

Ping-chu Hung; Ying-ping Chen

2006-01-01

194

A Novel Hardware Implementation of the Compact Genetic Algorithm  

Microsoft Academic Search

In this paper we show a novel and efficient design of a compact Genetic Algorithm (cGA) in Hardware. This design presents the following features: modularity, concurrency, minimal resource consumption, real time execution, and high scalability properties. According to the obtained results, we show that it is viable to have this search algorithm in Hardware to be used in real time

M. A. Moreno-Armenda?riz; N. Cruz-Corte?s; A. Leo?n-Javier

2010-01-01

195

A family of compact genetic algorithms for intrinsic evolvable hardware  

Microsoft Academic Search

For many evolvable hardware applications, small size and power efficiency are critical design considerations. One manner in which significant memory, and thus, power and space savings can be realized in a hardware-based evolutionary algorithm is to represent populations of candidate solutions as probability vectors rather than as sets of bit strings. The compact genetic algorithm (CGA) is a probability vector-based

John C. Gallagher; Saranyan Vigraham; Gregory R. Kramer

2004-01-01

196

Markov Random Field Modelling of Genetic Algorithms Evaluation of Research  

Microsoft Academic Search

Abstract The Proposed project Markov Random Field Modelling of Genetic Algorithm aims to introduce MOA: Markov Random Field Optimization Algorithm. The idea is based on the use of Markov Random Field models as a probabilistic model capturing the interdependency between variables in the GA chromosome,for better evolution of a solution. This report is a self evaluation of our research to

Siddhartha K Shakya

2004-01-01

197

Structure of morphologically expanded queries: A genetic algorithm approach  

Microsoft Academic Search

In this paper we deal with two issues. First, we discuss the negative effects of term correlation in query expansion algorithms, and we propose a novel and simple method (query clauses) to represent expanded queries which may alleviate some of these negative effects. Second, we discuss a method to optimize local query-expansion methods using genetic algorithms, and we apply this

Lourdes Araujo; Hugo Zaragoza; José R. Pérez-Agüera; Joaquín Pérez-Iglesias

2010-01-01

198

Applications of Genetic Algorithm in Polymer Science and Engineering  

Microsoft Academic Search

In the last several years, genetic algorithm (GA) has gained wide acceptance as a robust optimization algorithm in almost all areas of science and engineering. Polymer science and engineering is no exception. Researchers in this field have devoted considerable attention to the optimization of polymer productionusing objective functions and constraints that lead to products having desired material properties. Multiple-objective functions

Rahul B. Kasat; Ajay K. Ray; Santosh K. Gupta

2003-01-01

199

Identification of pneumatic cylinder friction parameters using genetic algorithms  

Microsoft Academic Search

A method for identifying friction parameters of pneumatic actuator systems is developed in this paper, based on genetic algorithms (GA). The statistical expectation of mean-squared errors is traditionally used to form evaluation functions in general optimization problems using GA. However, it has been found that, sometimes, this type of evaluation function does not lead the algorithms to have a satisfactory

J. Wang; N. Daw; Q. H. Wu

2004-01-01

200

Genetic Algorithm Based Selective Ensemble with Multiset Representation  

Microsoft Academic Search

Recently, it has been shown that, in ensemble learning, it may be preferable to ensemble some instead of all the classifiers. Various selective ensemble approaches are then designed, where optimization algorithms like genetic algorithm (GA) are used to evolve weights of component classifiers and classifiers with weights greater than a threshold are selected. This paper proposes a novel selective ensemble

Gang Wang; Xinshun Xu; Liang Peng

2010-01-01

201

Evolving homeostatic tissue using genetic algorithms  

PubMed Central

Multicellular organisms maintain form and function through a multitude of homeostatic mechanisms. The details of these mechanisms are in many cases unknown, and so are their evolutionary origin and their link to development. In order to illuminate these issues we have investigated the evolution of structural homeostasis in the simplest of cases, a tissue formed by a mono-layer of cells. To this end, we made use of a 3-dimensional hybrid cellular automaton, an individual-based model in which the behaviour of each cell depends on its local environment. Using an evolutionary algorithm (EA) we evolved cell signalling networks, both with a fixed and an incremental fitness evaluation, which give rise to and maintain a mono-layer tissue structure. Analysis of the solutions provided by the EA shows that the two evaluation methods gives rise to different types of solutions to the problem of homeostasis. The fixed method leads to almost optimal solutions, where the tissue relies on a high rate of cell turnover, while the solutions from the incremental scheme behave in a more conservative manner, only dividing when necessary. In order to test the robustness of the solutions we subjected them to environmental stress, by wounding the tissue, and to genetic stress, by introducing mutations. The results show that the robustness very much depends on the mechanism responsible for maintaining homeostasis. The two evolved cell types analysed present contrasting mechanisms by which tissue homeostasis can be maintained. This compares well to different tissue types found in multicellular organisms. For example the epithelial cells lining the colon in humans are shed at a considerable rate, while in other tissue types, which are not as exposed, the conservative type of homeostatic mechanism is normally found. These results will hopefully shed light on how multicellular organisms have evolved homeostatic mechanisms and what might occur when these mechanisms fail, as in the case of cancer.

Gerlee, Philip; Basanta, David; Anderson, Alexander R.A.

2013-01-01

202

Optimization of solar air collector using genetic algorithm and artificial bee colony algorithm  

NASA Astrophysics Data System (ADS)

Thermal performance of solar air collector depends on many parameters as inlet air temperature, air velocity, collector slope and properties related to collector. In this study, the effect of the different parameters which affect the performance of the solar air collector are investigated. In order to maximize the thermal performance of a solar air collector genetic algorithm (GA) and artificial bee colony algorithm (ABC) have been used. The results obtained indicate that GA and ABC algorithms can be applied successfully for the optimization of the thermal performance of solar air collector.

?encan ?ahin, Arzu

2012-11-01

203

Sequential multi-criteria feature selection algorithm based on agent genetic algorithm  

Microsoft Academic Search

A multi-criteria feature selection method-sequential multi-criteria feature selection algorithm (SMCFS) has been proposed\\u000a for the applications with high precision and low time cost. By combining the consistency and otherness of different evaluation\\u000a criteria, the SMCFS adopts more than one evaluation criteria sequentially to improve the efficiency of feature selection.\\u000a With one novel agent genetic algorithm (chain-like agent GA), the SMCFS

Yongming Li; Xiaoping Zeng

2010-01-01

204

Genetic Algorithms to Learn Feature Weights for the Nearest Neighbor Algorithm  

Microsoft Academic Search

In this paper we use genetic algorithms to learn feature weights for the NearestNeighbor classification algorithm. We represent feature weights as real values in [0..1]and their sum is 1. A new crossover operation, called continuous uniform crossover,is introduced where the legality of chromosomes is preserved after the crossoveroperation. This new crossover operation is compared with three other crossoveroperations---one-point crossover, two-point

Gülsen Demiroz; H. Altay Güvenir

1996-01-01

205

A simple algorithm for optimization and model fitting: AGA (asexual genetic algorithm)  

NASA Astrophysics Data System (ADS)

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 genetic algorithms (GA) for optimization. We refer to this algorithm as AGA (asexual genetic algorithm) 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 genetic algorithms 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.

Cantó, J.; Curiel, S.; Martínez-Gómez, E.

2009-07-01

206

Community detection based on modularity and an improved genetic algorithm  

NASA Astrophysics Data System (ADS)

Complex networks are widely applied in every aspect of human society, and community detection is a research hotspot in complex networks. Many algorithms use modularity as the objective function, which can simplify the algorithm. In this paper, a community detection method based on modularity and an improved genetic algorithm (MIGA) is put forward. MIGA takes the modularity Q as the objective function, which can simplify the algorithm, and uses prior information (the number of community structures), which makes the algorithm more targeted and improves the stability and accuracy of community detection. Meanwhile, MIGA takes the simulated annealing method as the local search method, which can improve the ability of local search by adjusting the parameters. Compared with the state-of-art algorithms, simulation results on computer-generated and four real-world networks reflect the effectiveness of MIGA.

Shang, Ronghua; Bai, Jing; Jiao, Licheng; Jin, Chao

2013-03-01

207

Application of Hybridized Genetic Algorithms to the Protein Folding Problem.  

National Technical Information Service (NTIS)

The protein folding problem consists of attempting to determine the native conformation of a protein given its primary structure. This study examines various methods of hybridizing a genetic algorithm implementation in order to minimize an energy function...

R. L. Gaulke

1995-01-01

208

Study on living object identification based on genetic algorithms  

NASA Astrophysics Data System (ADS)

Fast and effectual salvage can reduce accident loss, ensure people's lives and belongings safely when shipwreck occurs. It is very important that discovering objects should be timely and exactly to insure the salvage going on wheels. This text puts forward an object identification arithmetic based on Genetic Algorithms, which makes use of Genetic Algorithms to search living objects in the sea based on different infrared radiation characteristics between living objects and background, uses single point crossover method and simple mutation method with adaptive probability, ensures the global and local searching ability of Genetic Algorithms. Thus GA can accomplish searching course of optimization quickly and exactly with favorable searching ability. From identification test aiming at standard infrared image, it is seen that the image is strengthened by Genetic Algorithms, and the living objects can be identified exactly.

Wang, Yao; Xiong, Mu-di; Jia, Si-nan

2007-11-01

209

Shape Optimization of Cochlear Implant Electrode Array Using Genetic Algorithms.  

National Technical Information Service (NTIS)

Finite element analysis is used to compute the current distribution of the human cochlea during cochlear implant electrical stimulation. Genetic algorithms are then applied in conjunction with the finite element analysis to optimize the shape of cochlear ...

C. T. Choi

2001-01-01

210

Application of Genetic Algorithms to Tuning Fuzzy Control Systems.  

National Technical Information Service (NTIS)

Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the a...

T. Espy E. Vombrack J. Aldridge

1993-01-01

211

Internal quantum efficiency analysis of solar cell by genetic algorithm  

SciTech Connect

To investigate factors limiting the performance of a GaAs solar cell, genetic algorithm 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 genetic algorithm 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)

2010-11-15

212

Resource allocation by genetic algorithm with fuzzy inference  

Microsoft Academic Search

Assuming that a make-to-order manufacturing company has customer orders, the addressed capacity allocation problem is a due-date assignment problem for multiple manufacturing resources. The purpose of this study is to develop an intelligent resource allocation model using genetic algorithm and fuzzy inference for reducing lateness of orders with specific due dates. While the genetic algorithm is responsible for arranging and

Kung-jeng Wang; Y.-S. Lin

2007-01-01

213

A Parallel Compact Genetic Algorithm for Multi-FPGA Partitioning  

Microsoft Academic Search

In this paper we investigate the design of a compact genetic algorithm to solve multi-FPGA partitioning problems. Nowadays Multi-FPGA systems are used for a great variety of applications such as dynamically reconfigurable hardware applications, digital circuit emulation, and numerical computation. Both a sequential and a parallel version of a compact genetic algorithm (cGA) have been designed and implemented on a

Ranieri Baraglia; Raffaele Perego; José Ignacio Hidalgo; Juan Lanchares; Francisco Tirado

2001-01-01

214

Boiler-turbine control system design using a genetic algorithm  

Microsoft Academic Search

This paper discusses the application of a genetic algorithm to control system design for boiler-turbine plant. In particular we study the ability of the genetic algorithm 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

Robert Dimeo; Kwang Y. Lee

1995-01-01

215

Collaborative supply chain network using embedded genetic algorithms  

Microsoft Academic Search

Purpose – The aim of this paper is to propose a genetic algorithms approach to develop a collaborative supply chain network, i.e. a supply chain network with genetic algorithms embedded (GA-SCN), so as to increase the efficiency and effectiveness of a supply chain network. Design\\/methodology\\/approach – The methodologies of the GA-SCN are illustrated through a case study of a supply

C. Y. Lam; S. L. Chan; W. H. Ip; C. W. Lau

2008-01-01

216

Contents correlation and genetic algorithm based remote sensing images fusion  

NASA Astrophysics Data System (ADS)

A contents correlation and Genetic Algorithm based remote sensing images fusion method is presented. Based on the imaging properties of Panchromatic images and multi-spectral images, contents correlation analysis concept is introduced. The fusion procedure is that Contourlet transform decomposition of Panchromatic and multi-spectral images, Analysis of redundancy and supplement relations of images contents, the construction of fusion method to redundancy components and supplement components, fusion algorithms optimization by using Genetic Algorithm. Finally, a fused image can be obtained with inverse Contourlet transform. Preliminary experiment results show that this method is better than ordinary wavelet transform based fusion method, IHS transform based fusion method and PCA transform based fusion method.

Na, Yan; Ehlers, Manfred; Ji, Hongbin; Shi, Lin

2007-10-01

217

Optimization of a genetic algorithm for searching molecular conformer space.  

PubMed

We present two sets of tunings that are broadly applicable to conformer searches of isolated molecules using a genetic algorithm (GA). In order to find the most efficient tunings for the GA, a second GA--a meta-genetic algorithm--was used to tune the first genetic algorithm to reliably find the already known a priori correct answer with minimum computational resources. It is shown that these tunings are appropriate for a variety of molecules with different characteristics, and most importantly that the tunings are independent of the underlying model chemistry but that the tunings for rigid and relaxed surfaces differ slightly. It is shown that for the problem of molecular conformational search, the most efficient GA actually reduces to an evolutionary algorithm. PMID:22070291

Brain, Zoe E; Addicoat, Matthew A

2011-11-01

218

Novel hybrid genetic algorithm for progressive multiple sequence alignment.  

PubMed

The family of evolutionary or genetic algorithms is used in various fields of bioinformatics. Genetic algorithms (GAs) can be used for simultaneous comparison of a large pool of DNA or protein sequences. This article explains how the GA is used in combination with other methods like the progressive multiple sequence alignment strategy to get an optimal multiple sequence alignment (MSA). Optimal MSA get much importance in the field of bioinformatics and some other related disciplines. Evolutionary algorithms evolve and improve their performance. In this optimisation, the initial pair-wise alignment is achieved through a progressive method and then a good objective function is used to select and align more alignments and profiles. Child and subpopulation initialisation is based upon changes in the probability of similarity or the distance matrix of the alignment population. In this genetic algorithm, optimisation of mutation, crossover and migration in the population of candidate solution reflect events of natural organic evolution. PMID:24084242

Afridi, Muhammad Ishaq

2013-01-01

219

Genetic algorithms with permutation coding for multiple sequence alignment.  

PubMed

Multiple sequence alignment (MSA) is one of the topics of bio informatics that has seriously been researched. It is known as NP-complete problem. It is also considered as one of the most important and daunting tasks in computational biology. Concerning this a wide number of heuristic algorithms have been proposed to find optimal alignment. Among these heuristic algorithms are genetic algorithms (GA). The GA has mainly two major weaknesses: it is time consuming and can cause local minima. One of the significant aspects in the GA process in MSA is to maximize the similarities between sequences by adding and shuffling the gaps of Solution Coding (SC). Several ways for SC have been introduced. One of them is the Permutation Coding (PC). We propose a hybrid algorithm based on genetic algorithms (GAs) with a PC and 2-opt algorithm. The PC helps to code the MSA solution which maximizes the gain of resources, reliability and diversity of GA. The use of the PC opens the area by applying all functions over permutations for MSA. Thus, we suggest an algorithm to calculate the scoring function for multiple alignments based on PC, which is used as fitness function. The time complexity of the GA is reduced by using this algorithm. Our GA is implemented with different selections strategies and different crossovers. The probability of crossover and mutation is set as one strategy. Relevant patents have been probed in the topic. PMID:22974260

Othman, Mohamed Tahar Ben; Abdel-Azim, Gamil

2013-08-01

220

The crowding approach to niching in genetic algorithms.  

PubMed

A wide range of niching techniques have been investigated in evolutionary and genetic algorithms. In this article, we focus on niching using crowding techniques in the context of what we call local tournament algorithms. In addition to deterministic and probabilistic crowding, the family of local tournament algorithms includes the Metropolis algorithm, simulated annealing, restricted tournament selection, and parallel recombinative simulated annealing. We describe an algorithmic and analytical framework which is applicable to a wide range of crowding algorithms. As an example of utilizing this framework, we present and analyze the probabilistic crowding niching algorithm. Like the closely related deterministic crowding approach, probabilistic crowding is fast, simple, and requires no parameters beyond those of classical genetic algorithms. In probabilistic crowding, subpopulations are maintained reliably, and we show that it is possible to analyze and predict how this maintenance takes place. We also provide novel results for deterministic crowding, show how different crowding replacement rules can be combined in portfolios, and discuss population sizing. Our analysis is backed up by experiments that further increase the understanding of probabilistic crowding. PMID:18811245

Mengshoel, Ole J; Goldberg, David E

2008-01-01

221

Test driving three 1995 genetic algorithms: New test functions and geometric matching  

Microsoft Academic Search

Genetic algorithms have attracted a good deal of interest in the heuristic search community. Yet there are several different\\u000a types of genetic algorithms with varying performance and search characteristics. In this article we look at three genetic\\u000a algorithms: an elitist simple genetic algorithm, the CHC algorithm and Genitor. One problem in comparing algorithms is that\\u000a most test problems in the

D. Whitley; R. Beveridge; C. Graves; K. Mathias

1995-01-01

222

Improving the reliability of heuristic multiple fault diagnosis via the EC-based Genetic Algorithm  

Microsoft Academic Search

Engineered Conditioning (EC) is a Genetic Algorithm operator that works together with the typical genetic algorithm operators: mate selection, crossover, and mutation, in order to improve convergence toward an optimal multiple fault diagnosis. When incorporated within a typical genetic algorithm, the resulting hybrid scheme produces improved reliability by exploiting the global nature of the genetic algorithm as well as “local”

Walter D. Potter; John A. Miller; Bruce E. Tonn; Ravi V. Gandham; Chito N. Lapena

1992-01-01

223

A Knowledge based Genetic Algorithm for Path Planning of a Mobile Robot  

Microsoft Academic Search

In this paper, a knowledge based genetic algorithm (GA) for path planning of a mobile robot is proposed, which uses problem-specific genetic algorithms for robot path planning instead of the standard GAs. The proposed knowledge based genetic algorithm incorporates the domain knowledge into its specialized operators, where some also combine a local search technique. The proposed genetic algorithm also features

Yanrong Hu; Simon X. Yang

2004-01-01

224

An Implementation of Compact Genetic Algorithm on FPGA for Extrinsic Evolvable Hardware  

Microsoft Academic Search

Traditional genetic algorithms require a lot of memory and processing power on embedded logic projects. Representing populations of candidate solutions through vectors of probabilities rather than sets of bit strings saves memory and processing. The compact genetic algorithm (CGA) is a probability vector based genetic algorithm. The article presents an FPGA implementation of the standard compact genetic algorithm with a

Tiago Carvalho Oliveira; V. Pilla

2008-01-01

225

A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms  

Microsoft Academic Search

A problem that is inherent to the development and efficient use of solvers is that of tuning parameters. The CP community has a long history of ad- dressing this task automatically. We propose a robust, inherently parallel genetic algorithm for the problem of configuring solvers automatically. In order to cope with the high costs of evaluating the fitness of individuals,

Carlos Ansótegui; Meinolf Sellmann; Kevin Tierney

2009-01-01

226

Solving Classification Problems Using Genetic Programming Algorithms on GPUs  

NASA Astrophysics Data System (ADS)

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.

Cano, Alberto; Zafra, Amelia; Ventura, Sebastián

227

SexualGA: Gender-Specifc Selection for Genetic Algorithms  

Microsoft Academic Search

Selection for reproduction in the context of Genetic Algorithms uses only one selection scheme to select parent individuals. When considering the model of sexual selection in the area of population genetics it gets obvious that the process of choosing mating partners in natural populations is difierent for male and female individuals. In this paper the authors introduce a new selection

Stefan WAGNER; Michael AFFENZELLER

2005-01-01

228

Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization  

Microsoft Academic Search

The paper describes a rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to allow direct intervention of an external decision maker (DM). Finally, the MOGA is generalised further: the genetic

Carlos M. Fonseca; Peter J. Fleming

1993-01-01

229

Using Genetic Algorithm to Improve Information Retrieval Systems  

Microsoft Academic Search

This study investigates the use of genetic algorithms in information retrieval. The method is shown to be applicable to three well-known documents collections, where more relevant documents are presented to users in the genetic modification. In this paper we present a new fitness function for approximate information retrieval which is very fast and very flexible, than cosine similarity fitness function.

Ahmed A. A. Radwan; Bahgat A. Abdel Latef; Abdel Mgeid; A. Ali; Osman A. Sadek

2006-01-01

230

Analysis of the Effective Degrees of Freedom in Genetic Algorithms  

Microsoft Academic Search

An evolution equation for a population of strings evolving under the genetic operators: selection, mutation and crossover is derived. The corresponding equation describing the evolution of schematas is found by performing an exact coarse graining of this equation. In particular exact expressions for schemata reconstruction are derived which allows for a critical appraisal of the ``building-block hypothesis'' of genetic algorithms.

C. R. Stephens; H. Waelbroeck

1996-01-01

231

Multiple Query Evaluation Based on an Enhanced Genetic Algorithm.  

ERIC Educational Resources Information Center

|Explains the use of genetic algorithms to combine results from multiple query evaluations to improve relevance in information retrieval. Discusses niching techniques, relevance feedback techniques, and evolution heuristics, and compares retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation…

Tamine, Lynda; Chrisment, Claude; Boughanem, Mohand

2003-01-01

232

Fuzzy Information Retrieval Using Genetic Algorithms and Relevance Feedback.  

ERIC Educational Resources Information Center

|Describes an approach that combines concepts from information retrieval, fuzzy set theory, and genetic programing to improve weighted Boolean query formulation via relevance feedback. Highlights include background on information retrieval systems; genetic algorithms; subproblem formulation; and preliminary results based on a testbed. (Contains 12…

Petry, Frederick E.; And Others

1993-01-01

233

Genetic Algorithms and Evolution Strategies - Similarities and Differences  

Microsoft Academic Search

Evolution Strategies (ESs) and Genetic Algorithms (GAs) are compared in a formal as well as in an experimental way. It is shown, that both are identical with respect to their major working scheme, but nevertheless they exhibit significant differences with respect to the details of the selection scheme, the amount of the genetic representation and, especially, the self-adaptation of strategy

Frank Hoffmeister; Thomas Bäck

1990-01-01

234

A genetic algorithm based clustering using geodesic distance measure  

Microsoft Academic Search

Aim at the problem that classical Euclidean distance metric cannot generate a appropriate partition for data lying in a manifold, a genetic algorithm based clustering method using geodesic distance measure is put forward. In this study, a prototype-based genetic representation is utilized, where each chromosome is a sequence of positive integer numbers that represent the k-medoids. Additionally, a geodesic distance

Gang Li; Jian Zhuang; Hongning Hou; Dehong Yu

2009-01-01

235

A Cooperative Approach to Compact Genetic Algorithm for Evolvable Hardware  

Microsoft Academic Search

This paper presents a cooperative compact genetic algorithm (CoCGA). The CoCGA is developed from the compact GA and proposed to be used for intrinsic evolvable hardware. The concept and algorithm of the CoCGA are presented. The hardware implementation of the CoCGA and CGA were carried out. The standard test functions were selected to measure the effectiveness of the CoCGA. The

Y. Jewajinda; P. Chongstitvatana

2006-01-01

236

A Step Forward in Studying the Compact Genetic Algorithm  

Microsoft Academic Search

The compact Genetic Algorithm (cGA) is an Estimation of Distribution Algorithm that generates offspring population according to the estimated probabilistic model of the parent population instead of using traditional recombination and mutation operators. The cGA only needs a small amount of memory; therefore, it may be quite useful in memory-constrained applications. This paper introduces a theoretical framework for studying the

Reza Rastegar; Arash Hariri

2009-01-01

237

Designing a Compact Genetic Algorithm with Minimal FPGA Resources  

Microsoft Academic Search

The Compact Genetic Algorithms (cGA) are searching methods used in different engineering applications. These algorithms have\\u000a interesting features such as their capability to operate with very low memory resources while solving complex optimization\\u000a problems. In this paper we present a novel design for the implementation of a cGA on a FPGA. This design is modular, so its\\u000a components would be

Alejandro León-Javier; Marco A. Moreno-Armendáriz; Nareli Cruz-Cortés

238

Genetic algorithm for multi-objective experimental optimization  

PubMed Central

A new software tool making use of a genetic algorithm for multi-objective experimental optimization (GAME.opt) was developed based on a strength Pareto evolutionary algorithm. The software deals with high dimensional variable spaces and unknown interactions of design variables. This approach was evaluated by means of multi-objective test problems replacing the experimental results. A default parameter setting is proposed enabling users without expert knowledge to minimize the experimental effort (small population sizes and few generations).

Link, Hannes

2006-01-01

239

Active flatness control of membrane structures using adaptive genetic algorithm  

NASA Astrophysics Data System (ADS)

Membrane structures are attracting attention as excellent candidates for lightweight large space structures, which can be utilized to improve the performance and reduce the cost of space exploration and earth observation missions. Membrane structures can be stowed to a small volume during launch and function as large structures after deployed. For many applications, maintaining surface accuracy of membranes is extremely important to achieve satisfactory performance, especially for membrane antennas and adaptive optics. Active flatness control is a vital technology to maintain surface accuracy of membrane structures. In this research, multiple shape memory alloy (SMA) actuators around the boundary of a rectangular membrane are used to apply tension forces to membrane structures to compensate wrinkle effects. The dynamics of membrane structures is nonlinear and computationally expensive, hence unfeasible to be used in real-time active flatness control. As a parallel direct searching method, genetic algorithm (GA) is used search optimal tension force combination on a high dimensional nonlinear surface. Due to increasing number of tension forces to search, the convergence is more difficult to attain. In order to increase responsiveness and convergence of genetic algorithm, an adaptive genetic algorithm (AGA) is proposed. Adaptive rules are incorporated in a modified genetic algorithm to regulate control parameters of genetic algorithm. Through numerical simulation and experimental studies, it is demonstrated that AGA can expedite its search process and prevent premature convergence.

Wang, Xiaoyun; Zheng, Wanping; Hu, Yan-Ru

2007-04-01

240

The multi-niche crowding genetic algorithm: Analysis and applications  

SciTech Connect

The ability of organisms to evolve and adapt to the environment has provided mother nature with a rich and diverse set of species. Only organisms well adapted to their environment can survive from one generation to the next, transferring on the traits, that made them successful, to their offspring. Competition for resources and the ever changing environment drives some species to extinction and at the same time others evolve to maintain the delicate balance in nature. In this disertation we present the multi-niche crowding genetic algorithm, a computational metaphor to the survival of species in ecological niches in the face of competition. The multi-niche crowding genetic algorithm maintains stable subpopulations of solutions in multiple niches in multimodal landscapes. The algorithm introduces the concept of crowding selection to promote mating among members with qirnilar traits while allowing many members of the population to participate in mating. The algorithm uses worst among most similar replacement policy to promote competition among members with similar traits while allowing competition among members of different niches as well. We present empirical and theoretical results for the success of the multiniche crowding genetic algorithm for multimodal function optimization. The properties of the algorithm using different parameters are examined. We test the performance of the algorithm on problems of DNA Mapping, Aquifer Management, and the File Design Problem. Applications that combine the use of heuristics and special operators to solve problems in the areas of combinatorial optimization, grouping, and multi-objective optimization. We conclude by presenting the advantages and disadvantages of the algorithm and describing avenues for future investigation to answer other questions raised by this study.

Cedeno, W.

1995-09-01

241

A Genetic Algorithm for Job Shop Scheduling Problem Using Co-evolution and Competition Mechanism  

Microsoft Academic Search

Through analyzing the characteristic of genetic algorithm and Job Shop scheduling Problem, a new genetic algorithm is proposed. This algorithm is based on the mechanism of co-evolution and natural selection. The parents and the genetic operators are selected by the competitive principle. Therefore, this algorithm can not only denotes parallelism in the course of GA, but also develops the solution

Yang Xiaomei; Zeng Jianchao; Liang Jiye; Liang Jiahua

2010-01-01

242

Evolving fuzzy rule based controllers using genetic algorithms  

Microsoft Academic Search

The synthesis of genetics-based machine learning and fuzzy logic is beginning to show promise as a potent tool in solving complex control problems in multi-variate non-linear systems. In this paper an overview of current research applying the genetic algorithm to fuzzy rule based control is presented. A novel approach to genetics-based machine learning of fuzzy controllers, called a Pittsburgh Fuzzy

Brian Carse; Terence C. Fogarty; Alistair Munro

1996-01-01

243

Algorithmic Trading with Developmental and Linear Genetic Programming  

NASA Astrophysics Data System (ADS)

A developmental co-evolutionary genetic programming approach (PAM DGP) and a standard linear genetic programming (LGP) stock trading systemare applied to a number of stocks across market sectors. Both GP techniques were found to be robust to market fluctuations and reactive to opportunities associated with stock price rise and fall, with PAMDGP generating notably greater profit in some stock trend scenarios. Both algorithms were very accurate at buying to achieve profit and selling to protect assets, while exhibiting bothmoderate trading activity and the ability to maximize or minimize investment as appropriate. The content of the trading rules produced by both algorithms are also examined in relation to stock price trend scenarios.

Wilson, Garnett; Banzhaf, Wolfgang

244

Genetic algorithm dose minimization for an operational layout.  

SciTech Connect

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 genetic algorithm. 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 genetic algorithm 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.

2002-01-01

245

Genetic algorithm approach to aircraft gate reassignment problem  

SciTech Connect

The aircraft gate reassignment problem occurs when the departure of an incoming aircraft is delayed or a delay occurs in flight. If the delay is significant enough to delay the arrival of subsequent incoming aircraft at the assigned gate, the airline must revise the gate assignments to minimize extra delay times. This paper describes a genetic algorithm approach to solving the gate reassignment problem. By using a global search technique on quantified information, this genetic algorithm approach can efficiently find minimum extra delayed time solutions that are as effective or more effective than solutions generated by experienced gate managers.

Gu, Y.; Chung, C.A.

1999-10-01

246

Using genetic algorithms to search for an optimal investment strategy  

NASA Astrophysics Data System (ADS)

In this experiment we used genetic algorithms to search for an investment strategy by dividing capital among different stocks with varying returns. The algorithm involves having a ``manager'' who divides his capital among various ``experts'' each of whom has a simple investment strategy. The expert strategies act like genes, experiencing mutation and crossover, in a selection process using previous returns as the fitness function. When algorithm was run with test data where the optimal strategy favored non-uniform investment in one stock it consistently beat a simple buy hold. However when the algorithm was run on actual stock data the system overwhelmingly stabilized at a population that closely resembled a simple buy hold portfolio, that is, evenly distribute the capital among all stocks.

Mandere, Edward; Xi, Haowen

2007-10-01

247

Selection of relevant features in a fuzzy genetic learning algorithm.  

PubMed

Genetic algorithms offer a powerful search method for a variety of learning tasks, and there are different approaches in which they have been applied to learning processes. Structural learning algorithm on vague environment (SLAVE) is a genetic learning algorithm that uses the iterative approach to learn fuzzy rules. SLAVE can select the relevant features of the domain, but when working with large databases the search space is too large and the running time can sometimes be excessive. We propose to improve SLAVE by including a feature selection model in which the genetic algorithm works with individuals (representing individual rules) composed of two structures: one structure representing the relevance status of the involved variables in the rule, the other one representing the assignments variable/value. For this general representation, we study two alternatives depending on the information coded in the first structure. When compared with the initial algorithm, this new approach of SLAVE reduces the number of rules, simplifies the structure of the rules and improves the total accuracy. PMID:18244806

Gonzalez, A; Perez, R

2001-01-01

248

Economic Dispatch Using Genetic Algorithm Based Hybrid Approach  

SciTech Connect

Power Economic Dispatch (ED) is vital and essential daily optimization procedure in the system operation. Present day large power generating units with multi-valves steam turbines exhibit a large variation in the input-output characteristic functions, thus non-convexity appears in the characteristic curves. Various mathematical and optimization techniques have been developed, applied to solve economic dispatch (ED) problem. Most of these are calculus-based optimization algorithms that are based on successive linearization and use the first and second order differentiations of objective function and its constraint equations as the search direction. They usually require heat input, power output characteristics of generators to be of monotonically increasing nature or of piecewise linearity. These simplifying assumptions result in an inaccurate dispatch. Genetic algorithms have used to solve the economic dispatch problem independently and in conjunction with other AI tools and mathematical programming approaches. Genetic algorithms have inherent ability to reach the global minimum region of search space in a short time, but then take longer time to converge the solution. GA based hybrid approaches get around this problem and produce encouraging results. This paper presents brief survey on hybrid approaches for economic dispatch, an architecture of extensible computational framework as common environment for conventional, genetic algorithm and hybrid approaches based solution for power economic dispatch, the implementation of three algorithms in the developed framework. The framework tested on standard test systems for its performance evaluation. (authors)

Tahir Nadeem Malik; Aftab Ahmad [University of Engineering and Technology, Taxila (Pakistan); Shahab Khushnood [National Power Construction Corporation - NPCC, 9-Shadman II, Lahore -54000 (Pakistan)

2006-07-01

249

Genetic algorithms for multicriteria shape optimization of induction furnace  

NASA Astrophysics Data System (ADS)

In this contribution we deal with a multi-criteria shape optimization of an induction furnace. We want to find shape parameters of the furnace in such a way, that two different criteria are optimized. Since they cannot be optimized simultaneously, instead of one optimum we find set of partially optimal designs, so called Pareto front. We compare two different approaches to the optimization, one using nonlinear conjugate gradient method and second using variation of genetic algorithm. As can be seen from the numerical results, genetic algorithm seems to be the right choice for this problem. Solution of direct problem (coupled problem consisting of magnetic and heat field) is done using our own code Agros2D. It uses finite elements of higher order leading to fast and accurate solution of relatively complicated coupled problem. It also provides advanced scripting support, allowing us to prepare parametric model of the furnace and simply incorporate various types of optimization algorithms.

Ku?s, Pavel; Mach, František; Karban, Pavel; Doležel, Ivo

2012-09-01

250

A biased random-key genetic algorithm for data clustering.  

PubMed

Cluster analysis aims at finding subsets (clusters) of a given set of entities, which are homogeneous and/or well separated. Starting from the 1990s, cluster analysis has been applied to several domains with numerous applications. It has emerged as one of the most exciting interdisciplinary fields, having benefited from concepts and theoretical results obtained by different scientific research communities, including genetics, biology, biochemistry, mathematics, and computer science. The last decade has brought several new algorithms, which are able to solve larger sized and real-world instances. We will give an overview of the main types of clustering and criteria for homogeneity or separation. Solution techniques are discussed, with special emphasis on the combinatorial optimization perspective, with the goal of providing conceptual insights and literature references to the broad community of clustering practitioners. A new biased random-key genetic algorithm is also described and compared with several efficient hybrid GRASP algorithms recently proposed to cluster biological data. PMID:23896381

Festa, P

2013-07-26

251

Classifying epilepsy diseases using artificial neural networks and genetic algorithm.  

PubMed

In this study, FFT analysis is applied to the EEG signals of the normal and patient subjects and the obtained FFT coefficients are used as inputs in Artificial Neural Network (ANN). The differences shown by the non-stationary random signals such as EEG signals in cases of health and sickness (epilepsy) were evaluated and tried to be analyzed under computer-supported conditions by using artificial neural networks. Multi-Layer Perceptron (MLP) architecture is used Levenberg-Marquardt (LM), Quickprop (QP), Delta-bar delta (DBD), Momentum and Conjugate gradient (CG) learning algorithms, and the best performance was tried to be attained by ensuring the optimization with the use of genetic algorithms of the weights, learning rates, neuron numbers of hidden layer in the training process. This study shows that the artificial neural network increases the classification performance using genetic algorithm. PMID:20703541

Koçer, Sabri; Canal, M Rahmi

2009-10-21

252

Longest jobs first algorithm in solving job shop scheduling using adaptive genetic algorithm (GA)  

NASA Astrophysics Data System (ADS)

In this paper, genetic algorithm was used to solve job shop scheduling problems. One example discussed in JSSP (Job Shop Scheduling Problem) and I described how we can solve such these problems by genetic algorithm. The goal in JSSP is to gain the shortest process time. Furthermore I proposed a method to obtain best performance on performing all jobs in shortest time. The method mainly, is according to Genetic algorithm (GA) and crossing over between parents always follows the rule which the longest process is at the first in the job queue. In the other word chromosomes is suggested to sorts based on the longest processes to shortest i.e. "longest job first" says firstly look which machine contains most processing time during its performing all its jobs and that is the bottleneck. Secondly, start sort those jobs which are belonging to that specific machine descending. Based on the achieved results," longest jobs first" is the optimized status in job shop scheduling problems. In our results the accuracy would grow up to 94.7% for total processing time and the method improved 4% the accuracy of performing all jobs in the presented example.

Alizadeh Sahzabi, Vahid; Karimi, Iman; Alizadeh Sahzabi, Navid; Mamaani Barnaghi, Peiman

2011-12-01

253

The control of genetic algorithms using version spaces  

Microsoft Academic Search

It is demonstrated how the traditional genetic algorithm (GA) can be augmented by incorporating domain knowledge in the form of a version space (VS) into the structure. This hybrid inductive learning system is designed to handle problems in concept learning using the VS to control the search process that is performed by the GA. In this hybrid system a novel

Robert G. Reynolds

1990-01-01

254

Hybrid Genetic Algorithms Are Better for Spatial Clustering  

Microsoft Academic Search

Iterative methods and genetic algorithms have been used separately to minimise the loss function of many representative-based clustering formulations. Neither of them alone seems to be significantly better. Moreover, the trade-off of effort vs quality slightly favours gradient descent. We present a unifying view for the three most popular loss functions: least sum of squares, its fuzzy version and the

Vladimir Estivill-castro

2000-01-01

255

Design optimisation for permanent magnet synchronous motors using genetic algorithm  

Microsoft Academic Search

This research work presents a new and efficient design methodology for the specification, development and manufacture of permanent magnet sychronous motors (PMSMs). In this paper a genetic algorithm based design optimisation technique for PMSMs is presented in which the multi-criteria considered in the optimisation are the electromagnetic performance, the thermal performance and the material cost. Models have been developed for

G. Sooriyakumar; R. Perryman; S. J. Dodds

2010-01-01

256

Marine shafting reasonable alignment design based on genetic algorithm  

Microsoft Academic Search

Marine shafting alignment is an indispensable part of marine propulsion shafting design and installation. In this paper, the marine shafting reasonable alignment based on genetic algorithm is investigated. The three-moment theory is utilized to analyze the bearing loads at straight alignment and bearing additional loads caused by offsets of bearings and inflections of flanges. An optimization model of reasonable alignment

Li Ren; WenXiao Zhang; JingBo Yu

2010-01-01

257

An Efficient Genetic Algorithm for Loss Minimum Distribution System Reconfiguration  

Microsoft Academic Search

This article describes a chromosome coding method of genetic algorithm (GA) for minimizing losses of a radial distribution system when it is subjected to network reconfiguration. By simulating the survival of the fittest among the strings, the optimum string is searched by randomized information exchange between strings by performing crossover and mutation. A vector-based load flow method for distribution systems

S. Sivanagaraju; Y. Srikanth; E. Jagadish Babu

2006-01-01

258

Design optimization of electrical machines using genetic algorithms  

Microsoft Academic Search

The application of genetic algorithms (GAs) to the design optimization of electromagnetic devices is presented in detail. The method is demonstrated on a magnetizer by optimizing its pole face to obtain the desired magnetic flux density distribution. The shape of the pole face is constructed from the control points by means of uniform nonrational b-splines

G. F. Uler; O. A. Mohammed; Chang-Seop Koh

1995-01-01

259

Evaluation of voltage stability indices (VSI) using genetic algorithm  

Microsoft Academic Search

In this paper, three voltage stability indices (VSIs) have been assessed. The voltage stability problem has been formulated as an optimization problem. Genetic algorithm (GA) has been employed to solve this optimization problem. Wale & Hale 6 bus system was used in this paper study.

Muhammad Tami Al-Hajri; M. A. Abido

2010-01-01

260

On genetic algorithms for shoe making nesting - A Taiwan case  

Microsoft Academic Search

This paper proposes a methodology that integrates in-house placement heuristics with genetic algorithms to solve the nesting prob- lems of shoe making. The problems are classified as placing a set of irregular patterns on a regular area and limited to at most two dif- ferent types of patterns on the area. Because of the intractability of the nesting problem, our

Hsu-hao Yang; Chien-li Lin

2009-01-01

261

A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems  

Microsoft Academic Search

In this paper, we present a powerful hybrid genetic algorithm based around a heuristic timetabling framework. This combines a direct representation of the timetable with heuristic crossover operators to ensure that the most fundamental constraints are never violated. We explain how the population is seeded so as to produce a solution which cannot be outperformed by the heuristic method alone.

Edmund K. Burke; Dave Elliman; Rupert F. Weare

1995-01-01

262

Combining prediction through heuristic genetic algorithm on intelligence service system  

Microsoft Academic Search

In this paper, the personified home service system is described through making use of heuristic genetic algorithm and extending the human interaction with resource description framework (RDF) schema. The human-computer interaction (HCI) interface is distributed and ubiquitous. Sensor, TV, refrigerator etc. could be used as interactive device not only Mouse and CRT. RDF schema describe them to control TV, refrigerator,

Xuhui Chen; Liang Cheng; Jianjian Huo; Yibin Hou; Yimin Wang

2004-01-01

263

Sexual Selection with Competitive\\/Cooperative Operators for Genetic Algorithms  

Microsoft Academic Search

In a standard genetic algorithm (GA), individuals repro- duce asexually: any two organisms may be parents in crossover. Gender separation and sexual selection here in- spire a model of gendered GA in which crossover takes place only between individuals of opposite sex and the GA's evaluation, selection, and mutation strategies depend on gender. Consequently, a pattern of cross-gender co- operation

Jose Sánchez-velazco; John A. Bullinaria

2003-01-01

264

Note on Genetic Algorithms for Large-Scale Feature Selection.  

National Technical Information Service (NTIS)

We introduce the use of genetic algorithms for the selection of features in the design of automatic pattern classifiers. Our preliminary results suggest that GA is a powerful means of reducing the time for finding near-optimal subsets of features from lar...

W. Siedlecki J. Sklansky

1989-01-01

265

Optimization of methanol synthesis reactor using genetic algorithms  

Microsoft Academic Search

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, genetic algorithms were used as powerful methods for optimization of complex problems. Initially, optimal temperature profile along the reactor was studied. Then, a

H. Kordabadi; A. Jahanmiri

2005-01-01

266

Tolerance design optimization of machine elements using genetic algorithm  

Microsoft Academic Search

An important problem that faces design engineers is how to assign tolerance limits. In practical applications, tolerances are most often assigned as an informal compromise between functionality, quality and manufacturing cost. Frequently, the compromise is obtained iteratively by trial and error. A more scientific approach is often desirable for better performance. In this paper, a genetic algorithm (GA) is used

A. Noorul Haq; K. Sivakumar; R. Saravanan; V. Muthiah

2005-01-01

267

Improvement of unsupervised texture classification based on genetic algorithms  

Microsoft Academic Search

At the previous conference, the authors are proposed a new unsupervised texture classification method based on the genetic algorithms (GA). In the method, the GA are employed to determine location and size of the typical textures in the target image. The proposed method consists of the following procedures: 1) the determination of the number of classification category; 2) each chromosome

Hiroshi Okumura; Yuuki Togami; Kohei Arai

2004-01-01

268

Optimization of reliability allocation strategies through use of genetic algorithms  

SciTech Connect

This paper examines a novel optimization technique called genetic algorithms 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 genetic algorithm 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 genetic algorithm 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 genetic algorithm 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.

Campbell, J.E.; Painton, L.A.

1996-08-01

269

USING GENETIC ALGORITHMS TO DESIGN ENVIRONMENTALLY FRIENDLY PROCESSES  

EPA Science Inventory

Genetic algorithm calculations are applied to the design of chemical processes to achieve improvements in environmental and economic performance. By finding the set of Pareto (i.e., non-dominated) solutions one can see how different objectives, such as environmental and economic ...

270

Genetic Algorithm for Grid Scheduling using Best Rank Power  

Microsoft Academic Search

The large computing capacity provided by grid systems is beneficial for solving complex problems by using many nodes of the grid at the same time. The usefulness of a grid system largely depends, among other factors, on the efficiency of the system regarding the allocation of jobs to grid resources. This paper proposes an Roulette Wheel Selection Genetic Algorithm using

Wael Abdulal; Omar Al Jadaan; Ahmad Jabas; S. Ramachandram

2009-01-01

271

Genetic algorithm design of Pareto optimal broadband microwave absorbers  

Microsoft Academic Search

The concept of Pareto optimality is applied to the study of choice tradeoffs between reflectivity and thickness in the design of multilayer microwave absorbers. Absorbers composed of a given number of layers of absorbing materials selected from a predefined database of available materials are considered. Three types of Pareto genetic algorithms for absorber synthesis are introduced and compared to each

D. S. Weile; E. Michielssen; D. E. Goldberg

1996-01-01

272

Improving Digital Video Commercial Detectors With Genetic Algorithms  

Microsoft Academic Search

The advent of digital video offers many opportunities to add features that enhance the viewing experience. One much-discussed feature is the possibility that commercials might be automatically detected in the video stream. We report on initial experiments with a class of commercial detection algorithms and show how their performance can be enhanced by applying genetic search to the optimization of

J. David Schaffer; Lalitha Agnihotri; Nevenka Dimitrova; Thomas Mcgee; Sylvie Jeannin

2002-01-01

273

A PARALLEL GENETIC ALGORITHM FOR AUTOMATED ELECTRONIC CIRCUIT DESIGN  

Microsoft Academic Search

We describe a parallel genetic algorithm (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

2000-01-01

274

Global Structural Optimizations of Surface Systems with a Genetic Algorithm.  

National Technical Information Service (NTIS)

Global structural optimizations with a genetic algorithm were performed for atomic cluster and surface systems including aluminum atomic clusters, Si magic clusters on the Si(111) 7x7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstruc...

F. Chuang

2005-01-01

275

Optimization of Hot Rolled Coil Widths Using a Genetic Algorithm  

Microsoft Academic Search

This paper focuses on the assortment problem in the steel industry; with the help of the genetic algorithm, it attempts to determine the optimum width of the parent stock given a set of forecasted customer widths so that the trim loss is minimized. For each given set of forecasted customer widths, an attempt is made to find a single width

Satchidananda Mohanty; Biswajit Mahanty; Pratap K. J. Mohapatra

2003-01-01

276

An Investigation on the Optimization Procedures of Intelligent Genetic Algorithm  

Microsoft Academic Search

Although the genetic algorithm (GA) is powerful and not limited by restrictive assumptions about search space of optimization problems, it has a convergency problem when it is used to find the optimum point with high accuracy. The object of developing an intelligent GA is to improve this weakness. In this paper, the optimization procedures of an intelligent GA are investigated,

Yan-Gang Zhao

2008-01-01

277

Optimization of touristic distribution netwoorks using genetic algorithms  

Microsoft Academic Search

The eight basic elements to design genetic algorithms (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

Josep R. Medinaa; V ´ õctor Yepes

2003-01-01

278

Experiences with the PGAPack Parallel Genetic Algorithm library  

SciTech Connect

PGAPack is the first widely distributed parallel genetic algorithm 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.

Levine, D.; Hallstrom, P.; Noelle, D.; Walenz, B.

1997-07-01

279

A multiobjective genetic algorithm for radio network optimization  

Microsoft Academic Search

Engineering of mobile telecommunication net- works endures two major problems: the design of the network, and the frequency assignment. We address the first problem in this paper, which has been formulated as a multiobjective constrained combinatorial optimisation problem. We propose a genetic algorithm that aims to ap- proximate the Pareto frontier of the problem. Advanced techniques have been used such

Hervé MEUNIER; El-ghazali TALBI; Philippe REININGER

2000-01-01

280

Learning Vision Algorithms for Real Mobile Robots with Genetic Programming  

Microsoft Academic Search

We present a genetic programming system to evolve vision based obstacle avoidance algorithms. In order to develop autonomous behavior in a mobile robot, our purpose is to design automatically an obstacle avoidance controller adapted to the current context. We first record short sequences where we manually guide the robot to move away from the walls. This set of recorded video

Renaud Barate; Antoine Manzanera

2008-01-01

281

Adapting mutations in genetic algorithms using gene flow principles  

Microsoft Academic Search

Bit mutation in genetic algorithms is usually done with a single fixed probability. Methods to adapt this probability have been suggested, but they operate at the genome level. This paper describes a gene level adaption scheme, based on allele frequencies, which gives a better escape from local optima.

Garrison W. Greenwood

2003-01-01

282

The Fuzzy Based Compact Genetic Algorithm for Online TSP  

Microsoft Academic Search

In this paper, we extend the definition of the common TSP and propose the online TSP, whose purpose is to find the shortest time of visiting all cities while considering the traffic conditions. We also propose a fuzzy based compact genetic algorithm (FCGA) for online TSPs. The basic idea of FCGA is to adapt the size of population simulated of

Li Shugang

2009-01-01

283

Towards Billion Bit Optimization via Efficient Genetic Algorithms  

Microsoft Academic Search

This paper presents a highly efficient, fully parallelized implementation of the compact ge- netic algorithm to solve very large scale problems with millions to billions of variables. The paper presents principled results demonstrating the scalable solution of a difficult test function on instances over a billion variables using a parallel implementation of compact genetic algo- rithm (cGA). The problem addressed

Kumara Sastry; David E. Goldberg; Xavier Llora

2007-01-01

284

Tightness Time for the Linkage Learning Genetic Algorithm  

Microsoft Academic Search

This paper develops a model for tightness time, linkage learning time for a single building block, in the linkage learning genetic algorithm (LLGA). First, the existing models for both linkage learning mechanisms, linkage skew and linkage shift, are extended and investigated. Then, the tightness time model is derived and proposed based on the extended linkage learning mechanism models. Experimental results

Ying-ping Chen; David E. Goldberg

2003-01-01

285

Convergence Time for the Linkage Learning Genetic Algorithm  

Microsoft Academic Search

This paper identifies the sequential behavior of the linkage learning genetic algorithm, introduces the tightness time model for a single building block, and develops the connection between the sequential behavior and the tightness time model. By integrating the first-building-block model based on the sequential behavior, the tightness time model, and the connection between these two models, a convergence time model

Ying-ping Chen; David E. Goldberg

2005-01-01

286

Wind Turbine Tower Optimization Method Using a Genetic Algorithm  

Microsoft Academic Search

A wind turbine tower optimization program was developed, using a genetic algorithm. This allowed a rational analysis to reduce the mass of turbine tower, by considering, for example, the distributions of diameter and wall thickness, and the positions of flanges and access ports to navigation lights. Both extreme and fatigue loads were calculated, based on wind turbine design requirements and

Shigeo Yoshida

2006-01-01

287

A genetic algorithm for determining the location of structural impacts  

Microsoft Academic Search

The spectral-element method, which is very suitable for solving force-identification problems, is combined with a stochastic\\u000a genetic algorithm to give a scheme that can locate the source of structural impacts. The results are demonstrated with experimental\\u000a data from the impact of an aluminum beam.

James F. Doyle

1994-01-01

288

Feature Selection Using Hybrid Evaluation Approaches Based on Genetic Algorithms  

Microsoft Academic Search

For a given set of samples, a new model is proposed to reduce input feature space, which decreases the learning time of classifiers, but also, improves the prediction accuracy according to the chosen relevance criterion. This model is constructed by decision trees and genetic algorithms, which evaluates by means of k nearest neighbor rule for classification, allowing the evolution model

L. F. Giraldo T; E. Delgado T; J. C. Riano; G. Castellanos D

2006-01-01

289

A solution to the optimal power flow using genetic algorithm  

Microsoft Academic Search

Optimal power flow (OPF) is one of the main functions of power generation operation and control. It determines the optimal setting of generating units. It is therefore of great importance to solve this problem as quickly and accurately as possible. This paper presents the solution of the OPF using genetic algorithm technique. This paper proposes a new methodology for solving

M. S. Osman; Mahmoud A. Abo-sinna; A. A. Mousa

2004-01-01

290

GA net: genetic algorithm platform for pipe network optimisation  

Microsoft Academic Search

The marriage of object-oriented programming techniques and genetic algorithms (GAs) provides a uniquely flexible environment for the development of evolution programs with technical applications. Computerised representations of hydraulic network models are used in various guises throughout the water industry. As well as being commonly linked to relational databases to form asset management systems and to geographic information systems (GIS) to

M. S Morley; R. M Atkinson; D. A Savi?; G. A Walters

2001-01-01

291

A genetic algorithm based method for product family design optimization  

Microsoft Academic Search

Increased commonality in a family of products can simplify manufacturing and reduce the associated costs and lead-times. There is a tradeoff, however, between commonality and individual product performance within a product family, and this paper introduces a genetic algorithm based method to help find an acceptable balance between commonality in the product family and desired performance of the individual products

Bryan DSouza; Timothy W. Simpson

2003-01-01

292

Human posture estimation from multiple images using genetic algorithm  

Microsoft Academic Search

A new method for estimating human postures at a time instant from multiple images using a genetic algorithm is proposed. The posture parameters to be estimated are assigned to the genes of individuals in the population. For each individual, its fitness evaluates to what extent the multiple human images synthesized by deforming a 3D human model according to the values

Jun OHYA; F. Kishino

1994-01-01

293

Applying Genetic Algorithms To Query Optimization in Document Retrieval.  

ERIC Educational Resources Information Center

|Proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. Discusses Chinese text retrieval, term frequency rating formulas, vector space models, bigrams, the PAT-tree structure for information retrieval, query vectors, and relevance feedback. (Author/LRW)|

Horng, Jorng-Tzong; Yeh, Ching-Chang

2000-01-01

294

Crossover Improvement for the Genetic Algorithm in Information Retrieval.  

ERIC Educational Resources Information Center

|In information retrieval (IR), the aim of genetic algorithms (GA) is to help a system to find, in a huge documents collection, a good reply to a query expressed by the user. Analysis of phenomena seen during the implementation of a GA for IR has led to a new crossover operation, which is introduced and compared to other learning methods.…

Vrajitoru, Dana

1998-01-01

295

Simultaneous Feature Extraction and Selection Using a Masking Genetic Algorithm  

Microsoft Academic Search

Statistical pattern recognition techniques classify objects in terms of a representative set of features. The selection of features to measure and include can have a significant effect on the c ost and accuracy of an automated classifier. Our previous research has shown that a hybrid between a k-nearest-neighbors (knn) classifier and a genetic algorithm (GA) can reduce the size of

Michael L. Raymer; William F. Punch; Erik D. Goodman; Paul C. Sanschagrin; Leslie A. Kuhn

1997-01-01

296

Reconfiguration of distribution systems by a modified genetic algorithm  

Microsoft Academic Search

In this paper a genetic algorithm based reconfiguration method is proposed to minimize the real power losses of distribution systems. The main innovation of this research work is that new types of crossover and mutation operators are proposed, such that the best possible results are obtained, with an acceptable computational effort. The crossover and mutation operators were developed so as

Marcos A. N. Guimaraes; Carlos A. Castro; Ruben Romero

2007-01-01

297

Machining fixture layout optimization using the genetic algorithm  

Microsoft Academic Search

Dimensional and form accuracy of a workpiece are influenced by the fixture layout selected for the machining operation. Hence, optimization of fixture layout is a critical aspect of machining fixture design. This paper presents a fixture layout optimization technique that uses the genetic algorithm (GA) to find the fixture layout that minimizes the deformation of the machined surface due to

Kulankara Krishnakumar; Shreyes N. Melkote

2000-01-01

298

On solving facility layout problems using genetic algorithms  

Microsoft Academic Search

Tam and Chan (1998) present a parallel genetic algorithm 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

L. Al-Hakim

2000-01-01

299

Genetic algorithms for nondestructive testing in crack identification  

Microsoft Academic Search

A method to identify the nature of a crack on the surface of a region using nondestructive testing (NDT) and inverse problem methodology is presented. A genetic algorithm (GA) based approach, which involves a global search to avoid local minima, is presented and applied to solve the inverse problem of identifying the position, shape and the orientation of a surface

A. A. Arkadan; T. Sareen; S. Subramaniam

1994-01-01

300

Diesel engine systems with genetic algorithm self tuning PID controller  

Microsoft Academic Search

Speed control of power generation plants driven by diesel prime-movers is difficult because of the presence of a dead time and changes in parameters. This results in slow plant dynamics. In this paper, genetic algorithm self tuning PID controller based on indirect estimation of the dead time is proposed resulting in fast response at the startup and quick recovery, when

Faisal A. Mohamed; Heikki N. Koivo

2005-01-01

301

Evolving Networks: Using the Genetic Algorithm with Connectionist Learning  

Microsoft Academic Search

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

1990-01-01

302

Evolving Efficient Security Systems Under Budget Constraints Using Genetic Algorithms  

Microsoft Academic Search

The EASI model (estimate of adversary sequence interruption model) is a dynamic, analytic method widely used by security professionals to evaluate a physical protection security system (ppss). Our methods involve using genetic algorithms to evolve such systems when budget constraints or detector sequencing must be considered.

Michael L. Gargano; William Edelson; Paul Benjamin; Paul Meisinger; Maheswara Kasinadhuni; Joseph DeCicco

2003-01-01

303

High speed frequency response masking filter design using genetic algorithm  

Microsoft Academic Search

This paper presents the design of high-speed, multiplier free, arbitrary bandwidth shape FIR filters based on frequency response masking technique (FRM). The FRM filter structure has been modified to improve the throughput by replacing long bandedge shaping filter with several cascaded short filters [Yong Lian, 2000]. Genetic algorithm (GA) is introduced to simultaneously optimize all subfilters in a cascaded connection.

Ling Cen; Yong Lian

2003-01-01

304

Tuning of a neuro-fuzzy controller by genetic algorithm  

Microsoft Academic Search

Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the Radial Basis Function neural network (RBF) with Gaussian

Teo Lian Seng; Marzuki Bin Khalid; Rubiyah Yusof

1999-01-01

305

Genetic algorithm optimisation of water consumption and wastewater network topology  

Microsoft Academic Search

Genetic algorithm (GA) optimisation applied to water systems with multiple contaminants and several contaminated sources is presented. This approach generates the overall optimal water network topology with respect to the minimum supply water usage, complying, in the same time, with all restrictions. An optimal water network could be viewed as an oriented graph, starting from unit operations which must not

Vasile Lavric; Petrica Iancu; Valentin Ple?u

2005-01-01

306

Design of sophisticated fuzzy logic controllers using genetic algorithms  

Microsoft Academic Search

The design of fuzzy logic controllers encounters difficulties in the selection of optimized membership functions and a fuzzy rule base, which is traditionally achieved by a tedious trial-and error process. This paper develops genetic algorithms for the automatic design of high-performance fuzzy logic controllers using sophisticated membership functions that intrinsically reflect the nonlinearities encountered in many engineering control applications. The

Kim Chwee Ng; Yun Li

1994-01-01

307

OPTIMIZATION FOR INDUCTION MOTOR DESIGN BY IMPROVED GENETIC ALGORITHM  

Microsoft Academic Search

Optimization for induction motor design is one of the interested subjects by electrical engineers. This paper proposes an Improved Genetic Algorithm (IGA) for optimization of 3-phase induction motor design. The proposed IGA possesses the characteristics of real number encoding, stochastic crossover operator, self-adaptable mutation operator and annealing penalty function, and multi- turns evolution strategy for solving nonlinear constrained multivariable optimization

Li HAN; Hui LI; Jingcan LI; Jianguo ZHU

2004-01-01

308

Optimal operation of pipeline systems using Genetic Algorithm  

Microsoft Academic Search

A Genetic Algorithm (GA) is used in this paper for the optimal operation, result in better solution than the existing one, of the pipeline systems under transient conditions caused by valve closure. Simulation of pipeline system is carried out here by the Implicit Method of Characteristics, a method recently developed and introduced by the authors. This method uses an element-wise

Mohamad Hadi Afshar; Maryam Rohani

2009-01-01

309

Emotional image and musical information retrieval with interactive genetic algorithm  

Microsoft Academic Search

Several techniques in artificial intelligence have shown a great potential to develop useful human-computer interfaces, but it is still quite far from realizing a system of matching the human performance, especially in terms of emotion, intuition and inspiration. To overcome this shortcoming, we present a promising technique called interactive genetic algorithm (IGA), which performs optimization with human evaluation, and with

Sung-Bae Cho

2004-01-01

310

Genetic Algorithms for Noise Reduction in Tire Design  

Microsoft Academic Search

In this paper we report about deployment of genetic algorithms in order to optimize tread profiles for tires that will produce an unobtrusive noise. Since the complexity of the problem grows exponentially (the search space is typically of the order of a 65-dimensional vector space), a complete search for the optimal tread profile is not possible even with today's computers.

Matthias Becker

2006-01-01

311

Optimization of Construction of Tire Reinforcement by Genetic Algorithm  

Microsoft Academic Search

A new tire design procedure capable of determining the optimum tire construction was developed by combining a finite element method approach with mathematical programming and a genetic algorithm (GA). Both procedures successfully generated optimized belt structures. The design variables in the mathematical programming were belt angle and belt width. Using the merits of a GA which enabled the use of

Akihiko Abe; Tatsuihko Kamegawa; Yukio Nakajima

2004-01-01

312

Genetic-algorithm-directed polarimetric sensing for optimum pattern classification  

Microsoft Academic Search

In this paper an automated technique for adaptive radar polarimetric pattern classification is described. The approach is based on a genetic algorithm that uses probabilistic patterns separation distance function and searches for those transmit and receive states of polarization sensing angles that optimize this function. Seven pattern separation distance functions, the Rayleigh quotient, Bhattacharyya, Divergence, Kolmogorov, Matusta, Kullback-Leibler distances, and

Firooz A. Sadjadi

2004-01-01

313

Design search under probabilistic specifications using genetic algorithms  

Microsoft Academic Search

In this paper a specification-based design evaluation method is presented and applied to optimization using genetic algorithms. The evaluation model is intended to emulate how specifications are used by product designers in a concurrent design environment. The approach allows designers to formulate optimization problems in terms of design specifications. The method is intended for a wide variety of design applications,

David R. Wallace; Mark J. Jakiela; Woodie C. Flowers

1996-01-01

314

All-optical microwave filter design employing a genetic algorithm  

Microsoft Academic Search

The problem of synthesizing all-optical networks for microwave frequency filtering applications is addressed. Analysis techniques are based upon established discrete time signal theory, but the assumption of incoherent optical interference restricts the time domain output to positive values, thus preventing the reliable application of traditional digital filter design methods. A new approach, based on the application of genetic algorithms, is

Thomas A. Cusick; Stavros Iezekiel; Robert E. Miles

1998-01-01

315

A new genetic algorithm for solving nonconvex nonlinear programming problems  

Microsoft Academic Search

In nonlinear programming problems (especially nonconvex problems), attaining the global optimum is crucial. To reach this purpose, the current paper represents a new genetic algorithm for solving nonconvex nonlinear programming problems. The new method is simpler and more intuitive than the existing models and finds the global optimum of the problem in a reasonable time. The proposed technique, to attain

M. B. Aryanezhad; Mohammad Hemati

2008-01-01

316

An Improved Genetic Algorithm for Job Shop Scheduling Problem  

Microsoft Academic Search

Job shop scheduling problem is a typical NP-hard problem. In this paper, new designed crossover and mutation operators based on the characteristic of the job shop problem itself are specifically designed. Based on these, an improved genetic algorithm is proposed. The computer simulations are made on a set of benchmark problems and the results indicate the effectiveness of the proposed

Ren Qing-dao-er-ji; Yuping Wang; Xiaojing Si

2010-01-01

317

Genetic Algorithm Application to the Standard Arabic Phonemes Classification  

Microsoft Academic Search

The goal of this article is the application of genetic algorithms (GAs) to the automatic speech recognition (ASR) domain at the acoustic sequences classification level. Speech recognition has been cast as a pattern classification problem where we would like to classify an input acoustic signal into one of all possible phonemes. Also, the supervised classification has been formulated as a

M. Aissiou; M. Guerti

2008-01-01

318

Genetic algorithm for control design of biped locomotion  

Microsoft Academic Search

Dynamic biped walking is a difficult control problem. The problem involves the design of the controller as well as the gait. In this paper, the design of the controller and the gait is formulated as a parameter search problem and a genetic algorithm is applied to help the design. Designs to achieve different goals, such as being able to walk

M.-Y. Cheng; C.-S. Lin

1995-01-01

319

GENETIC ALGORITHMS AND OPTIMIZING CHEMICAL OXYGEN-IODINE LASERS  

Microsoft Academic Search

This paper presents results from the first known application of the genetic algorithm (GA) technique for optimizing the performance of a laser system (chemical, solid-state, or gaseous). The effects of elitism, single point and uniform crossover, creep mutation, different random number seeds, population size, niching and the number of children per pair of parents on the performance of the GA

David L. Carroll

1996-01-01

320

MRCD: a genetic algorithm for multiobjective robust control design  

Microsoft Academic Search

A genetic algorithm (GA) for the class of multiobjective optimization problems that appears in the design of robust controllers is presented in this paper. The design of a robust controller is a trade-off problem among competitive objectives such as disturbance rejection, reference tracking, stability against unmodeled dynamics, moderate control effort and so on. However, general methodologies for solving this class

Alberto Herreros; Enrique Baeyens; José R. Perán

2002-01-01

321

A Flipping Genetic Algorithm for Hard 3SAT Problems  

Microsoft Academic Search

In this paper we propose a novel heuristicbased genetic algorithm for solving the satisfiabilityproblem. The idea is to act repeatedlyon a population of candidate solutions:at each iteration, first a simple local searchprocedure is applied to each element of thepopulation; next the genetic operators (selection,recombination and mutation) are appliedto the resulting population.Extensive experiments are performed onbenchmark instances from the literature.The results

Elena Marchiori; Claudio Rossi

322

HGA-COFFEE : Aligning Multiple Sequences by Hybrid Genetic Algorithm  

Microsoft Academic Search

\\u000a For multiple sequence alignment problem in molecular biological sequence analysis, a hybrid genetic algorithm and an associated\\u000a software package called HGA-COFFEE are presented. The COFFEE function is used to measure individual fitness, and five novel\\u000a genetic operators are designed, a selection operator, two crossover operators and two mutation operators. One of the mutation\\u000a operators is designed based on the COFFEE’s

Li-fang Liu; Hong-wei Huo; Bao-shu Wang

2005-01-01

323

A novel compact genetic algorithm using offspring survival evolutionary strategy  

Microsoft Academic Search

This article describes a compact genetic algorithm (cGA) with an offspring survival evolutionary strategy. The cGA requires\\u000a less memory than the population-based GA since the whole population is not necessary. The cGA can easily be implemented because\\u000a it has no complex genetic operator. However, the cGA requires a large amount of fitness evaluation to provide acceptable solutions\\u000a in problems involving

Joon-Hong Seok; Ju-Jang Lee

2009-01-01

324

Bayesian network-based non-parametric compact genetic algorithm  

Microsoft Academic Search

We present a non-parametric compact genetic algorithm (cGA) employing a new update strategy of the probability vector (PV) based on Bayesian networks. Since the cGAs use the PV of the current population to reproduce offsprings of the next generation instead of the genetic operators such as crossover and mutation, the cGA needs no parameter tuning. Besides, the cGA has some

Joon-Yong Lee; Soung-Min Im; Ju-Jang Lee

2008-01-01

325

Global structual optimizations of surface systems with a genetic algorithm  

SciTech Connect

Global structural optimizations with a genetic algorithm 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 genetic algorithm coupled with a tight-binding potential. Second, a genetic algorithm 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 genetic algorithm 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 genetic algorithm 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.

Chuang, Feng-Chuan

2005-05-01

326

Parameter tuning of production scheduling rules by an ant system-embedded genetic algorithm  

Microsoft Academic Search

In this paper, a search algorithm is proposed for parameter tuning of production rules in job shop scheduling problems. This algorithm is developed based on the genetic algorithm, which is the core for exploration in the search space. Then an ant system is incorporated, which directs the genetic algorithm to search in the potential regions by marking potential genes for

Tsung-che Chiang; Li-chen Fu

2004-01-01

327

Multiple Magnetic Dipole Modeling Coupled with a Genetic Algorithm  

NASA Astrophysics Data System (ADS)

Magnetic field measurements of scientific spacecraft can be modelled successfully with the multiple magnetic dipole method. The existing GANEW software [1] uses a modified Gauss-Newton algorithm to find good magnetic dipole models. However, this deterministic approach relies on suitable guesses of the initial parameters which require a lot of expertise and time-consuming interaction of the user. Here, the use of probabilistic methods employing genetic algorithms is put forward. Stochastic methods like these are well- suited for providing good initial starting points for GANEW. Furthermore a computer software is reported upon that was successfully tested and used for a Cluster II satellite.

Lientschnig, G.

2012-05-01

328

Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm  

Microsoft Academic Search

Abstract This paper proposes a dependency structure matrix driven genetic algorithm (DSMDGA) which utilizes the dependency structure matrix (DSM) clustering to extract building block (BB) information and use the information to accomplish BB - wise crossover A population sizing model is also built for the DSMDGA Three cases: tight, loose, and random linkage, are tested on both a DSMDGA and

Tian-li Yu; David E. Goldberg; Ali Yassine; Ying-ping Chen

2003-01-01

329

Conformation of an ideal bucky ball molecule by genetic algorithm and geometric constraint from pair distance data: genetic algorithm  

Microsoft Academic Search

A genetic algorithm is proposed with real value variables, spatially based crossover operator, a small mutation, large scale mutation, vector sum local search and geometric only based objective function to generate candidate molecule conformations from atomic pair distance data. To better simulate experimental data only information from the pair distance data is used as constraints. Ideal Bucky ball with 60

David M. Cherba; William F. Punch; Phil Duxbury; Simon Billinge; Pavol Juhas

2005-01-01

330

Genetic algorithms for generalised hypertree decompositions  

Microsoft Academic Search

Many practical problems in mathematics and computer science may be formulated as Constraint Satisfaction Problems (CSPs). Although CSPs are NP-hard in general, it has been proven that instances of CSPs may be solved efficiently, if they have generalised hypertree decompositions of small width. Unfortunately, finding a generalised hypertree decomposition of minimum width is an NP-hard problem. Based on a Genetic

Nysret Musliu; Werner Schafhauser

2007-01-01

331

A Memory-Efficient Elitist Genetic Algorithm  

Microsoft Academic Search

This paper proposes a memory-efficient elitist genetic algo- rithm (me 2 GA) for solving hard optimization problems quickly and ef- fectively. The idea is to properly reconcile multiple probability (distribu- tion) vectors (PVs) with elitism. Multiple PVs (rather than a single PV as in compact GA (cGA)) provide an effective framework for represent- ing the population as a probability distribution

Chang Wook Ahn; Ki Pyo Kim; Rudrapatna S. Ramakrishna

2003-01-01

332

Genetic algorithms and their use in geophysical problems  

NASA Astrophysics Data System (ADS)

Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or "fittest" models from a "population" and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show that certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Also, optimal efficiency is usually achieved with smaller (<50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (>2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free parameters and with computationally expensive objective function calculations. More sophisticated techniques are presented for special problems. Niching and island model algorithms are introduced as methods to find multiple, distinct solutions to the nonunique problems that are typically seen in geophysics. Finally, hybrid algorithms are investigated as a way to improve the efficiency of the standard genetic algorithm.

Parker, Paul Bradley

333

RCQ-GA: RDF Chain Query Optimization Using Genetic Algorithms  

NASA Astrophysics Data System (ADS)

The application of Semantic Web technologies in an Electronic Commerce environment implies a need for good support tools. Fast query engines are needed for efficient querying of large amounts of data, usually represented using RDF. We focus on optimizing a special class of SPARQL queries, the so-called RDF chain queries. For this purpose, we devise a genetic algorithm called RCQ-GA that determines the order in which joins need to be performed for an efficient evaluation of RDF chain queries. The approach is benchmarked against a two-phase optimization algorithm, previously proposed in literature. The more complex a query is, the more RCQ-GA outperforms the benchmark in solution quality, execution time needed, and consistency of solution quality. When the algorithms are constrained by a time limit, the overall performance of RCQ-GA compared to the benchmark further improves.

Hogenboom, Alexander; Milea, Viorel; Frasincar, Flavius; Kaymak, Uzay

334

Genetic algorithms for industrial ethernet network design  

Microsoft Academic Search

The Ethernet network is increasingly being used for industrial communications which are strongly time-constrained. This kind of network is intrinsically nondeterministic and does not guarantee that communication end-to-end delays will be bounded. Nevertheless, from the observed traffic between industrial devices, some network topologies improving the availability and temporal performances can be designed. This paper describes the use of a genetic

Nicolas Krommenacker; Eric Rondeau; Thieny Divoux

2002-01-01

335

Solving the Sayre equations for centrosymmetric structures with a genetic algorithm  

NASA Astrophysics Data System (ADS)

A new method employing Sayre's equations and a genetic algorithm to solve the phase problem is developed for centrosymmetric structures. Compared to simulated annealing, a genetic algorithm is more efficient.

Zhou, Y.; Su, W.-P.

2004-07-01

336

Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics.  

National Technical Information Service (NTIS)

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and es...

T. Kobayashi D. L. Simon

2001-01-01

337

Locomotive assignment problem with train precedence using genetic algorithm  

NASA Astrophysics Data System (ADS)

This paper aims to study the locomotive assignment problem which is very important for railway companies, in view of high cost of operating locomotives. This problem is to determine the minimum cost assignment of homogeneous locomotives located in some central depots to a set of pre-scheduled trains in order to provide sufficient power to pull the trains from their origins to their destinations. These trains have different degrees of priority for servicing, and the high class of trains should be serviced earlier than others. This problem is modeled using vehicle routing and scheduling problem where trains representing the customers are supposed to be serviced in pre-specified hard/soft fuzzy time windows. A two-phase approach is used which, in the first phase, the multi-depot locomotive assignment is converted to a set of single depot problems, and after that, each single depot problem is solved heuristically by a hybrid genetic algorithm. In the genetic algorithm, various heuristics and efficient operators are used in the evolutionary search. The suggested algorithm is applied to solve the medium sized numerical example to check capabilities of the model and algorithm. Moreover, some of the results are compared with those solutions produced by branch-and-bound technique to determine validity and quality of the model. Results show that suggested approach is rather effective in respect of quality and time.

Noori, Siamak; Ghannadpour, Seyed Farid

2012-07-01

338

Threshold matrix for digital halftoning by genetic algorithm optimization  

NASA Astrophysics Data System (ADS)

Digital halftoning is used both in low and high resolution high quality printing technologies. Our method is designed to be mainly used for low resolution ink jet marking machines to produce both gray tone and color images. The main problem with digital halftoning is pink noise caused by the human eye's visual transfer function. To compensate for this the random dot patterns used are optimized to contain more blue than pink noise. Several such dot pattern generator threshold matrices have been created automatically by using genetic algorithm optimization, a non-deterministic global optimization method imitating natural evolution and genetics. A hybrid of genetic algorithm with a search method based on local backtracking was developed together with several fitness functions evaluating dot patterns for rectangular grids. By modifying the fitness function, a family of dot generators results, each with its particular statistical features. Several versions of genetic algorithms, backtracking and fitness functions were tested to find a reasonable combination. The generated threshold matrices have been tested by simulating a set of test images using the Khoros image processing system. Even though the work was focused on developing low resolution marking technology, the resulting family of dot generators can be applied also in other halftoning application areas including high resolution printing technology.

Alander, Jarmo T.; Mantere, Timo; Pyylampi, Tero

1998-10-01

339

GENETIC DISTANCE MEASURE FOR K-MODES ALGORITHM  

Microsoft Academic Search

K-means algorithm has been shown to be an effective and efficient algorithm for clustering. However, the k-means algorithm is developed for numerical data only. It is not suitable for the clustering of non-numerical data. K-modes algorithm has been developed for clustering categorical objects by extending from the k-means algorithm. However, no one applies this technique for classification of categorical data.

Ching-San Chiang; Shu-Chuan Chu; Yi-Chih Hsin; Ming-Hui Wang

2006-01-01

340

Implementation of advanced genetic algorithm to modern power system stabilization control  

Microsoft Academic Search

This paper focuses on the use of advanced techniques in genetic algorithm for solving power system stabilization control problems. At the outset, the proposed hierarchical genetic algorithm (HGA) and parallel micro genetic algorithm (parallel microGA) are proposed and then they will be extended to solve two example problems. In the first example, these techniques are applied to simultaneously tune power

K. Hongesombut; Y. Mitani

2004-01-01

341

Use of genetic algorithms with backpropagation in training of feedforward neural networks  

Microsoft Academic Search

Genetic algorithms are searching strategies available for finding the globally optimal solution. The problem of genetic algorithms is that they are inherently slow. A hybrid of genetic and backpropagation algorithms (GA-BP) that should always find the correct global minima without getting stuck at local minima is presented. Various versions of the GA-BP method are presented and experimental results show that

M. McInerney; Atam P. Dhawan

1993-01-01

342

Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis  

Microsoft Academic Search

Genetic algorithms play a significant role, as search techniques for handling com- plex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms 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

1998-01-01

343

Design of wavelength-selective waveplates using genetic algorithm  

NASA Astrophysics Data System (ADS)

Wavelength-selective waveplates, which act either identically or differently for plural wavelengths, are useful for optical systems that handle plural wavelengths. However, they cannot be analytically designed because of the complexity of their structure. Genetic algorithm is one of the methods for solving optimization problems and is used for several kinds of optical design (e.g., design of thin films, diffractive optical elements, and lenses). I considered that it is effective for designing wavelength-selective waveplates also and tried to design them using the genetic algorithm for the first time to the best of my knowledge. As a result, four types of wavelength-selective waveplate for three wavelengths (405, 650, and 780 nm) were successfully designed. These waveplates are useful for Blu-ray Disc/Digital Versatile Disc/Compact Disc compatible optical pickups.

Katayama, Ryuichi

2013-03-01

344

Genetic algorithm for multiple-target-tracking data association  

NASA Astrophysics Data System (ADS)

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 genetic algorithms (GA) for the multiple target tracking data association problem. GA are search methods based on the mechanics of natural selection and genetics. They have been proven theoretically and empirically robust in complex space searches by the founder J. H. Holland. Contrary to most optimization techniques, which seek to improve performance toward the optimum, GA find near-optimal solutions through parallel searches in the solution space. We propose to optimize a simplified version of the neural energy function proposed by Sengupta and Iltis in their network implementation of the joint probability data association. We follow an identical approach by first implementing a GA for the travelling salesperson problem based on Hopfield and Tank's neural network research.

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

1996-06-01

345

Calibration of FRESIM for Singapore expressway using genetic algorithm  

SciTech Connect

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 genetic algorithm 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 genetic algorithm 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.

Cheu, R.L.; Jin, X.; Srinivasa, D. [National Univ. of Singapore (Singapore); Ng, K.C.; Ng, Y.L. [CET Technologies Pte. LTD, Singapore (Singapore)

1998-11-01

346

Users guide to the PGAPack parallel genetic algorithm library  

SciTech Connect

PGAPack is a parallel genetic algorithm library that is intended to provide most capabilities desired in a genetic algorithm package, in an integrated, seamless, and portable manner. Key features of PGAPack are as follows: Ability to be called from Fortran or C. Executable on uniprocessors, multiprocessors, multicomputers, and workstation networks. Binary-, integer-, real-, and character-valued native data types. Object-oriented data structure neutral design. Parameterized population replacement. Multiple choices for selection, crossover, and mutation operators. Easy integration of hill-climbing heuristics. Easy-to-use interface for novice and application users. Multiple levels of access for expert users. Full extensibility to support custom operators and new data types. Extensive debugging facilities. Large set of example problems.

Levine, D.

1996-01-01

347

Multi-Objective Optimization of Spectra Using Genetic Algorithms  

Microsoft Academic Search

This paper applies genetic algorithms (GAs), a powerful general-purpose biologically motivated optimization technique, to the multi-objective problem of spectrum optimization. Two objectives, color and efficiency, are address using real spectra, although the addition of other objectives (e.g., color rendering, color temperature) is relatively straightforward. The direct application of the method presented is to transform the spectrum of newly developed lighting

Neil H. Eklund; Oak Grove Scientific; Mark J. Embrechts

348

Steady-state genetic algorithms for discrete optimization of trusses  

Microsoft Academic Search

This paper presents the applications of steady-state genetic algorithms to discrete optimization of trusses. It is mathematically formulated as a constrained nonlinear optimization problem with discrete design variables. Discrete design variables are treated by a two-stage mapping process which is constructed by the mapping relationships between unsigned decimal integers and discrete values. With small generation gap and careful modification, steady-state

Shyue-Jian Wu; Pei-Tse Chow

1995-01-01

349

A multi-GPU implementation of a Cellular Genetic Algorithm  

Microsoft Academic Search

In this paper, we present a novel implementation of a Cellular Genetic Algorithm (cGA) model for a multi-GPU platform using NVIDIA's CUDA technology. This multi-GPU cGA model is compared first against a serial version in CPU and then versus an implementation on a single GPU. We divide the different operations of the cGA into distinct sets of instructions called kernels.

Pablo Vidal; Enrique Alba

2010-01-01

350

Decision support for irrigation project planning using a genetic algorithm  

Microsoft Academic Search

This work presents a model based on on-farm irrigation scheduling and the simple genetic algorithm optimization (GA) method for decision support in irrigation project planning. The proposed model is applied to an irrigation project located in Delta, Utah of 394.6ha in area, for optimizing economic profits, simulating the water demand, crop yields, and estimating the related crop area percentages with

Sheng-Feng Kuo; Gary P. Merkley; Chen-Wuing Liu

2000-01-01

351

Multi-objective design space exploration using genetic algorithms  

Microsoft Academic Search

In this work, we provide a technique for efficiently exploring a parameterized system-on-a-chip (SoC) architecture to find all Pareto-optimal configurations in a multi-objective design space. Globally, our approach uses a parameter dependency model of our target parameterized SoC architecture to extensively prune non-optimal sub-spaces. Locally, our approach applies Genetic Algorithms (GAs) to discover Pareto-optimal configurations within the remaining design points.

Maurizio Palesi; Tony Givargis

2002-01-01

352

A genetic algorithm for ground-based telescope observation scheduling  

NASA Astrophysics Data System (ADS)

A prototype genetic algorithm (GA) is being developed to provide assisted and ultimately automated observation scheduling functionality. Harnessing the logic developed for manual queue preparation, the GA can build suitable sets of queues for the potential combinations of environmental and atmospheric conditions. Evolving one step further, the GA can select the most suitable observation for any moment in time, based on allocated priorities, agency balances, and realtime availability of the skies' condition.

Mahoney, William; Veillet, Christian; Thanjavur, Karun

2012-09-01

353

A Genetic Algorithm Scheme for Pairing Meteorite Finds  

NASA Astrophysics Data System (ADS)

A genetic algorithm is employed to perform the pairing of meteorite fragments based on various characteristics measured from thin sections using an image analysis program, and analyses routinely carried out during classification. The genetic algorithm searches for best group pairings by: generating a population of trial pairs; linking them together to form groups; and evolving the population so that only pairs that are members of likely pairing groups survive to the next generation of the population. In this way meaningful pairing groups will emerge from the population, as long as characteristics from within real pairing groups have variance sufficiently small compared to the variance between groups. What constitutes `sufficiently small' is discussed and investigated by testing the genetic algorithm method on artificial data, which shows that, in principle, the method can achieve a 100 success rate. The method is then tested on real data whose pairing groups are definitely known. This is achieved by gathering data from the image processing of several scenes of the same meteorite thin section, treating each scene as a separate fragment. Using thin sections from the Reg el Acfer meteorite population, we find that the genetic algorithm identifies almost all of the main pairing groups, with about half the groups being found in their entirety; the pair-wise success rate being 76. Although this methodology requires some refinement before it could be applied to a population of meteorite fragments, these preliminary results are encouraging. The potential benefit of an automated approach lies in the tremendous savings in time and effort, allowing meaningful and reproducible pairings to be made from data sets which are prohibitively large for a human being.

Conway, A. J.; Bland, P. A.

1998-05-01

354

Multiobjective programming using uniform design and genetic algorithm  

Microsoft Academic Search

Abstract: The notion of Pareto-optimality is one of the majorapproaches to multiobjective programming. While it is desirableto find more Pareto-optimal solutions, it is also desirable to findthe ones scattered uniformly over the Pareto frontier in order toprovide a variety of compromise solutions to the decision maker.In this paper, we design a genetic algorithm for this purpose. Wecompose multiple fitness functions

Yiu-wing Leung; Yuping Wang

2000-01-01

355

Genetic algorithm for designing DWDM optical networks under demand uncertainty  

Microsoft Academic Search

Delivery of the required QoS in IP-over-DWDM networks would face uncertain environment capacity problems due to demand variations over different periods of network operation. Therefore, future demand uncertainties should be considered in planning DWDM transport backbones for capacity sensitive telecommunication applications with enhanced quality of service. This paper, presents a genetic algorithm optimization approach for designing robust optical cores by

Y. S. Kavian; W. Ren; H. F. Rashvand; M. S. Leeson; M. Naderi; E. L. Hines

2009-01-01

356

Epistasis in Genetic Algorithms: An Experimental Design Perspective  

Microsoft Academic Search

In an earlier paper we examined the relationshipbetween genetic algorithms (GAs)and traditional methods of experimental design.This was motivated by an investigationinto the problems caused by epistasis inthe implementation and application of GAsto optimization problems. We showed howthis viewpoint enables us to gain further insightsinto the determination of epistatic effects,and into the value of different forms ofencoding a problem for a

Colin R. Reeves; Christine C. Wright

1995-01-01

357

Using genetic algorithms to find technical trading rules1  

Microsoft Academic Search

We use a genetic algorithm 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

Franklin Allen; Risto Karjalainen

1993-01-01

358

Efficiency of Modified Genetic Algorithms on Two-Dimensional System  

NASA Astrophysics Data System (ADS)

The genetic algorithms (GA) have been successfully applied to optimization problems in a variety of areas. In this paper, some modifications to GA are designed to study their performance and efficiency on a typical 2D system, 2D Ising spin glass. In particular, two kinds of modified GAs are compared for their searching ability (performance) and efficiency (convergence), by presenting a direct and visual criterion. Finally, some constructive comments and prospects on GA are presented.

Fan, Lewen; Fang, Haiping; Lin, Zhifang

359

Updating Strategy in Compact Genetic Algorithm Using Moving Average Approach  

Microsoft Academic Search

The compact genetic algorithm (cGA) has a distinct characteristic that it requires almost minimal memory to store candidate solutions. It represents a population structure as a probability distribution over the set of solutions. Although cGA offers many advantages, it has a limitation that hinges on an assumption of the independency between each individual bit. For example, cGA fails to solve

Sunisa Rimcharoen; Daricha Sutivong; Prabhas Chongstitvatana

2006-01-01

360

Real-Valued Compact Genetic Algorithms for Embedded Microcontroller Optimization  

Microsoft Academic Search

Recent research on compact genetic algorithms (cGAs) has proposed a number of evolutionary search methods with reduced memory requirements. In cGAs, the evolution of populations is emulated by processing a probability vector with specific update rules. This paper considers the implementation of cGAs in microcontroller-based control platforms. In particular, to overcome some problems related to the binary encoding schemes adopted

Ernesto Mininno; Francesco Cupertino; David Naso

2008-01-01

361

Toward Virtual Machine Packing Optimization Based on Genetic Algorithm  

Microsoft Academic Search

To enable efficient resource provisioning in HaaS (Hardware as a Service) cloud systems, virtual machine packing, which migrate\\u000a virtual machines to minimize running real node, is essential. The virtual machine packing problem is a multi-objective optimization\\u000a problem with several parameters and weights on parameters change dynamically subject to cloud provider preference. We propose\\u000a to employ Genetic Algorithm (GA) method, that

Hidemoto Nakada; Takahiro Hirofuchi; Hirotaka Ogawa; Satoshi Itoh

2009-01-01

362

A Grouping Genetic Algorithm for Graph Colouring and Exam Timetabling  

Microsoft Academic Search

It has frequently been reported that pure genetic algorithms for graph colouring are in general outperformed by more conventional\\u000a methods. There is every reason to believe that this is mainly due to the choice of an unsuitable encoding of solutions. Therefore,\\u000a an alternative representation, based on the grouping character of the graph colouring problem, was chosen. Furthermore, a fitness function

Wilhelm Erben

2000-01-01

363

Investigation of range extension with a genetic algorithm  

SciTech Connect

Range optimization is one of the tasks associated with the development of cost- effective, stand-off, air-to-surface munitions systems. The search for the optimal input parameters that will result in the maximum achievable range often employ conventional Monte Carlo techniques. Monte Carlo approaches can be time-consuming, costly, and insensitive to mutually dependent parameters and epistatic parameter effects. An alternative search and optimization technique is available in genetic algorithms. In the experiments discussed in this report, a simplified platform motion simulator was the fitness function for a genetic algorithm. The parameters to be optimized were the inputs to this motion generator and the simulator`s output (terminal range) was the fitness measure. The parameters of interest were initial launch altitude, initial launch speed, wing angle-of-attack, and engine ignition time. The parameter values the GA produced were validated by Monte Carlo investigations employing a full-scale six-degree-of-freedom (6 DOF) simulation. The best results produced by Monte Carlo processes using values based on the GA derived parameters were within - 1% of the ranges generated by the simplified model using the evolved parameter values. This report has five sections. Section 2 discusses the motivation for the range extension investigation and reviews the surrogate flight model developed as a fitness function for the genetic algorithm tool. Section 3 details the representation and implementation of the task within the genetic algorithm framework. Section 4 discusses the results. Section 5 concludes the report with a summary and suggestions for further research.

Austin, A. S., LLNL

1998-03-04

364

Determining Hydraulic Characteristics of Production Wells using Genetic Algorithm  

Microsoft Academic Search

Proper well management requires the determination of characteristic hydraulic parameters of production wells such as well loss coefficient (C) and aquifer loss coefficient (B), which are conventionally determined by the graphical analysis ofstep-drawdowntest data. However, in the present study, the efficacy of a non-conventional optimization technique called Genetic Algorithm (GA), which ensures near-optimal or optimal solutions, is assessedin determining well

Madan K. Jha; Gaurav Nanda; Manoj P. Samuel

2004-01-01

365

Using a Genetic Algorithm to Solve the Generalized Orienteering Problem  

Microsoft Academic Search

In this chapter, we use genetic algorithms (GAs) to solve the generalized orienteering problem (GOP). In the orienteering\\u000a problem (OP), we are given a transportation network in which a start point and an end point are specified, and other points\\u000a have associated scores. Given a fixed amount of time, the goal is to determine a path from start to end

Xia Wang; Bruce L. Golden; Edward A. Wasil

366

Pareto optimization using the struggle genetic crowding algorithm  

Microsoft Academic Search

Many real-world engineering design problems involve the simultaneous optimization of several conflicting objectives. In this paper, a method combining the struggle genetic crowding algorithm with Pareto-based population ranking is proposed to elicit trade-off frontiers. The new method has been tested on a variety of published problems, reliably locating both discontinuous Pareto frontiers as well as multiple Pareto frontiers in multi-modal

Johan Andersson; David Wallace

2002-01-01

367

OPTIMIZATION OF LONG RURAL FEEDERS USING A GENETIC ALGORITHM  

SciTech Connect

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 Genetic Algorithm (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)

2010-06-15

368

A meta-learning system based on genetic algorithms  

NASA Astrophysics Data System (ADS)

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 genetic algorithms 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 genetic algorithms 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 genetic algorithms and their capability to learn to learn.

Pellerin, Eric; Pigeon, Luc; Delisle, Sylvain

2004-04-01

369

Optimization of Optical Systems Using Genetic Algorithms: a Comparison Among Different Implementations of The Algorithm  

NASA Astrophysics Data System (ADS)

The Genetic Algorithms, GAs, are a method of global optimization that we use in the stage of optimization in the design of optical systems. In the case of optical design and optimization, the efficiency and convergence speed of GAs are related with merit function, crossover operator, and mutation operator. In this study we present a comparison between several genetic algorithms implementations using different optical systems, like achromatic cemented doublet, air spaced doublet and telescopes. We do the comparison varying the type of design parameters and the number of parameters to be optimized. We also implement the GAs using discreet parameters with binary chains and with continuous parameter using real numbers in the chromosome; analyzing the differences in the time taken to find the solution and the precision in the results between discreet and continuous parameters. Additionally, we use different merit function to optimize the same optical system. We present the obtained results in tables, graphics and a detailed example; and of the comparison we conclude which is the best way to implement GAs for design and optimization optical system. The programs developed for this work were made using the C programming language and OSLO for the simulation of the optical systems.

López-Medina, Mario E.; Vázquez-Montiel, Sergio; Herrera-Vázquez, Joel

2008-04-01

370

A multi-objective genetic algorithm approach to the design of cellular manufacturing systems  

Microsoft Academic Search

In this paper, a multi-objective integer programming model is constructed for the design of cellular manufacturing systems with independent cells. A genetic algorithm with multiple fitness functions is proposed to solve the formulated problem. The proposed algorithm finds multiple solutions along the Pareto optimal frontier. There are some features that make the proposed algorithm different from other algorithms used in

Maghsud Solimanpur; Prem Vrat; Ravi Shankar

2004-01-01

371

Cellular Genetic Algorithms and Local Search for 3SAT problem on Graphic Hardware  

Microsoft Academic Search

As a well known NP-hard problem, SAT problem is widely discussed by computer science society. In this paper, two common algorithms for SAT problems are implemented based on graphic hardware. They are greedy local search and genetic algorithm. After a brief description of the basic algorithm, we give our modification of the algorithm for fitting with graphic hardware's SIMD model.

Zhongwen Luo; Hongzhi Liu

2006-01-01

372

Application of vector optimization employing modified genetic algorithm to permanent magnet motor design  

Microsoft Academic Search

This paper presents a method to solve the vector optimization problem that determines both the noninferior solution set and the best compromise solution employing a modified genetic algorithm. The algorithm differs from the conventional one in the definition of fitness value and convergence criterion. Some parameters of the algorithm are adjusted to the vector optimization. The algorithm also contains the

Dong-Joon Sim; Hyun-Kyo Jung; Song-Yop Hahn; Jong-Soo Won

1997-01-01

373

A Hybrid Metaheuristic for Biclustering Based on Scatter Search and Genetic Algorithms  

NASA Astrophysics Data System (ADS)

In this paper a hybrid metaheuristic for biclustering based on Scatter Search and Genetic Algorithms is presented. A general scheme of Scatter Search has been used to obtain high-quality biclusters, but a way of generating the initial population and a method of combination based on Genetic Algorithms have been chosen. Experimental results from yeast cell cycle and human B-cell lymphoma are reported. Finally, the performance of the proposed hybrid algorithm is compared with a genetic algorithm recently published.

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

374

Optimization of Circular Ring Microstrip Antenna Using Genetic Algorithm  

NASA Astrophysics Data System (ADS)

Circular ring microstrip antennas have several interesting properties that make it attractive in wireless applications. Although several analysis techniques such as cavity model, generalized transmission line model, Fourier-Hankel transform domain and the method of matched asymptotic expansion have been studied by researchers, there is no efficient design tool that has been incorporated with a suitable optimization algorithm. In this paper, the cavity model analysis along with the genetic optimization algorithm is presented for the design of circular ring microstrip antennas. The method studied here is based on the well-known cavity model and the optimization of the dimensions and feed point location of the circular ring antenna is performed via the genetic optimization algorithm, to achieve an acceptable antenna operation around a desired resonance frequency. The antennas designed by this efficient design procedure were realized experimentally, and the results are compared. In addition, these results are also compared to the results obtained by the commercial electromagnetic simulation tool, the FEM based software, HFSS by ANSOFT.

Sathi, V.; Ghobadi, Ch.; Nourinia, J.

2008-10-01

375

Library design using genetic algorithms for catalyst discovery and optimization  

NASA Astrophysics Data System (ADS)

This study reports a detailed investigation of catalyst library design by genetic algorithm (GA). A methodology for assessing GA configurations is described. Operators, which promote the optimization speed while being robust to noise and outliers, are revealed through statistical studies. The genetic algorithms were implemented in GA platform software called OptiCat, which enables the construction of custom-made workflows using a tool box of operators. Two separate studies were carried out (i) on a virtual benchmark and (ii) on real surface response which is derived from HT screening. Additionally, we report a methodology to model a complex surface response by binning the search space in small zones that are then independently modeled by linear regression. In contrast to artificial neural networks, this approach allows one to obtain an explicit model in an analogical form that can be further used in Excel or entered in OptiCat to perform simulations. While speeding the implementation of a hybrid algorithm combining a GA with a knowledge-based extraction engine is described, while speeding up the optimization process by means of virtual prescreening the hybrid GA enables one to open the ``black-box'' by providing knowledge as a set of association rules.

Clerc, Frederic; Lengliz, Mourad; Farrusseng, David; Mirodatos, Claude; Pereira, Sílvia R. M.; Rakotomalala, Ricco

2005-06-01

376

Anisotropic Crustal Structure Inversion Using a Niching Genetic Algorithm.  

NASA Astrophysics Data System (ADS)

Seismic anisotropy in the crust can provide important constraints about past and present tectonic processes. The inversion of seismograms with anisotropic effects is a problem that is not easily linearized, and consequently linear inversion techniques are not suitable for the determination of anisotropic crustal structure. One global minimization technique that is applicable to highly nonlinear problems is that of genetic algorithms. We are using a niching genetic algorithm to invert synthetic seismograms with anisotropy in order to determine which model parameters can be resolved, how sensitive the parameters are to noise, and how many back azimuths are required to constrain the parameters. A niching genetic algorithm is useful for finding multiple potential solutions by determining various minima that are separated in the solution space be some pre-determined value. By using synthetic data we are able to compare the resultant model with the original model and determine which parameters have been correctly resolved. We are also able to introduce noise into the synthetic data and determine the effect that this has on the results. Finally, we can include a variable number of back azimuths in order to determine how many are required to constrain the model parameters suitably. We are using an algorithm that attempts to fit simultaneously the radial and transverse components of receiver functions for multiple back azimuths. It is necessary to include both components of the seismograms and multiple back azimuths in the inversion because the solutions for one component or one back azimuth are highly non-unique. By including multiple back azimuths more constraints are placed on the problem and many duplicate solutions are eliminated. So far, all our tests have been performed on models with one anisotropic layer over an isotropic half space. These tests have shown that thickness tends to be the best resolved parameter. We have also found that when multiple back azimuths are included the strike and dip of the anisotropy are well constrained, and that the percent anisotropy is the least constrained. The niching aspect of the genetic algorithm has proven to be important because we have found that in some cases a single solution test is unable to find a good solution while a multiple solution run is able to better fit the data and find a more correct solution. We hope to refine this method in the future in order to obtain better resolution of all the model parameters and apply the technique to real data from the Tibetan plateau and the Andes.

Erickson, J. P.; Koper, K. D.; Zandt, G.

2001-12-01

377

A step forward in studying the compact genetic algorithm.  

PubMed

The compact Genetic Algorithm (cGA) is an Estimation of Distribution Algorithm that generates offspring population according to the estimated probabilistic model of the parent population instead of using traditional recombination and mutation operators. The cGA only needs a small amount of memory; therefore, it may be quite useful in memory-constrained applications. This paper introduces a theoretical framework for studying the cGA from the convergence point of view in which, we model the cGA by a Markov process and approximate its behavior using an Ordinary Differential Equation (ODE). Then, we prove that the corresponding ODE converges to local optima and stays there. Consequently, we conclude that the cGA will converge to the local optima of the function to be optimized. PMID:16903794

Rastegar, Reza; Hariri, Arash

2006-01-01

378

Application of genetic algorithm in atmospheric carbon dioxide concentration retrieval  

NASA Astrophysics Data System (ADS)

This paper introduces the basic theory and method of carbon dioxide (CO2) retrieval. The key step is to search for the optimal solution and the random search algorithm Genetic Algorithm (GA) which can effectively avoid the local optimization. We first investigate the basic principles of GA in CO2 retrieval and then design the corresponding encoding and decoding methods as well as the fitness function. This newly-developed GA is further applied to retrieve the atmospheric CO2 concentration using Atmospheric Infrared Sounder (AIRS) observations from January 2006 to December 2008 centered at 20°N, 144°E. Compared to the aircraft measurements, the GA retrieval yields the small root mean square error of 1.13 ppmv and reproduces good results with the observed seasonal cycle.

Li, Jingyao; Shi, Runhe; Gao, Wei

2013-09-01

379

Multilayer Traffic Network Optimized by Multiobjective Genetic Clustering Algorithm  

NASA Astrophysics Data System (ADS)

This paper introduces a multilayer traffic network model and traffic network clustering method for solving the route selection problem (RSP) in car navigation system (CNS). The purpose of the proposed method is to reduce the computation time of route selection substantially with acceptable loss of accuracy by preprocessing the large size traffic network into new network form. The proposed approach further preprocesses the traffic network than the traditional hierarchical network method by clustering method. The traffic network clustering considers two criteria. We specify a genetic clustering algorithm for traffic network clustering and use NSGA-II for calculating the multiple objective Pareto optimal set. The proposed method can overcome the size limitations when solving route selection in CNS. Solutions provided by the proposed algorithm are compared with the optimal solutions to analyze and quantify the loss of accuracy.

Wen, Feng; Gen, Mitsuo; Yu, Xinjie

380

Optimization algorithm of digital watermarking anti-coalition attacks in DWT-domain based on genetic algorithm  

NASA Astrophysics Data System (ADS)

An adaptive optimization watermarking algorithm based on Genetic Algorithm (GA) and discrete wavelet transform (DWT) is proposed in this paper. The core of this algorithm is the fitness function optimization model for digital watermarking based on GA. The embedding intensity for digital watermarking can be modified adaptively, and the algorithm can effectively ensure the imperceptibility of watermarking while the robustness is ensured. The optimization model research may provide a new idea for anti-coalition attacks of digital watermarking algorithm. The paper has fulfilled many experiments, including the embedding and extracting experiments of watermarking, the influence experiments by the weighting factor, the experiments of embedding same watermarking to the different cover image, the experiments of embedding different watermarking to the same cover image, the comparative analysis experiments between this optimization algorithm and human visual system (HVS) algorithm and etc. The simulation results and the further analysis show the effectiveness and advantage of the new algorithm, which also has versatility and expandability. And meanwhile it has better ability of anti-coalition attacks. Moreover, the robustness and security of watermarking algorithm are improved by scrambling transformation and chaotic encryption while preprocessing the watermarking.

Que, Dashun; Li, Gang; Yue, Peng

2007-11-01

381

Research on natural language IR system based on genetic algorithm and VSM  

Microsoft Academic Search

This work brought forward a kind of arithmetic of information retrieval, namely combining the positive genes of genetic algorithm and vector space model on the base of nature language. Genetic algorithm is used for a predication case-frame of query in this system. Based on HowNet, this algorithm gains inherent characteristics of data objects, and retrieve the useful information according to

Hai-Yan Kang; Yan-Fang Li; Gui-Fa Teng; Xiao-Zhong Fan; Xiao-Yang He

2004-01-01

382

A Knowledge Based Genetic Algorithm for Path Planning in Unstructured Mobile Robot Environments  

Microsoft Academic Search

This paper proposes a knowledge based genetic algorithm (GA) for path planning of a mobile robot in unstructured environments. The algorithm uses a unique problem representation method to represent 2-dimensional robot environments with complex obstacle layouts of arbitrary obstacle shapes. An effective evaluation method is specially developed for the proposed genetic algorithm. The evaluation method is able to accurately detect

Yanrong Hu; S. X. Yang; Li-Zhong Xu; Q.-H. Meng

2004-01-01

383

An Immune System Based Genetic Algorithm Using Permutation-Based Dualism for Dynamic Traveling Salesman Problems  

Microsoft Academic Search

In recent years, optimization in dynamic environments has attracted a growing interest from the genetic algorithm community due to the importance and practicability in real world applications. This pa- per proposes a new genetic algorithm, based on the inspiration from biological immune systems, to address dynamic traveling salesman prob- lems. Within the proposed algorithm, a permutation-based dualism is introduced in

Lili Liu; Dingwei Wang; Shengxiang Yang

2009-01-01

384

Solving the Steady Flight State of Aircraft Based on Hybrid Genetic Algorithm  

Microsoft Academic Search

Steady flat flight is widely used in the flight simulator training as an ideal initial state. To ensure the accurate solving of the steady flat flight state a hybrid genetic algorithm is put forward. The algorithm based on the new concept of ldquoindividual learning potentialityrdquo make the Lamarckian learning and Baldwinina learning genetic algorithm combination together organically according to the

Luan Zhibo; Huang Qitao; Jiang Hongzhou; Li Hongren

2009-01-01

385

Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems  

Microsoft Academic Search

In this paper, a novel genetic algorithm is developed by generating artificial chro- mosomes with probability control to solve the machine scheduling problems. Generating artificial chromosomes for Genetic Algorithm (ACGA) is closely related to Evolutionary Algorithms Based on Probabilistic Models (EAPM). The artificial chromosomes are gen- erated by a probability model that extracts the gene information from current population. ACGA

Pei-Chann Chang; Shih-Hsin Chen; Chin-Yuan Fan; V. Mani

2010-01-01

386

An Introduction to Genetic Algorithms and to Their Use in Information Retrieval.  

ERIC Educational Resources Information Center

|Genetic algorithms, a class of nondeterministic algorithms in which the role of chance makes the precise nature of a solution impossible to guarantee, seem to be well suited to combinatorial-optimization problems in information retrieval. Provides an introduction to techniques and characteristics of genetic algorithms and illustrates their…

Jones, Gareth; And Others

1994-01-01

387

The design and implementation of MPI master-slave parallel genetic algorithm  

NASA Astrophysics Data System (ADS)

In this paper, the MPI master-slave parallel genetic algorithm is implemented by analyzing the basic genetic algorithm 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 genetic algorithm 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 genetic algorithms.

Liu, Shuping; Cheng, Yanliu

2013-03-01

388

Optimal design of binary phase-only filters using genetic algorithms  

NASA Astrophysics Data System (ADS)

The genetic algorithm is a mathematical optimization technique which has generally been applied to one-dimensional problems. In this work, the genetic algorithm was applied to a two-dimensional problem--the construction of binary phase-only spatial filters for optical pattern recognition. Spatial filters that are invariant to range and aspect changes are required for robust pattern recognition. Construction of invariant filters is an optimization problem where the correlation is the objective function for the genetic algorithm. Results are presented for correlation of a genetic algorithm-constructed filter with a multiple aspect angle target set. Filters using a hill-climber algorithm were also constructed and tested.

Deb, Kalyanmoy

1993-08-01

389

Analysis of the numerical effects of parallelism on a parallel genetic algorithm  

SciTech Connect

This paper examines the effects of relaxed synchronization on both the numerical and parallel efficiency of parallel genetic algorithms (GAs). We describe a coarse-grain geographically structured parallel genetic algorithm. Our experiments show that asynchronous versions of these algorithms have a lower run time than-synchronous GAs. Furthermore, we demonstrate that this improvement in performance is partly due to the fact that the numerical efficiency of the asynchronous genetic algorithm is better than the synchronous genetic algorithm. Our analysis includes a critique of the utility of traditional parallel performance measures for parallel GAs, and we evaluate the claims made by several researchers that parallel GAs can have superlinear speedup.

Hart, W.E.; Belew, R.K.; Kohn, S. [Sandia National Labs., Albuquerque, NM (United States); Baden, S. [California Univ., San Diego, La Jolla, CA (United States). Dept. of Computer Science and Engineering

1995-09-18

390

Optimal parameter estimation for Muskingum model based on Gray-encoded accelerating genetic algorithm  

NASA Astrophysics Data System (ADS)

In order to reduce the computational amount and improve the computational precision for parameter optimization of Muskingum model, a new algorithm, Gray-encoded accelerating genetic algorithm (GAGA) is proposed. With the shrinking of searching range, the method gradually directs to an optimal result with the excellent individuals obtained by Gray genetic algorithm (GGA). The global convergence is analyzed for the new genetic algorithm. Its efficiency is verified by application of Muskingum model. Compared with the nonlinear programming methods, least residual square method and the test method, GAGA has higher precision. And compared with GGA and BGA (binary-encoded genetic algorithm), GAGA has rapider convergent speed.

Chen, Jianjun; Yang, Xiaohua

2007-08-01

391

Vertical decomposition with Genetic Algorithm for Multiple Sequence Alignment  

PubMed Central

Background Many Bioinformatics studies begin with a multiple sequence alignment as the foundation for their research. This is because multiple sequence alignment can be a useful technique for studying molecular evolution and analyzing sequence structure relationships. Results In this paper, we have proposed a Vertical Decomposition with Genetic Algorithm (VDGA) for Multiple Sequence Alignment (MSA). In VDGA, we divide the sequences vertically into two or more subsequences, and then solve them individually using a guide tree approach. Finally, we combine all the subsequences to generate a new multiple sequence alignment. This technique is applied on the solutions of the initial generation and of each child generation within VDGA. We have used two mechanisms to generate an initial population in this research: the first mechanism is to generate guide trees with randomly selected sequences and the second is shuffling the sequences inside such trees. Two different genetic operators have been implemented with VDGA. To test the performance of our algorithm, we have compared it with existing well-known methods, namely PRRP, CLUSTALX, DIALIGN, HMMT, SB_PIMA, ML_PIMA, MULTALIGN, and PILEUP8, and also other methods, based on Genetic Algorithms (GA), such as SAGA, MSA-GA and RBT-GA, by solving a number of benchmark datasets from BAliBase 2.0. Conclusions The experimental results showed that the VDGA with three vertical divisions was the most successful variant for most of the test cases in comparison to other divisions considered with VDGA. The experimental results also confirmed that VDGA outperformed the other methods considered in this research.

2011-01-01

392

Flexible Job-Shop Scheduling Problem by Genetic Algorithm  

NASA Astrophysics Data System (ADS)

Flexible Job-shop Scheduling Problem is expansion of the traditional Job-shop Scheduling Problem that an operation can be processed one or more machines. The purpose of this problem is to look for the smallest makespan. For that purpose, it is necessary to decide optimal assignment of machines to operations and order of operations on machines. In this paper, we focus on maximum of workloads for all machines and propose new suvival selection, creation method of initial solution, mutation, and escape method to Genetic Algorithm for this problem. The efficacy of our method is demonstrated by comparing its numerical experiment results with another methods.

Ida, Kenichi; Oka, Kensaku

393

Optimal brushless DC motor design using genetic algorithms  

NASA Astrophysics Data System (ADS)

This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using a genetic algorithm. Characteristics of the motor are expressed as functions of motor geometries. The objective function is a combination of losses, volume and cost to be minimized simultaneously. Electrical and mechanical requirements (i.e. voltage, torque and speed) and other limitations (e.g. upper and lower limits of the motor geometries) are cast into constraints of the optimization problem. One sample case is used to illustrate the design and optimization technique.

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

2010-11-01

394

A new chromatic dispersion compensation method based on genetic algorithm  

NASA Astrophysics Data System (ADS)

In the 40Gbps high-speed optical fiber communication system, chromatic dispersion of optical signal brings about to generation of inter-symbol interface which influences the quality of optical fiber communication. In order to solve the above questions in the 40Gbps differential quarter phase-shift keying (DQPSK) optical fiber communication system, a new method of chromatic dispersion compensation based on genetic algorithm is proposed according to the demodulation of DQPSK optical signal and the trait of chromatic dispersion. Result shows that the system's receiving sensitivity has been enhanced up to six orders of magnitude.

Liu, Chun-wu; Qin, Jiang-yi; Huang, Zhi-ping; Zhang, Yi-meng

2013-08-01

395

Fuzzy logic controllers generated by pseudo-bacterial genetic algorithm with adaptive operator  

Microsoft Academic Search

This paper presents a new genetic operator called adaptive operator to improve local portions of chromesomes. This new operator is implemented in a pseudo-bacterial genetic algorithm (PBGA). The PBGA was proposed by the authors as a new approach combining a genetic algorithm (GA) with a local improvement mechanism inspired by a process in bacterial genetics. The PBGA was applied for

N. E. Nawa; T. Hashiyama; T. Furuhashi; Y. Uchikawa

1997-01-01

396

The Performance Analysis of a Multi-Objective Immune Genetic Algorithm for Flexible Job Shop Scheduling  

Microsoft Academic Search

First, a multi-objective immune genetic algorithm integrating immune algorithm and genetic algorithm for flexible job shop\\u000a scheduling is designed. Second, Markov chain is used to analyze quantitatively its convergence. Third, a simulation experiment\\u000a of the flexible job shop scheduling is carried out. Running results show that the proposed algorithm can converge to the Pareto\\u000a frontier quickly and distribute evenly along

Xiuli Wu; Shudong Sun; Ganggang Niu; Yinni Zhai

2006-01-01

397

Analysis of new niching genetic algorithms for finding multiple solutions in the job shop scheduling  

Microsoft Academic Search

In this paper the performance of the most recent multi-modal genetic algorithms (MMGAs) on the Job Shop Scheduling Problem (JSSP) is compared in term of efficacy, multi-solution based efficacy (the algorithm’s capability to find multiple optima), and diversity in the final set of solutions. The capability of Genetic Algorithms (GAs) to work on a set of solutions allows us to

E. Pérez; M. Posada; F. Herrera

398

Core Hunter: an algorithm for sampling genetic resources based on multiple genetic measures  

PubMed Central

Background Existing algorithms and methods for forming diverse core subsets currently address either allele representativeness (breeder's preference) or allele richness (taxonomist's preference). The main objective of this paper is to propose a powerful yet flexible algorithm capable of selecting core subsets that have high average genetic distance between accessions, or rich genetic diversity overall, or a combination of both. Results We present Core Hunter, an advanced stochastic local search algorithm for selecting core subsets. Core Hunter is able to find core subsets having more genetic diversity and better average genetic distance than the current state-of-the-art algorithms for all genetic distance and diversity measures we evaluated. Furthermore, Core Hunter can attempt to optimize any number of genetic measures simultaneously, based on the preference of the user. Notably, Core Hunter is able to select significantly smaller core subsets, which retain all unique alleles from a reference collection, than state-of-the-art algorithms. Conclusion Core Hunter is a highly effective and flexible tool for sampling genetic resources and establishing core subsets. Our implementation, documentation, and source code for Core Hunter is available at

Thachuk, Chris; Crossa, Jose; Franco, Jorge; Dreisigacker, Susanne; Warburton, Marilyn; Davenport, Guy F

2009-01-01

399

Genetic algorithm in the structural design of Cooke triplet lenses  

NASA Astrophysics Data System (ADS)

This paper is in tune with our efforts to develop a systematic method for multicomponent lens design. Our aim is to find a suitable starting point in the final configuration space, so that popular local search methods like damped least squares (DLS) may directly lead to a useful solution. For 'ab initio' design problems, a thin lens layout specifying the powers of the individual components and the intercomponent separations are worked out analytically. Requirements of central aberration targets for the individual components in order to satisfy the prespecified primary aberration targets for the overall system are then determined by nonlinear optimization. The next step involves structural design of the individual components by optimization techniques. This general method may be adapted for the design of triplets and their derivatives. However, for the thin lens design of a Cooke triplet composed of three airspaced singlets, the two steps of optimization mentioned above may be combined into a single optimization procedure. The optimum configuration for each of the single set, catering to the required Gaussian specification and primary aberration targets for the Cooke triplet, are determined by an application of genetic algorithm (GA). Our implementation of this algorithm is based on simulations of some complex tools of natural evolution, like selection, crossover and mutation. Our version of GA may or may not converge to a unique optimum, depending on some of the algorithm specific parameter values. With our algorithm, practically useful solutions are always available, although convergence to a global optimum can not be guaranteed. This is perfectly in keeping with our need to allow 'floating' of aberration targets in the subproblem level. Some numerical results dealing with our preliminary investigations on this problem are presented.

Hazra, Lakshminarayan; Banerjee, Saswatee

1999-08-01

400

Ancestral genome inference using a genetic algorithm approach.  

PubMed

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 genetic algorithm (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

Gao, Nan; Yang, Ning; Tang, Jijun

2013-05-02

401

Partial AUC maximization for essential gene prediction using genetic algorithms.  

PubMed

Identifying genes indispensable for an organism's life and their characteristics is one of the central questions in current biological research, and hence it would be helpful to develop computational approaches towards the prediction of essential genes. The performance of a predictor is usually measured by the area under the receiver operating characteristic curve (AUC). We propose a novel method by implementing genetic algorithms to maximize the partial AUC that is restricted to a specific interval of lower false positive rate (FPR), the region relevant to follow-up experimental validation. Our predictor uses various features based on sequence information, protein-protein interaction network topology, and gene expression profiles. A feature selection wrapper was developed to alleviate the over-fitting problem and to weigh each feature's relevance to prediction. We evaluated our method using the proteome of budding yeast. Our implementation of genetic algorithms maximizing the partial AUC below 0.05 or 0.10 of FPR outperformed other popular classification methods. PMID:23351383

Hwang, Kyu-Baek; Ha, Beom-Yong; Ju, Sanghun; Kim, Sangsoo

2013-01-01

402

Segmentation of thermographic images of hands using a genetic algorithm  

NASA Astrophysics Data System (ADS)

This paper presents a new technique for segmenting thermographic images using a genetic algorithm (GA). The individuals of the GA also known as chromosomes consist of a sequence of parameters of a level set function. Each chromosome represents a unique segmenting contour. An initial population of segmenting contours is generated based on the learned variation of the level set parameters from training images. Each segmenting contour (an individual) is evaluated for its fitness based on the texture of the region it encloses. The fittest individuals are allowed to propagate to future generations of the GA run using selection, crossover and mutation. The dataset consists of thermographic images of hands of patients suffering from upper extremity musculo-skeletal disorders (UEMSD). Thermographic images are acquired to study the skin temperature as a surrogate for the amount of blood flow in the hands of these patients. Since entire hands are not visible on these images, segmentation of the outline of the hands on these images is typically performed by a human. In this paper several different methods have been tried for segmenting thermographic images: Gabor-wavelet-based texture segmentation method, the level set method of segmentation and our GA which we termed LSGA because it combines level sets with genetic algorithms. The results show a comparative evaluation of the segmentation performed by all the methods. We conclude that LSGA successfully segments entire hands on images in which hands are only partially visible.

Ghosh, Payel; Mitchell, Melanie; Gold, Judith

2010-02-01

403

Genetic Algorithms and Experimental Discrimination of SUSY Models  

NASA Astrophysics Data System (ADS)

We introduce genetic algorithms as a means to estimate the accuracy required to discriminate among different models using experimental observables. We exemplify the technique in the context of the minimal supersymmetric standard model. If supersymmetric particles are discovered, models of supersymmetry breaking will be fit to the observed spectrum and it is beneficial to ask beforehand: what accuracy is required to always allow the discrimination of two particular models and which are the most important masses to observe? Each model predicts a bounded patch in the space of observables once unknown parameters are scanned over. The questions can be answered by minimising a ``distance'' measure between the two hypersurfaces. We construct a distance measure that scales like a constant fraction of an observable. Genetic algorithms, including concepts such as natural selection, fitness and mutations, provide a solution to the minimisation problem. We illustrate the efficiency of the method by comparing three different classes of string models for which the above questions could not be answered with previous techniques. The required accuracy is in the range accessible to the Large Hadron Collider (LHC) when combined with a future linear collider (LC) facility. The technique presented here can be applied to more general classes of models or observables.

Allanach, B. C.; Grellscheid, D.; Quevedo, F.

2004-07-01

404

Searching for allotropes of Wigner crystal clusters with Genetic Algorithm  

NASA Astrophysics Data System (ADS)

We have recently discovered the configuration existence theorem for N electrons in magnetic and circularly polarized fields stating that the maximum number of configurations may be the product of all differential foldings [1] (maximum number of times the ZVS gradient manifold is cut by even lower dimensional arbitrary plane to disjoint sets): There may be for example at least 2^180=1532495540865888858358347027150309183618739122183602176 maximum number of configurations of electrons corresponding to the complexity of carbon C60 (N=60) assuming the lowest nontrivial folding 2 (parabola-like). We use therefore genetic algorithm to find possible classical Wiger crystal allotropes leading quantum configurations for large number of electrons. We find several distinct configurations for large number of electrons. The genetic operations on configuration spiecies are also discussed. [1] M. Kalinski, L. Hansen, and D. Farrelly, Phys. Rev. Lett. 95, 103001 (2005).

Kalinski, Matt

2008-05-01

405

Genetic Algorithms And Its Application To Economic Load Dispatch  

NASA Astrophysics Data System (ADS)

Genetic Algorithm (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 set of potential solutions. This process continues until an acceptable solution is found. GA is becoming popular to solve the optimization problems mainly because of its robustness in finding optimal solution and ability to provide near optimal solution close to global optimum. The ELD approach is tested on sample 3-generator system with the load of 24 hours.

Borana, Kavita

2010-11-01

406

Fast correspondence of unrectified stereo images using genetic algorithm and spline representation  

NASA Astrophysics Data System (ADS)

Spline representations have been successfully used with a genetic algorithm to determine a disparity map for stereo image pairs. This paper describes work to modify the genetic spline algorithm to use a version of the genetic algorithm with small populations and few generations, previously referred to as "Tiny GAs", to allow algorithm implementations to achieve real-time performance. The algorithm was also targeted at unrectified stereo image pairs to reduce preprocessing making it more suitable for real-time performance. To ensure disparity map quality is preserved, the two dimensional nature of images is maintained to leverage persistent information instead of representing the images as 1-D signals as suggested in the orignal genetic spline algorithm. Experimental results are given of this modified algorithm using unrectified images.

Tippetts, Beau; Lee, Dah Jye; Archibald, James

2010-01-01

407

Object support reconstruction from the support of its autocorrelation using multiresolution genetic algorithms  

NASA Astrophysics Data System (ADS)

The problem of reconstructing the support of an imaged object from the support of its autocorrelation is addressed within the framework of genetic algorithms. First, we propose a method of coding binary sets into chromosomes that is both efficient and general, producing reasonably short chromosomes and being able to represent convex objects, as well as some non-convex and even clustered ones. Furthermore, in order to compensate for the computational costs normally incurred when genetic algorithms are applied, a novel multiresolution version of the algorithm was introduced and tested. The multiresolution genetic algorithm consists of a superposition of multiple algorithms evolving at different resolutions, sequentially. Upon occurrence of some convergence criteria at the current scale, the genetic population was mapped at a superior scale by a coarse-to- fine mapping that preserved the progress registered previously. This mapping is implemented in a genetic algorithm framework by a new genetic operator called cloning. A number of experiments of object support reconstruction were performed and the best results from different genetic generations were depicted in chronological sequence. While both versions of genetic algorithms achieved good results, the multiresolution approach wa also able to substantially improve the convergence speed of the process. The effectiveness of the method can be extended even further if a parallel implementation of the genetic algorithm is employed. Finally, alternate coding methods could be readily used in both the standard and the multiresolution approaches, with no need for further adaptations of the basic structure of the genetic algorithm.

Voicu, Liviu; Rabadi, Wissam A.; Myler, Harley R.

1997-10-01

408

A New Approach Belonging to EDAs: Quantum-Inspired Genetic Algorithm with Only One Chromosome  

Microsoft Academic Search

\\u000a The paper proposed a novel quantum-inspired genetic algorithm with only one chromosome, which we called Single-Chromosome\\u000a Quantum Genetic algorithm (SCQGA). In SCQGA, by bringing the information representation in quantum computing into the algorithm,\\u000a only one quantum chromosome (QC) is used to represent all possible states of the entire population. A novel quantum evolution\\u000a method without using conventional genetic operators such

Shude Zhou; Zengqi Sun

2005-01-01

409

Identification of a Motor with Multiple Nonlinearities by Improved Genetic Algorithm  

Microsoft Academic Search

\\u000a This paper presents a mathematical model that employs a new genetic algorithm for motor identification. Mechanical structures\\u000a require precise motor information for high control performance. However, it is difficult to acquire accurate motor information\\u000a and a genetic algorithm can be an adequate method to search unknown parameters using only angular position. The previous methods\\u000a by using conventional genetic algorithms do

Jung-shik Kong; Jin-geol Kim

2005-01-01

410

Intelligent dispatch for public traffic vehicles based on improved Genetic Algorithm  

Microsoft Academic Search

Bus intelligent scheduling problem, the application of existing passenger flow information, and to take into account the passengers and the bus companies' dual interests, is constructed to the traffic laws of encoding, genetic operators, fitness function and other improved genetic algorithm design. Finally, the simulation results are obtained by using the improved Genetic Algorithm for solving the non-uniform grid schedule.

Zhu Chang-sheng; Liang Jian-bo; Feng Wen-yi; Miao Xu-gang

2010-01-01

411

Application of genetic algorithm in tracking convective cloud images from Chinese FY2C satellite  

Microsoft Academic Search

It is significance to identify and track convective clouds using satellite data in nowcasting and severe weather warning. This article uses genetic algorithm to match and track convection clouds identified from infrared channel images of FY - 2C satellite. The preliminary results suggest that the genetic algorithm need set up enough group size & genetic algebra, and can select appropriate

Xiaofang Pei; Nan Li; Yating Zhan

2010-01-01

412

Research on Float-Coded Genetic Algorithm Based on Wavelet Denoising Mutation  

Microsoft Academic Search

Coding is a difficult subject of research on genetic algorithm (GA). In many codes, float code (FC) is super to other codes in use. But, noise and its influence on GA performance were ignored by researches in genetic operation. Mutation played an important role of improving GA performance. Hence, float-coded genetic algorithm (FCGA) based on wavelet denoising mutation (FCGAWM) was

Mingyi Cui; Yanli Shangguan

2007-01-01

413

Identification of vibration loads on hydro generator by using hybrid genetic algorithm  

Microsoft Academic Search

Vibration dynamic characteristics have been a major issue in the modeling and mechanical analysis of large hydro generators. An algorithm is developed for identifying vibration dynamic characteristics by means of hybrid genetic algorithm. From the measured dynamic responses of a hydro generator, an appropriate estimation algorithm is needed to identify the loading parameters, including the main frequencies and amplitudes of

Shouju Li; Yingxi Liu

2006-01-01

414

Optimal reconfiguration of radial distribution systems using a fuzzy mutated genetic algorithm  

Microsoft Academic Search

A new method based on a fuzzy mutated genetic algorithm 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

K. Prasad; R. Ranjan; N. C. Sahoo; A. Chaturvedi

2005-01-01

415

A multi-objective genetic local search algorithm and its application to flowshop scheduling  

Microsoft Academic Search

We propose a hybrid algorithm for finding a set of nondominated solutions of a multi objective optimization problem. In the proposed algorithm, a local search procedure is applied to each solution (i.e., each individual) generated by genetic operations. Our algorithm uses a weighted sum of multiple objectives as a fitness function. The fitness function is utilized when a pair of

Hisao Ishibuchi; Tadahiko Murata

1998-01-01

416

Collision-Free Cartesian Trajectory Generation Using Raster Scanning and Genetic Algorithms  

Microsoft Academic Search

An algorithm for Cartesian trajectory generation by redundant robots in environments with obstacles is presented. The algorithm combines a raster scanning technique, genetic algorithms and functions for interpolation in the joint coordinates space in order to approximate a desired Cartesian curve by the robot's hand tip under maximum allowed position deviation. A raster scanning technique determines a minimal set of

Andreas C. Nearchou; Nikos A. Aspragathos

1998-01-01

417

Engineering Case Studies Using Parameterless Penalty Non-dominated Ranked Genetic Algorithm  

Microsoft Academic Search

The new elitist multi-objective genetic algorithm PPNRGA have been used for solving engineering design problems with multiple objectives. Although there exists a number of classical techniques, evolutionary algorithms (EAs) have an edge over the classical methods where they can find multiple Pareto optimal solutions in one single simulation run. The new proposed algorithm is a parameterless penalty non-dominated ranking GA

Omar Al Jadaan; Ahmad Jabas; Wael Abdula; Lakshmi Rajamani; Essa Zaiton; C. R. Rao

2009-01-01

418

Enhancing the efficiency of genetic algorithm by identifying linkage groups using DSM clustering  

Microsoft Academic Search

Standard genetic algorithms are not very suited to problems with multivariate interactions among variables. This problem has been identified from the beginning of these algorithms and has been termed as the linkage learning problem. Numerous attempts have been carried out to solve this problem with various degree of success. In this paper, we employ an effective algorithm to cluster a

Amin Nikanjam; Hadi Sharifi; B. Hoda Helmi; Adel Rahmani

2010-01-01

419

Data-intensive computing for competent genetic algorithms: a pilot study using meandre  

Microsoft Academic Search

Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases (selectorecombinative genetic algorithms and estimation of distribution algorithms) are presented, analyzed, and discussed. This study shows that equivalent

Xavier Llorà

2009-01-01

420

Tracking multiple point targets using genetic interacting multiple model based algorithm  

Microsoft Academic Search

Multiple point target tracking in the presence of dense clutter re- quires tracking maneuvering and non-maneuvering targets simul- taneously in the absence of any apriori information about target dynamics. We propose a tracking algorithm based on interacting multiple model (IMM) which exploits the genetic algorithm for data association. In the proposed algorithm no observation is as- signed to any trajectory,

Mukesh A. Zaveri; Shabbir N. Merchant; Uday B. Desai

2004-01-01

421

Finding a better-than-classical quantum AND\\/OR algorithm using genetic programming  

Microsoft Academic Search

This paper documents the discovery of a new, better-than-classical quantum algorithm for the depth-two AND\\/OR tree problem. We describe the genetic programming system that was constructed specifically for this work, the quantum computer simulator that is used to evaluate the fitness of evolving quantum algorithms, and the newly discovered algorithm

Lee Spector; H. Barnum; H. J. Bernstein; Nikhil Swamy

1999-01-01

422

Preserving and Exploiting Genetic Diversity in Evolutionary Programming Algorithms  

Microsoft Academic Search

Evolution programming (EP) is an important category of evolutionary algorithms. It relies primarily on mutation operators to search for solutions of function optimization problems (FOPs). Recently a series of new mutation operators have been proposed in order to improve the performance of EP. One prominent example is the fast EP (FEP) algorithm which employs a mutation operator based on the

Gang Chen; Chor Ping Low; Zhonghua Yang

2009-01-01

423

Radial basis function network configuration using genetic algorithms  

Microsoft Academic Search

Most training algorithms for radial basis function (RBF) neural networks start with a predetermined network structure which is chosen either by using a priori knowledge or based on previous experience. The resulting network is often insufficient or unnecessarily complicated and an appropriate network structure can only be obtained by trial and error. Training algorithms which incorporate structure selection mechanisms are

Stephen A. Billings; Guang L. Zheng

1995-01-01

424

An enhanced nonparametric streamflow disaggregation model with genetic algorithm  

NASA Astrophysics Data System (ADS)

Stochastic streamflow generation is generally utilized for planning and management of water resources systems. For this purpose, a number of parametric and nonparametric models have been suggested in literature. Among them, temporal and spatial disaggregation approaches play an important role particularly to make sure that historical variance-covariance properties are preserved at various temporal and spatial scales. In this paper, we review the underlying features of existing nonparametric disaggregation methods, identify some of their pros and cons, and propose a disaggregation algorithm that is capable of surmounting some of the shortcomings of the current models. The proposed models hinge on k-nearest neighbor resampling, the accurate adjusting procedure, and a genetic algorithm. The models have been tested and compared to an existing nonparametric disaggregation approach using data of the Colorado River system. It has been shown that the model is capable of (1) reproducing the season-to-season correlations including the correlation between the last season of the previous year and the first season of the current year, (2) minimizing or avoiding the generation of flow patterns across the year that are literally the same as those of the historical records, and (3) minimizing or avoiding the generation of negative flows. In addition, it is applicable to intermittent river regimes.

Lee, T.; Salas, J. D.; Prairie, J.

2010-08-01

425

Human emotion detector based on genetic algorithm using lip features  

NASA Astrophysics Data System (ADS)

We predicted human emotion using a Genetic Algorithm (GA) based lip feature extractor from facial images to classify all seven universal emotions of fear, happiness, dislike, surprise, anger, sadness and neutrality. First, we isolated the mouth from the input images using special methods, such as Region of Interest (ROI) acquisition, grayscaling, histogram equalization, filtering, and edge detection. Next, the GA determined the optimal or near optimal ellipse parameters that circumvent and separate the mouth into upper and lower lips. The two ellipses then went through fitness calculation and were followed by training using a database of Japanese women's faces expressing all seven emotions. Finally, our proposed algorithm was tested using a published database consisting of emotions from several persons. The final results were then presented in confusion matrices. Our results showed an accuracy that varies from 20% to 60% for each of the seven emotions. The errors were mainly due to inaccuracies in the classification, and also due to the different expressions in the given emotion database. Detailed analysis of these errors pointed to the limitation of detecting emotion based on the lip features alone. Similar work [1] has been done in the literature for emotion detection in only one person, we have successfully extended our GA based solution to include several subjects.

Brown, Terrence; Fetanat, Gholamreza; Homaifar, Abdollah; Tsou, Brian; Mendoza-Schrock, Olga

2010-04-01

426

Image Segmentation by a Genetic Fuzzy c-Means Algorithm Using Color and Spatial Information  

Microsoft Academic Search

\\u000a This paper describes a new clustering algorithm for color image segmentation. We combine the classical fuzzy c-means algorithm\\u000a (FCM) with a genetic algorithm (GA), and we modify the objective function of the FCM for taking into account the spatial information\\u000a of image data and the intensity inhomogeneities. An application to medical images is presented. Experiments show that the\\u000a proposed algorithm

Lucia Ballerini; Leonardo Bocchi; Carina B. Johansson

2004-01-01

427

Multiobjective optimization of steam reformer performance using genetic algorithm  

SciTech Connect

An existing side-fired stream reformer is simulated using a rigorous model with proven reaction kinetics, incorporating aspects of heat transfer in the furnace and diffusion in the catalyst pellet. Thereafter, optimal conditions, which could lead to an improvement in its performance, are obtained. An adaptation of the nondominated sorting genetic algorithm is employed to perform a multiobjective optimization. For a fixed production rate of hydrogen from the unit, the simultaneous minimization of the methane feed rate and the maximization of the flow rate of carbon monoxide in the syngas are chosen as the two objective functions, keeping in mind the processing requirements, heat integration, and economics. For the design configuration considered in this study, sets of Pareto-optimal operating conditions are obtained. The results are expected to enable the engineer to gain useful insights into the process and guide him/her in operating the reformer to minimize processing costs and to maximize profits.

Rajesh, J.K.; Gupta, S.K.; Rangaiah, G.P.; Ray, A.K.

2000-03-01

428

Automatic radiometric normalization with genetic algorithms and a Kriging model  

NASA Astrophysics Data System (ADS)

An automatic procedure of radiometric normalization is proposed for multi-temporal satellite image correction, with a modified genetic algorithm (GA) regression method and a spatially variant normalization model using the Kriging interpolation.The proposed procedure was tested on a synthetic altered image and an image pair from FORMOSAT-2; the results show that the GA method is more robust than the conventional PCA methods in high-resolution imaging, and that different regression-error evaluation models have different sensitivities to the linear regression parameters. A statistical comparison demonstrates that 1-km sampling spacing is able to successfully achieve the parameter spatial variation. Error validation on FORMOSAT-2 image pair shows it is a decent combination of radiometric normalization with GA estimation and a spatially variant parameter normalization model.

Liu, Shou-Heng; Lin, Ching-Weei; Chen, Yie-Ruey; Tseng, Chih-Ming

2012-06-01

429

Genetic-algorithm-directed polarimetric sensing for optimum pattern classification  

NASA Astrophysics Data System (ADS)

In this paper an automated technique for adaptive radar polarimetric pattern classification is described. The approach is based on a genetic algorithm that uses probabilistic patterns separation distance function and searches for those transmit and receive states of polarization sensing angles that optimize this function. Seven pattern separation distance functions, the Rayleigh quotient, Bhattacharyya, Divergence, Kolmogorov, Matusta, Kullback-Leibler distances, and the Bayesian Probability of Error, are used on real, fully polarimetric synthetic aperture radar target signatures. Each of these signatures is represented as functions of transmit and receive polarization ellipticity angle and the angle of polarization ellipse. The results indicate that based on the majority of the distance functions used; there is a unique set of state of polarization angles whose use will lead to improved classification performance.

Sadjadi, Firooz A.

2004-10-01

430

Alien Genetic Algorithm for Exploration of Search Space  

NASA Astrophysics Data System (ADS)

Genetic Algorithm (GA) is a widely accepted population based stochastic optimization technique used for single and multi objective optimization problems. Various versions of modifications in GA have been proposed in last three decades mainly addressing two issues, namely increasing convergence rate and increasing probability of global minima. While both these. While addressing the first issue, GA tends to converge to a local optima and addressing the second issue corresponds the large computational efforts. Thus, to reduce the contradictory effects of these two aspects, we propose a modification in GA by adding an alien member in the population at every generation. Addition of an Alien member in the current population at every generation increases the probability of obtaining global minima at the same time maintaining higher convergence rate. With two test cases, we have demonstrated the efficacy of the proposed GA by comparing with the conventional GA.

Patel, Narendra; Padhiyar, Nitin

2010-10-01

431

Selecting Random Distributed Elements for HIFU using Genetic Algorithm  

NASA Astrophysics Data System (ADS)

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. Genetic algorithm 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 genetic algorithm is effective in selecting those randomly distributed elements in a HIFU array.

Zhou, Yufeng

2011-09-01

432

A Hybrid Feature Selection Algorithm: Combination of Symmetrical Uncertainty and Genetic Algorithms  

Microsoft Academic Search

A hybrid feature selection method called SU-GA-W is proposed to make full use of advantages of filter and wrapper methods. This method falls into two phases. The filter phase removes features with lower SU and guides the initialization of GA population; the wrapper phase searches the final feature subset. The effectiveness of this algorithm is demonstrated on various data sets.

Bai-Ning Jiang; Xiang-Qian Ding; Lin-Tao Ma; Ying He; Tao Wang; Wei-Wei Xie

2008-01-01

433

A Genetic Algorithm to Solve the Static Transmission System Expansion Planning  

Microsoft Academic Search

\\u000a This paper proposes a genetic algorithm (GA) to solve the transmission system expansion planning (TSEP) problem in power systems.\\u000a The transmission network is represented using the DC power flow model. The problem is then formulated as a mixed integer nonlinear\\u000a problem (MINLP) which is very complex to solve in large-scale networks using classical optimization algorithms. Genetic algorithms\\u000a (GAs) are a

José Antonio Sánchez Rodríguez; José Coto; Javier Gómez-Aleixandre

434

A Simple Real-Coded Compact Genetic Algorithm and its Application to Antenna Optimization  

Microsoft Academic Search

Based on Harik's compact genetic algorithm, in this paper a real-coded type of compact genetic algorithm, RCGA, based on probability distribution function of each gene is developed. The algorithm is applied to design a new small-size tapered monopole ultra-wideband antenna as a practical utilization. The antenna is a modified PTMA which is able to improve the bandwidth using eleven degrees

Soheil Radiom; H. Aliakbarian; G. Vandenbosch; G. Gielen

2007-01-01

435

A New Mutation Operator for the Elitism-Based Compact Genetic Algorithm  

Microsoft Academic Search

A Compact Genetic Algorithm (CGA) is a genetic algorithm specially devised to meet the tight restrictions of hardware-based\\u000a implementations. We propose a new mutation operator for an elitism-based CGA. The performance of this algorithm, named emCGA,\\u000a was tested using a set of algebraic functions for optimization. The optimal mutation rate found for high-dimensionality functions\\u000a is around 0.5%, and the low

Rafael R. Silva; Heitor Silvério Lopes; Carlos R. Erig Lima

2007-01-01

436

Two coding based adaptive parallel co-genetic algorithm with double agents structure  

Microsoft Academic Search

This paper systematically proposed a multi-population agent co-genetic algorithm with double chain-like agent structure (MPATCGA) to solve the problem of the low optimization precision and long optimization time of simple genetic algorithm in terms of two coding strategy. This algorithm adopted multi-population parallel searching mode, close chain-like agent structure, cycle chain-like agent structure, dynamic neighborhood competition, and improved crossover strategy

Yongming Li; Xiaoping Zeng; Liang Han; Pin Wang

2010-01-01

437

Evaluating Clustering Algorithms for Genetic Regulatory Network Structural Inference  

NASA Astrophysics Data System (ADS)

Modern biological research increasingly recognises the importance of genome-wide gene regulatory network inference; however, a range of statistical, technological and biological factors make it a difficult and intractable problem. One approach that some research has used is to cluster the data and then infer a structural model of the clusters. When using this kind of approach it is very important to choose the clustering algorithm carefully. In this paper we explicitly analyse the attributes that make a clustering algorithm appropriate, and we also consider how to measure the quality of the identified clusters. Our analysis leads us to develop three novel cluster quality measures that are based on regulatory overlap. Using these measures we evaluate two modern candidate algorithms: FLAME, and KMART. Although FLAME was specifically developed for clustering gene expression profile data, we find that KMART is probably a better algorithm to use if the goal is to infer a structural model of the clusters.

Fogelberg, Christopher; Palade, Vasile

438

Algorithm Engineering  

Microsoft Academic Search

Algorithm Engineering is concerned with the design, analysis, implementation, tun- ing, debugging and experimental evaluation of computer programs for solving algorithmic problems. It provides methodologies and tools for developing and engineering efficient al- gorithmic codes and aims at integrating and reinforcing traditional theoretical approaches for the design and analysis of algorithms and data structures.

Camil Demetrescu; Irene FinocchiGiuseppe; F. Italianok

439

Genetic Algorithms Vs. Classical Search Techniques For Identification Of Fuzzy Models  

Microsoft Academic Search

: Genetic algorithms (GAs) have been established as a viable technique for search problemsacross diverse disciplines of science and engineering. In this paper, different genetic algorithms are reportedthat have been implemented and applied to optimization problems in general system theory. The identificationof qualitative models in Fuzzy Inductive Reasoning (FIR) is a complex search problem of behavior analysis. Astudy of GAs\\

Antoni Jerez; Angela Nebot

440

Application of genetic algorithms to constrain shallow elastic parameters using in situ ground inclination measurements  

Microsoft Academic Search

Among the class of global optimization techniques, which includes Monte Carlo and simulated annealing methods, the Genetic Algorithms constitute a new class of methods to solve highly non-linear optimization problems. The issue has generated considerable interest in the field of artificial intelligence, and recently, in some multi-parameter optimization geophysical problems. In this study, we explore the applicability of genetic algorithms

R. Gaulon

1997-01-01

441

Coarse-grained parallel genetic algorithm applied to a nuclear reactor core design optimization problem  

Microsoft Academic Search

This work extends the research related to genetic algorithms (GA) in core design optimization problems, which basic investigations were presented in previous work. Here we explore the use of the Island Genetic Algorithm (IGA), a coarse-grained parallel GA model, comparing its performance to that obtained by the application of a traditional non-parallel GA. The optimization problem consists on adjusting several

Cláudio M. N. A. Pereira; Celso M. F. Lapa

2003-01-01

442

The Science of Breeding and Its Application to the Breeder Genetic Algorithm (BGA)  

Microsoft Academic Search

The breeder genetic algorithm (BGA) models artificial selection as performed by human breeders. The science of breeding is based on advanced statistical methods. In this paper a connection between genetic algorithm theory and the science of breeding is made. We show how the response to selection equation and the concept of heritability can be applied to predict the behavior of

Heinz Mühlenbein; Dirk Schlierkamp-voosen

1993-01-01

443

Integral Formulation and Genetic Algorithms for Defects Geometry Reconstruction Using Pulse Eddy Currents  

Microsoft Academic Search

A method for reconstruction of zero-thickness defects, buried deep under material surface, using pulse eddy currents, is proposed. Both an integral-FEM method for simulation of transient eddy-currents and genetic algorithms, as a model-free inversion technique, are proposed. Numerical results for the inversion of the eddy-currents signals, using genetic algorithms, are shown.

Gabriel Preda; Mihai Rebican; Florea Ioan Hantila

2010-01-01

444

Multi-response simulation optimization using genetic algorithm within desirability function framework  

Microsoft Academic Search

This paper presents a new methodology to solve multi-response statistical optimization problems. This methodology integrates desirability function and simulation approach with a genetic algorithm. The desirability function is responsible for modeling the multi-response statistical problem, the simulation approach generates required input data from a simulated system, and finally the genetic algorithm tries to optimize the model. This methodology includes four

Seyed Hamid Reza Pasandideh; Seyed Taghi Akhavan Niaki

2006-01-01

445

A Genetic Algorithm Based on Multi-bee population evolutionary for numerical optimization  

Microsoft Academic Search

In this paper, genetic algorithm based on multi-bee population evolutionary (BMGA) is proposed. In BMGA, there are many bee populations. One is from generation by the BMGA, the others are random populations, and consequentially it enhances the exploration of genetic algorithm. Optimum individual being a queen-bee in each population crossover with each selected individual (drone). As a result it reinforces

Xueyan Lu; Yongquan Zhou

2008-01-01

446

Forecasting the SST space-time variability of the Alboran Sea with genetic algorithms  

Microsoft Academic Search

We propose a nonlinear ocean forecasting technique based on a combination of genetic algorithms and empirical orthogonal function (EOF) analysis. The method is used to forecast the space-time variability of the sea surface temperature (SST) in the Alboran Sea. The genetic algorithm finds the equations that best describe the behavior of the different temporal amplitude functions in the EOF decomposition

Alberto Álvarez; Cristóbal López; Margalida Riera; Emilio Hernández-García; Joaquín Tintoré

2000-01-01

447

Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms  

Microsoft Academic Search

This paper proposes a genetic-algorithm-based approach to the construction of fuzzy classification systems with rectangular fuzzy rules. In the proposed approach, compact fuzzy classification systems are automatically constructed from numerical data by selecting a small number of significant fuzzy rules using genetic algorithms. Since significant fuzzy rules are selected and unnecessary fuzzy rules are removed, the proposed approach can be

Hisao Ishibuchi; Ken Nozaki; Naohisa Yamamoto; Hideo Tanaka

1994-01-01

448

Rank Based Genetic Algorithm for solving the Banking ATM's Location Problem using convolution  

Microsoft Academic Search

In order t o satisfy the client needs, his Utility should be increased by covering his Demand. The service Utility should be maximized through effective deployment of ATMs. Genetic Algorithm is one of widely used techniques to solve complex op- timization problems, such as Banking ATM's Location Problem. This paper proposes a novel Rank Based Genetic Algorithm using convolution for

Alaa Alhaffa; Omar Al Jadaan; Wael Abdulal; Ahmad Jabas

2011-01-01

449

Rank-Based Genetic Algorithm with Limited Iteration for Grid Scheduling  

Microsoft Academic Search

In grid computing the number of resources and tasks is usually very large, which makes the scheduling task very complex optimization problem. Genetic algorithms (GAs) have been broadly used to solve these NP-complete problems efficiently. On the other hand, the standard genetic algorithm (SGA) is too slow when used in a realistic scheduling due to its time consuming iteration. This

Wael Abdulal; O. Al Jadaan; A. Jabas; S. Ramachandram; M. Kaiiali; C. R. Rao

2009-01-01

450

A WLAN planning proposal through computational intelligence and genetic algorithms hybrid approach  

Microsoft Academic Search

This paper proposes a WLAN planning strategy through the use of computational intelligence and genetic algorithm. A measurement technique was used to collect data from a real WLAN network. Metrics like power, distance, delay, jitter, packet loss, throughput and PMOS were analysed through the use of bayesian networks. Finally, to optimize the QoS parameters a genetic algorithm was applied to

Jasmine P. L. Araújo; Josiane C. Rodrigues; Simone G. C. Fraiha; Hermínio Gomes; Gervásio P. S. Cavalcante; Carlos Renato Lisboa Francês

2008-01-01

451

A Genetic Local Search Algorithm for Solving Symmetric and Asymmetric Traveling Salesman Problems  

Microsoft Academic Search

The combination of local search heuristics and genetic algorithms is a promising approach for finding nearoptimum solutions to the traveling salesman problem (TSP). In this paper, an approach is presented in which local search techniques are used to find local optima in a given TSP search space, and genetic algorithms are used to search the space of local optima in

Bernd Freisleben; Peter Merz

1996-01-01

452

Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms  

Microsoft Academic Search

This paper examines the applicability of genetic algorithms (GA's) in the simultaneous design of membership functions and rule sets for fuzzy logic controllers. Previous work using genetic algorithms has focused on the development of rule sets or high performance membership functions; however, the interdependence between these two components suggests a simultaneous design procedure would be a more appropriate methodology. When

Abdollah Homaifar; Ed McCormick

1995-01-01

453

The application of combinatorial optimization by Genetic Algorithm and Neural Network  

Microsoft Academic Search

A optimization model of sizing the storage section in a renewable power generation system was set up, and two methods were used to solve the model: genetic algorithm or combinatorial optimization by genetic algorithm and neural network. The system includes the photovoltaic arrays, the lead-acid battery and a flywheel. The optimal sizing can be considered as a constrained optimization problem:

Shiqiong Zhou; Longyun Kang; Guifang Guo; Yanning Zhang; Jianbo Cao; Binggang Cao

2008-01-01

454

The compact Genetic Algorithm for likelihood estimator of first order moving average model  

Microsoft Academic Search

Recently Genetic Algorithms (GAs) have frequently been used for optimizing the solution of estimation problems. One of the main advantages of using these techniques is that they require no knowledge or gradient information about the response surface. The poor behavior of genetic algorithms in some problems, sometimes attributed to design operators, has led to the development of other types of

Rawaa Dawoud Al-Dabbagh; Mohd. Sapiyan Baba; Saad Mekhilef; Azeddien Kinsheel

2012-01-01

455

Music Composition Using Combination of Genetic Algorithms and Recurrent Neural Networks  

Microsoft Academic Search

Creativity has a fundamental role in music composition. One of the theories, which exist about creativity, is combination-theory. In this paper the suitability of genetic algorithms and recurrent neural networks for modeling this theory is considered. We discuss that two phases of combination occurs: one at the genetic algorithm level, and the other at the network level. One important challenge

Peyman Sheikholharam; Mohammad Teshnehlab

2008-01-01

456

A genetic local search algorithm for random binary constraint satisfaction problems  

Microsoft Academic Search

This paper introduces a genetic local search algorithm for bi- nary constraint satisfaction problems. The core of the algo- rithm consists of an ad-hoc optimization procedure followed by the application of blind genetic operators. A standard set of benchmark instances is used in order to assess the performance of the algorithm. The results indicate that this apparently naive hybridation of

Elena Marchiori; Adri G. Steenbeek

2000-01-01

457

AERODYNAMIC AND AEROACOUSTIC OPTIMIZATION OF AIRFOI LS VIA A PARALLEL GENETIC ALGORITHM  

Microsoft Academic Search

A parallel genetic algorithm (GA) was used to generate, in a single run, a family of aerodynamically efficient, low-noise rotor blade designs representing th e Pareto optimal set. The n-branch tournament, uniform crossover genetic algorithm operates on twenty design variables, which constitute the control points for a spline representing the airfoil surface. The GA takes advantage of available computer resources

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

1998-01-01

458

GRCA: a hybrid genetic algorithm for circuit ratio-cut partitioning  

Microsoft Academic Search

A genetic algorithm for partitioning a hypergraph into two disjoint graphs of minimum ratio cut is presented. As the Fiduccia-Mattheyses graph partitioning heuristic turns out to be not effective when used in the context of a hybrid genetic algorithm, we propose a modification of the Fiduccia-Mattheyses heuristic for more effective and faster space search by introducing a number of novel

Thang Nguyen Bui; Byung-Ro Moon

1998-01-01

459

Joint parameters identification for redundant manipulators based on fuzzy theory and genetic algorithm  

Microsoft Academic Search

The joint parameters of redundant manipulators are prerequisite data for effective dynamics control. An identification method via fuzzy theory and genetic algorithm has been presented to study modular redundant robots. The genetic algorithm is used in the fuzzy optimization expecting to obtain global optimal solutions. Experimental modal analysis and finite element method have been exploited in dynamics modeling. The joint

Yangmin Li; Xiaoping Liu; Zhaoyang Peng; Yugang Liu

2002-01-01

460

User-Based Document Clustering by Redescribing Subject Descriptions with a Genetic Algorithm.  

ERIC Educational Resources Information Center

|Discussion of clustering of documents and queries in information retrieval systems focuses on the use of a genetic algorithm to adapt subject descriptions so that documents become more effective in matching relevant queries. Various types of clustering are explained, and simulation experiments used to test the genetic algorithm are described. (27…

Gordon, Michael D.

1991-01-01

461

Order-Based Fitness Functions for Genetic Algorithms Applied to Relevance Feedback.  

ERIC Educational Resources Information Center

|Discusses genetic algorithms in information retrieval, especially for relevance feedback, and evaluates the efficacy of a genetic algorithm with various order-based fitness functions for relevance feedback in a test database. Compares results with the Ide dec-hi method, one of the best traditional methods. (Contains 56 references.) (Author/LRW)|

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

2003-01-01

462

An improved genetic algorithm for facility layout problems having inner structure walls and passages  

Microsoft Academic Search

This study proposes an improved genetic algorithm (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

Kyu-yeul Lee; Seong-nam Han; Myung-il Roh

2003-01-01

463

Balancing Manufacturability and Optimal Structural Performance for Laminate Composites through a Genetic Algorithm  

Microsoft Academic Search

This paper details the application of a specialised genetic algorithm to reduce the mass of a laminated composite wing rib. The genetic algorithm has been customised specifically to optimise the performance of polymer-laminated composites. The technology allows the mass to be minimized by the removal or addition of plies of various discrete orientations whilst satisfying the structural intent of the

Mike Stephens; Vassili Toropov

2004-01-01

464

A genetic algorithm for shortest path routing problem and the sizing of populations  

Microsoft Academic Search

This paper presents a genetic algorithmic 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

Chang Wook Ahn; Rudrapatna S. Ramakrishna

2002-01-01

465

Use of Engineering Fuzzy Sets, BP Neural Network and Genetic Algorithm for Intelligent Decision-Making  

Microsoft Academic Search

On the ground of fuzzy optimum selection of BP neural network model, the paper introduces genetic algorithm to the model and presents an intelligent decision-making model based on fuzzy optimum selection of BP neural network model and mixed with genetic algorithm. A case proved that the intelligent decision-making model is efficient and robust in determining network topologic structure, accelerating convergence

Shouyu Chen; Yu Guo; Dagang Wang

2006-01-01

466

Design optimization of a two-dimensional hydrofoil by applying a genetic algorithm  

Microsoft Academic Search

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

H. Ouyang; L. J. Weber; A. J. Odgaard

2006-01-01

467

Genetic algorithms in computational materials science and engineering: simulation and design of self-assembling materials  

Microsoft Academic Search

We introduce here two genetic algorithms that were developed in order to aid in the design of molecules for self-assembling materials. The first constructs molecules from sets of chemical building blocks, searching for candidates that are determined by an ancillary modeling program to assemble into low-energy aggregates. The results of running this Genetic Algorithm (GA) on a set of building

Milan Keser; Samuel I Stupp

2000-01-01

468

A Parallel Genetic Algorithm to Discover Patterns in Genetic Markers that Indicate Predisposition to Multifactorial Disease  

PubMed Central

This paper describes a novel algorithm to analyze genetic linkage data using pattern recognition techniques and genetic algorithms (GA). The method allows a search for regions of the chromosome that may contain genetic variations that jointly predispose individuals for a particular disease. The method uses correlation analysis, filtering theory and genetic algorithms (GA) to achieve this goal. Because current genome scans use from hundreds to hundreds of thousands of markers, two versions of the method have been implemented. The first is an exhaustive analysis version that can be used to visualize, explore, and analyze small genetic data sets for two marker correlations; the second is a GA version, which uses a parallel implementation allowing searches of higher-order correlations in large data sets. Results on simulated data sets indicate that the method can be informative in the identification of major disease loci and gene-gene interactions in genome-wide linkage data and that further exploration of these techniques is justified. The results presented for both variants of the method show that it can help genetic epidemiologists to identify promising combinations of genetic factors that might predispose to complex disorders. In particular, the correlation analysis of IBD expression patterns might hint to possible gene-gene interactions and the filtering might be a fruitful approach to distinguish true correlation signals from noise.

Rausch, Tobias; Thomas, Alun; Camp, Nicola J.; Cannon-Albright, Lisa A.; Facelli, Julio C.

2008-01-01

469

Optimal design of viscous damper connectors for adjacent structures using genetic algorithm and Nelder-Mead algorithm  

NASA Astrophysics Data System (ADS)

Passive dampers can be used to connect two adjacent structures in order to mitigate earthquakes induced pounding damages. Theoretical and experimental studies have confirmed efficiency and applicability of various connecting devices, such as viscous damper, MR damper, etc. However, few papers employed optimization methods to find the optimal mechanical properties of the dampers, and in most papers, dampers are assumed to be uniform. In this study, we optimized the optimal damping coefficients of viscous dampers considering a general case of non-uniform damping coefficients. Since the derivatives of objective function to damping coefficients are not known, to optimize damping coefficients, a heuristic search method, i.e. the genetic algorithm, is employed. Each structure is modeled as a multi degree of freedom dynamic system consisting of lumped-masses, linear springs and dampers. In order to examine dynamic behavior of the structures, simulations in frequency domain are carried out. A pseudo-excitation based on Kanai-Tajimi spectrum is used as ground acceleration. The optimization results show that relaxing the uniform dampers coefficient assumption generates significant improvement in coupling effectiveness. To investigate efficiency of genetic algorithm, solution quality and solution time of genetic algorithm are compared with those of Nelder-Mead algorithm.

Bigdeli, Kasra; Hare, Warren; Tesfamariam, Solomon

2012-03-01

470

Vibration reduction optimum design of a steam-turbine rotor-bearing system using a hybrid genetic algorithm  

Microsoft Academic Search

This paper describes the vibration optimum design for the low-pressure steam-turbine rotor of a 1007-MW nuclear power plant by using a hybrid genetic algorithm (HGA) that combines a genetic algorithm and a local concentration search algorithm using a modified simplex method. This algorithm not only calculates the optimum solution faster and more accurately than the standard genetic algorithm but can

B. S. Yang; S. P. Choi; Y. C. Kim

2005-01-01

471

Molecular binding in structure-based drug design: a case study of the population-based annealing genetic algorithms  

Microsoft Academic Search

The molecular binding problem, one of the most important problems in structure based drug design, can be formulated as a global energy optimization problem by using molecular mechanics. A novel computational algorithm is proposed to address the molecular binding problem. The algorithm is derived from genetic algorithms (GA) plus simulated annealing (SA) hybrid techniques, namely population based annealing genetic algorithms

Chien-Cheng Chen; Leuo-Hong Wang; Cheng-Yan Kao; Ming Ouhyoung; Wen-Chin Chen

1998-01-01

472

Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system  

PubMed Central

We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, 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.

2010-01-01

473

Optimization of Electric Power Leveling Systems by a Novel Hybrid Algorithm with Simulated Evolution and Genetic Algorithms  

NASA Astrophysics Data System (ADS)

Electric power demand has an increasing tendency year by year. The fluctuation of the electric power causes further increase in the cost of the electric power facility and electricity charges. The development of the electric power-leveling systems (EPLS) using energy storage technology is desired to improve the electric power quality. The EPLS with a SMES is proposed as one of the countermeasures for the electric power quality improvement. However, the SMES is very expensive and it is difficult to decide the gains of the controller. It is essential in the practical use that the reduction of SMES capacity is realized. This paper proposes a new optimization method of the EPLS. The proposed algorithm is hybrid architecture with a combination of SimE (Simulated Evolution) and GA (Genetic Algorithms). The optimization of the EPLS can be achieved by the proposed hybrid algorithm compared to the SimE and the GA.

Itoh, Jyunpei; Yamamoto, Masayoshi; Funabiki, Shigeyuki

474

Efficient Improvement of Silage Additives by Using Genetic Algorithms  

PubMed Central

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 genetic algorithms 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 genetic algorithm 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.

2000-01-01

475

Quantifying and Interpreting the Effect of Intelligent Information Exchange between Chromosomes in a Human Simulation of a Genetic Algorithm.  

National Technical Information Service (NTIS)

A genetic algorithm is simulated using human beings as 'chromosomes' in a preliminary study intended to quantify and interpret the effect of intelligent information exchange on genetic algorithm performance. Two factors are varied: the amount of informati...

T. P. Riopka M. Diab P. Book

2000-01-01

476

Tailoring harmonic radiation to different applications using a genetic algorithm  

NASA Astrophysics Data System (ADS)

We use a genetic algorithm to theoretically optimize several properties of extreme ultraviolet (XUV) radiation, generated as high-order harmonics in xenon and argon. We maximize the harmonic pulse energy, minimize the pulse duration or optimize the temporal coherence by varying at the same time two or three parameters that are easily accessible in experiments, related to the characteristics of the laser beam and the nonlinear medium. For the 15th and 29th harmonics in argon, we find up to 109 photons per pulse, and pulse durations as short as 6 fs generated by a 50 fs laser pulse. We can also tailor the phase matching conditions to spectrally select the transform-limited part of the harmonic radiation. This allows us to identify conditions when the time structure of the XUV radiation presents a train of attosecond pulses. We find that the optimum conditions for the different properties are in general not the same. They depend in particular on whether the harmonic belongs to the plateau or the cutoff region of the harmonic spectrum. This reflects the unavoidable interplay between the microscopic intensity-dependent harmonic phase and the macroscopic phase matching conditions imposed by a nonlinear medium interacting with an intense, focused laser beam.

Roos, L.; Gaarde, M. B.; L'Huillier, A.

2001-12-01

477

Experimental optimization of protein refolding with a genetic algorithm  

PubMed Central

Refolding of proteins from solubilized inclusion bodies still represents a major challenge for many recombinantly expressed proteins and often constitutes a major bottleneck. As in vitro refolding is a complex reaction with a variety of critical parameters, suitable refolding conditions are typically derived empirically in extensive screening experiments. Here, we introduce a new strategy that combines screening and optimization of refolding yields with a genetic algorithm (GA). The experimental setup was designed to achieve a robust and universal method that should allow optimizing the folding of a variety of proteins with the same routine procedure guided by the GA. In the screen, we incorporated a large number of common refolding additives and conditions. Using this design, the refolding of four structurally and functionally different model proteins was optimized experimentally, achieving 74–100% refolding yield for all of them. Interestingly, our results show that this new strategy provides optimum conditions not only for refolding but also for the activity of the native enzyme. It is designed to be generally applicable and seems to be eligible for all enzymes.

Anselment, Bernd; Baerend, Danae; Mey, Elisabeth; Buchner, Johannes; Weuster-Botz, Dirk; Haslbeck, Martin

2010-01-01

478

Genetic algorithms for rheological parameter estimation of magnetorheological fluids  

NASA Astrophysics Data System (ADS)

The primary objective of this study is to estimate the parameters of constitutive models characterizing the rheological properties of ferrous and cobalt nanoparticle-based magnetorheological fluids. Constant shear rate rheometer measurements were carried out using suspensions of nanometer sized particles in hydraulic oil. These measurements yielded shear stress vs. shear rate as a function of applied magnetic field. The MR fluid was characterized using both Bingham-Plastic and Herschel-Bulkley constitutive models. Both these models have two regimes: a rigid pre-yield behavior for shear stress less than a field-dependant yield stress, and viscous behavior for higher shear rates. While the Bingham-Plastic model assumes linear post-yield behavior, the Herschel-Bulkley model uses a power law dependent on the dynamic yield shear stress, a consistency parameter and a flow behavior index. Determination of the model parameters is a complex problem due to the non-linearity of the model and the large amount of scatter in the experimentally observed data. Usual gradient-based numerical methods are not sufficient to determine the characteristic values. In order to estimate the rheological parameters, we have used a genetic algorithm and carried out global optimization. The obtained results provide a good fit to the experimental data.

Chaudhuri, Anirban; Wereley, Norman M.; Radhakrishnan, R.

2005-05-01

479

Improvement of unsupervised texture classification based on genetic algorithms  

NASA Astrophysics Data System (ADS)

At the previous conference, the authors are proposed a new unsupervised texture classification method based on the genetic algorithms (GA). In the method, the GA are employed to determine location and size of the typical textures in the target image. The proposed method consists of the following procedures: 1) the determination of the number of classification category; 2) each chromosome used in the GA consists of coordinates of center pixel of each training area candidate and those size; 3) 50 chromosomes are generated using random number; 4) fitness of each chromosome is calculated; the fitness is the product of the Classification Reliability in the Mixed Texture Cases (CRMTC) and the Stability of NZMV against Scanning Field of View Size (SNSFS); 5) in the selection operation in the GA, the elite preservation strategy is employed; 6) in the crossover operation, multi point crossover is employed and two parent chromosomes are selected by the roulette strategy; 7) in mutation operation, the locuses where the bit inverting occurs are decided by a mutation rate; 8) go to the procedure 4. However, this method has not been automated because it requires not only target image but also the number of categories for classification. In this paper, we describe some improvement for implementation of automated texture classification. Some experiments are conducted to evaluate classification capability of the proposed method by using images from Brodatz's photo album and actual airborne multispectral scanner. The experimental results show that the proposed method can select appropriate texture samples and can provide reasonable classification results.

Okumura, Hiroshi; Togami, Yuuki; Arai, Kohei

2004-11-01

480

Toward Developing Genetic Algorithms to Aid in Critical Infrastructure Modeling  

SciTech Connect

Today’s society relies upon an array of complex national and international infrastructure networks such as transportation, telecommunication, financial and energy. Understanding these interdependencies is necessary in order to protect our critical infrastructure. The Critical Infrastructure Modeling System, CIMS©, examines the interrelationships between infrastructure networks. CIMS© development is sponsored by the National Security Division at the Idaho National Laboratory (INL) in its ongoing mission for providing critical infrastructure protection and preparedness. A genetic algorithm (GA) is an optimization technique based on Darwin’s theory of evolution. A GA can be coupled with CIMS© to search for optimum ways to protect infrastructure assets. This includes identifying optimum assets to enforce or protect, testing the addition of or change to infrastructure before implementation, or finding the optimum response to an emergency for response planning. This paper describes the addition of a GA to infrastructure modeling for infrastructure planning. It first introduces the CIMS© infrastructure modeling software used as the modeling engine to support the GA. Next, the GA techniques and parameters are defined. Then a test scenario illustrates the integration with CIMS© and the preliminary results.

Not Available

2007-05-01

481

A Moving Target Environment for Computer Configurations Using Genetic Algorithms  

SciTech Connect

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 Genetic Algorithm (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.

Crouse, Michael; Fulp, Errin W.

2011-10-31

482

Improved satellite image compression and reconstruction via genetic algorithms  

NASA Astrophysics Data System (ADS)

A wide variety of signal and image processing applications, including the US Federal Bureau of Investigation's fingerprint compression standard [3] and the JPEG-2000 image compression standard [26], utilize wavelets. This paper describes new research that demonstrates how a genetic algorithm (GA) may be used to evolve transforms that outperform wavelets for satellite image compression and reconstruction under conditions subject to quantization error. The new approach builds upon prior work by simultaneously evolving real-valued coefficients representing matched forward and inverse transform pairs at each of three levels of a multi-resolution analysis (MRA) transform. The training data for this investigation consists of actual satellite photographs of strategic urban areas. Test results show that a dramatic reduction in the error present in reconstructed satellite images may be achieved without sacrificing the compression capabilities of the forward transform. The transforms evolved during this research outperform previous start-of-the-art solutions, which optimized coefficients for the reconstruction transform only. These transforms also outperform wavelets, reducing error by more than 0.76 dB at a quantization level of 64. In addition, transforms trained using representative satellite images do not perform quite as well when subsequently tested against images from other classes (such as fingerprints or portraits). This result suggests that the GA developed for this research is automatically learning to exploit specific attributes common to the class of images represented in the training population.

Babb, Brendan; Moore, Frank; Peterson, Michael; Lamont, Gary

2008-09-01

483

Horizontal axis wind turbine systems: optimization using genetic algorithms  

NASA Astrophysics Data System (ADS)

A method for the optimization of a grid-connected wind turbine system is presented. The behaviour of the system components is coupled in a non-linear way, and optimization must take into account technical and economical aspects of the complete system design. The annual electrical energy cost is estimated using a cost model for the wind turbine rotor, nacelle and tower and an energy output model based on the performance envelopes of the power coefficient of the rotor, CP, on the Weibull parameters k and c and on the power law coefficient of the wind profile. In this study the site is defined with these three parameters and the extreme wind speed Vmax. The model parameters vary within a range of possible values. Other elements of the project (foundation, grid connection, financing cost, etc.) are taken into account through coefficients. The optimal values of the parameters are determined using genetic algorithms, which appear to be efficient for such a problem. These optimal values were found to be very different for a Mediterranean site and a northern European site using our numerical model. Optimal wind turbines at the Mediterranean sites considered in this article have an excellent profitability compared with reference northern European wind turbines. Most of the existing wind turbines appear to be well designed for northern European sites but not for Mediterranean sites.

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

2001-10-01

484

Aerodynamics Design and Genetic Algorithms for Optimization of Airship Bodies  

NASA Astrophysics Data System (ADS)

A special and effective aerodynamics calculation method has been applied for the flow field around a body of revolution to find the drag coefficient for a wide range of Reynolds numbers. The body profile is described by a first order continuous axial singularity distribution. The solution of the direct problem then gives the radius and inviscid velocity distribution. Viscous effects are considered by means of an integral boundary layer procedure, and for determination of the transition location the forced transition criterion is applied. By avoiding those profiles, which result in the separation of the boundary layer, the drag can be calculated at the end of the body by using Young's formula. In this study, a powerful optimization procedure known as a Genetic Algorithms (GA) is used for the first time in the shape optimization of airship hulls. GA represents a particular artificial intelligence technique for large spaces, striking a remarkable balance between exploration and exploitation of search space. This method could reach to minimum objective function through a better path, and also could minimize the drag coefficient faster for different Reynolds number regimes. It was found that GA is a powerful method for such multi-dimensional, multi-modal and nonlinear objective function.

Nejati, Vahid; Matsuuchi, Kazuo

485

The adaptive analysis of visual cognition using genetic algorithms.  

PubMed

Two experiments used a novel, open-ended, and adaptive test procedure to examine visual cognition in animals. Using a genetic algorithm, a pigeon was tested repeatedly from a variety of different initial conditions for its solution to an intermediate brightness search task. On each trial, the animal had to accurately locate and peck a target element of intermediate brightness from among a variable number of surrounding darker and lighter distractor elements. Displays were generated from 6 parametric variables, or genes (distractor number, element size, shape, spacing, target brightness, and distractor brightness). Display composition changed over time, or evolved, as a function of the bird's differential accuracy within the population of values for each gene. Testing 3 randomized initial conditions and 1 set of controlled initial conditions, element size and number of distractors were identified as the most important factors controlling search accuracy, with distractor brightness, element shape, and spacing making secondary contributions. The resulting changes in this multidimensional stimulus space suggested the existence of a set of conditions that the bird repeatedly converged upon regardless of initial conditions. This psychological "attractor" represents the cumulative action of the cognitive operations used by the pigeon in solving and performing this search task. The results are discussed regarding their implications for visual cognition in pigeons and the usefulness of adaptive, subject-driven experimentation for investigating human and animal cognition more generally. (PsycINFO Database Record (c) 2013 APA, all rights reserved). PMID:24000905

Cook, Robert G; Qadri, Muhammad A J

2013-09-02

486

Docking ligands to vasopressin and oxytocin receptors via genetic algorithm.  

PubMed

The aim of the study was to computer-dock selected ligands to neurophyseal receptors in order to identify amino acid residues responsible for ligand-receptor interactions. To this aim, reliable oxytocin receptor (OTR) and arginine-vasopressin receptor (V1aR/V2R) models were built. The OTR-selective agonist [Thr4,Gly7]OT, the OTR-selective cyclohexapeptide antagonist L-366,948 and OT itself were docked via genetic algorithm to OTR, V1aR, and V2R and relaxed using a constrained simulated annealing protocol. For the analysis of receptor/ligand interactions a subset of initial conformations was chosen using energetic and steric criteria. All three ligands seem to prefer similar modes of binding to the receptors, manifested by repetitive residues of the receptors which directly interact with the ligands. Taking into account that many aspects of mechanisms of G protein-coupled receptor (GPCR) action are still unsolved, the results obtained with the docking simulations may propose future experimental research, especially in site-directed mutagenesis analysis and searching for key amino acid residues responsible for drug activities. PMID:12503629

Politowska, E; Drabik, P; Kazmierkiewicz, R; Ciarkowsk, J

487

Si tight-binding parameters from genetic algorithm fitting  

NASA Astrophysics Data System (ADS)

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 genetic algorithm 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

2000-02-01

488

Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.  

PubMed

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments. PMID:18811247

Yang, Shengxiang

2008-01-01

489

Target geometry matching problem for hybrid genetic algorithm used to design structures subjected to uncertainty  

Microsoft Academic Search

The uncertainty in many engineering problems can be handled through probabilistic, fuzzy, or interval methods. This paper aims to use a hybrid genetic algorithm for tackling such problems. The proposed hybrid algorithm integrates a simple local search strategy as the worst-case-scenario technique of anti-optimization with a constrained multi-objective evolutionary algorithm. The work demonstrates the use of a technique alternating between

Nianfeng F. Wang; Y. W. Yang

2009-01-01

490

A new multi-objective genetic algorithm applied to hot-rolling process  

Microsoft Academic Search

A new genetic algorithms based multi-objective optimization algorithm (NMGA) has been developed during study. It works on a neighborhood concept in the functional space, utilizes the ideas on weak dominance and ranking and uses its own procedures for population sizing. The algorithm was successfully tested with some standard test functions, and when applied to a real-life data of the hot-rolling

N. Chakraborti; B. Siva Kumar; V. Satish Babu; S. Moitra; A. Mukhopadhyay

2008-01-01

491

Object matching using weight Hausdorff distance matrix combined with genetic algorithm  

NASA Astrophysics Data System (ADS)

A new similarity measure based on Hausdorff Distance Matrix Frobenius Norm for object matching is proposed in this paper. This measure is more reliable and can achieve higher location accuracy compared with other measures based on classic and modified Hausdorff Distance under the condition of high level noise and high ratio occlusion of template. The search strategy based on genetic algorithms is employed to make algorithm faster. Experimental results under noise of different level demonstrate high performance of the matching algorithm.

Yu, Qiuze; Yang, Bing; Liu, Jian; Tian, Jinwen

2007-11-01

492

Frequency response masking filter design using an oscillation search genetic algorithm  

Microsoft Academic Search

In this paper, an efficient optimization scheme is proposed for the design of frequency-response masking (FRM) FIR filters in the signed power-of-two (SPoT) space. The scheme is generated by integrating the genetic algorithm (GA) with an oscillation search (OS) algorithm. The optimization process is controlled by the GA. The OS algorithm is developed based on the properties of filter coefficients,

Ling Cen

2007-01-01

493

A real-coded genetic algorithm involving a hybrid crossover method for power plant control system design  

Microsoft Academic Search

This paper introduces a new hybrid crossover method for a real-coded genetic algorithm and its application to control system design of a power plant. Determining gains for controllers by using a genetic algorithm method usually involves multiple training stages. This method is not necessarily optimal. This paper applies a hybrid crossover method in a real-coded genetic algorithm to simultaneously find

Kwang Y. Lee; P. S. Mohamed

2002-01-01

494

Parallel island genetic algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimization  

Microsoft Academic Search

In this work, we focus the application of an Island Genetic Algorithm (IGA), a coarse-grained parallel genetic algorithm (PGA) model, to a Nuclear Power Plant (NPP) Auxiliary Feedwater System (AFWS) surveillance tests policy optimization. Here, the main objective is to outline, by means of comparisons, the advantages of the IGA over the simple (non-parallel) genetic algorithm (GA), which has been

Cláudio M. N. A. Pereira; Celso M. F. Lapa

2003-01-01

495

Automatic Music Genre Classification Using Hybrid Genetic Algorithms  

Microsoft Academic Search

This paper aims at developing an Automatic Music Genre Classification system and focuses on calculating algorithms that (ideally) can predict the music class in which a music file belongs. The proposed system is based on techniques from the fields of Signal Processing, Pattern Recognition, and Information Retrieval, as well as Heuristic Optimization Methods. One thousand music files are used for

George V. Karkavitsas; George A. Tsihrintzis

2011-01-01

496

Balanced accuracy for feature subset selection with genetic algorithms  

Microsoft Academic Search

The relevance of a set of measured features describing labeled patterns within a problem domain affects classifier performance. Feature subset selection algorithms employing a wrapper approach typically assess the fitness of a feature subset simply as the accuracy of a given classifier over a set of available patterns using the candidate feature set. For datasets with many patterns for some

Michael R. Peterson; Michael L. Raymer; Gary B. Lamont

2005-01-01

497

RGBCA-genetic bee colony algorithm for travelling salesman problem  

Microsoft Academic Search

Challenge of finding the shortest route visiting each member of a collection of locations and returning to starting point is an NP-hard problem. It is also known as Traveling salesman problem, TSP is specific problem of combinatorial optimization studied in computer science and mathematical applications. In our work we present a hybrid version of Evolutionary algorithm to solve TSP problem.

Vikas singh; Ritu Tiwari; Deepak Singh; Anupam Shukla

2011-01-01

498

Registration area planning for PCS networks using genetic algorithms  

Microsoft Academic Search

In a personal communication service (PCS) network, the signaling traffic required to support user mobility is extremely high due to the huge numbers of users and the small sizes of cells. The requirement to minimize this traffic increases the importance of registration area (RA) planning in the PCS network design. In the literature, several heuristic algorithms have been proposed for

Tsan-Pin Wang; Shu-Yuen Hwang; Chien-Chao Tseng

1998-01-01

499

A Genetic Algorithm Approach to Nonlinear Least Squares Estimation  

ERIC Educational Resources Information Center

|A common type of problem encountered in mathematics is optimizing nonlinear functions. Many popular algorithms that are currently available for finding nonlinear least squares estimators, a special class of nonlinear problems, are sometimes inadequate. They might not converge to an optimal value, or if they do, it could be to a local rather than…

Olinsky, Alan D.; Quinn, John T.; Mangiameli, Paul M.; Chen, Shaw K.

2004-01-01

500

Flight management of multiple aerial vehicles using genetic algorithms  

Microsoft Academic Search

Flight management system (FMS) plays an important role in automatic and fuel-efficient flight from take-off to landing and reducing the pilot's workload. Path planning and tracking are two major functions of FMS. Most flight control algorithms assume that reference trajectory is available, which is not the case in real world. In this paper we introduce a new FMS that not

S. Kanury; Y. D. Song

2006-01-01