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1

Genetic algorithms  

NASA Technical Reports Server (NTRS)

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

Wang, Lui; Bayer, Steven E.

1991-01-01

2

Genetic Algorithms and Genetic Linkage  

Microsoft Academic Search

This chapter provides a summary of fundamental materials on genetic algorithms. It presents definitions of genetic algorithm terms and briefly describes how a simple genetic algorithm works. Then, it introduces the term genetic linkage and the so-called linkage problem that exists in common genetic algorithm practice. The importance of genetic linkage is often overlooked, and this chapter helps explain why

Ying-ping Chen

3

Hierarchical Cellular Genetic Algorithm  

Microsoft Academic Search

\\u000a Cellular Genetic Algorithms (cGA) are spatially distributed Genetic Algorithms that, because of their high level of diversity,\\u000a are superior to regular GAs on several optimization functions. Also, since these distributed algorithms only require communication\\u000a between few closely arranged individuals, they are very suitable for a parallel implementation. We propose a new kind of cGA,\\u000a called hierarchical cGA (H-cGA), where the

Stefan Janson; Enrique Alba; Bernabé Dorronsoro; Martin Middendorf

2006-01-01

4

GAHC: Hybrid Genetic Algorithm  

NASA Astrophysics Data System (ADS)

This paper introduces a novel improved evolutionary algorithm, which combines genetic algorithms and hill climbing. Genetic Algorithms (GA) belong to a class of well established optimization meta-heuristics and their behavior are studied and analyzed in great detail. Various modifications were proposed by different researchers, for example modifications to the mutation operator. These modifications usually change the overall behavior of the algorithm. This paper presents a binary GA with a modified mutation operator, which is based on the well-known Hill Climbing Algorithm (HCA). The resulting algorithm, referred to as GAHC, also uses an elite tournament selection operator. This selection operator preserves the best individual from the GA population during the selection process while maintaining the positive characteristics of the standard tournament selection. This paper discusses the GAHC algorithm and compares its performance with standard GA.

Matousek, Radomil

5

Genetic Algorithms Artificial Life  

E-print Network

] are associated with a variety of work in the late 1950s and early 1960s, some of which presages the later research questions in this field. 1 #12;2 Overview of Genetic Algorithms In the 1950s and 1960s several, and some, like many GAs, had binary strings as abstrac- tions of biological chromosomes. In the later 1960s

Mitchell, Melanie

6

Genetic Algorithms and Local Search  

NASA Technical Reports Server (NTRS)

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

Whitley, Darrell

1996-01-01

7

Hybrid Genetic Algorithms: A Review  

Microsoft Academic Search

Hybrid genetic algorithms have received significant interest in recent years and are being increasingly used to solve real-world problems. A genetic algorithm is able to incorporate other techniques within its framework to produce a hybrid that reaps the best from the combination. In this paper, different forms of integration between genetic algorithms and other search and optimization techniques are reviewed.

Tarek A. El-mihoub; Adrian A. Hopgood; Lars Nolle; Alan Battersby

2006-01-01

8

Genetic-Algorithm Programming Environments  

Microsoft Academic Search

Interest in Genetic algorithms is expanding rapidly. This paper reviews software environments for programming Genetic Algorithms (GAs). As background, we initially preview genetic algorithms' models and their programming. Next we classify GA software environments into three main categories: Application-oriented, Algorithm-oriented and Tool-Kits. For each category of GA programming environment we review their common features and present a case study of

José L. Ribeiro Filho; Philip C. Treleaven; Cesare Alippi

1994-01-01

9

GENETIC ALGORITHMS CONTROL SYSTEMS ENGINEERING  

E-print Network

GENETIC ALGORITHMS IN CONTROL SYSTEMS ENGINEERING P. J. FLEMING R. C. PURSHOUSE Department. 789 May 2001 #12;Genetic algorithms in control systems engineering P. J. Fleming and R. C. Purshouse of Automatic Control and Systems Engineering University of Sheffield Sheffield, S1 3JD UK Research Report No

Coello, Carlos A. Coello

10

Messy genetic algorithms: Recent developments  

SciTech Connect

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

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

1996-09-01

11

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

12

Genetic Algorithms Artificial Life  

E-print Network

], Bledsoe [18], and Bremermann [22] are associated with a variety of work in the late 1950s and early 1960s strategies, see [9]. Also in the 1960s Fogel, Owens, and Walsh developed ``evolutionary programming'' [36 Algorithms In the 1950s and 1960s several computer scientists independently studied evolutionary sys­ tems

Forrest, Stephanie

13

Scheduling with genetic algorithms  

NASA Technical Reports Server (NTRS)

In many domains, scheduling a sequence of jobs is an important function contributing to the overall efficiency of the operation. At Boeing, we develop schedules for many different domains, including assembly of military and commercial aircraft, weapons systems, and space vehicles. Boeing is under contract to develop scheduling systems for the Space Station Payload Planning System (PPS) and Payload Operations and Integration Center (POIC). These applications require that we respect certain sequencing restrictions among the jobs to be scheduled while at the same time assigning resources to the jobs. We call this general problem scheduling and resource allocation. Genetic algorithms (GA's) offer a search method that uses a population of solutions and benefits from intrinsic parallelism to search the problem space rapidly, producing near-optimal solutions. Good intermediate solutions are probabalistically recombined to produce better offspring (based upon some application specific measure of solution fitness, e.g., minimum flowtime, or schedule completeness). Also, at any point in the search, any intermediate solution can be accepted as a final solution; allowing the search to proceed longer usually produces a better solution while terminating the search at virtually any time may yield an acceptable solution. Many processes are constrained by restrictions of sequence among the individual jobs. For a specific job, other jobs must be completed beforehand. While there are obviously many other constraints on processes, it is these on which we focussed for this research: how to allocate crews to jobs while satisfying job precedence requirements and personnel, and tooling and fixture (or, more generally, resource) requirements.

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

1994-01-01

14

Genetic algorithms and their applications  

Microsoft Academic Search

This article introduces the genetic algorithm (GA) as an emerging optimization algorithm for signal processing. After a discussion of traditional optimization techniques, it reviews the fundamental operations of a simple GA and discusses procedures to improve its functionality. The properties of the GA that relate to signal processing are summarized, and a number of applications, such as IIR adaptive filtering,

K. S. Tang; K. F. Man; S. Kwong; Q. He

1996-01-01

15

Jitter minimization with genetic algorithms  

Microsoft Academic Search

Transmission network induced jitter in periodic control variables is a known problem on field bus based distributed systems for embedded control applications. This jitter can be reduced or eliminated if adequate release instants are imposed to the periodic messages transmitted. In this paper, jitter reduction is achieved testing a variant genetic algorithm that determines an adequate initial phasing. This algorithm

Fernanda Coutinho; J. Barreiros; J. A. Fonseca; Ernesto Costa

2000-01-01

16

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

17

Distance Measures in Genetic Algorithms  

Microsoft Academic Search

\\u000a Metric is one of the fundamental tools for understanding space. It gives induced topology to the space and it is the most\\u000a basic way to provide the space with topology. Di.erent metrics make di.erent topologies. The shape of the space largely depends\\u000a on its metric. In understanding genetic algorithms, metric is also basic and important. In genetic algorithms, a good

Yong-hyuk Kim; Byung-Ro Moon

2004-01-01

18

Genetic algorithm with affinity propagation  

Microsoft Academic Search

Classical genetic algorithm suffers heavy pressure of fitness evaluation for time-consuming optimization problems, e.g., aerodynamic design optimization, qualitative model learning in bioinformatics. To address this problem, we present a combination between genetic algorithms and clustering methods. Specifically, the clustering method used in this paper is affinity propagation. The numerical experiments demonstrate that the proposed method performs promisingly for well-known benchmark

Chunguo Wu; Hao Gao; Lianjiang Yu; Yanchun Liang; Rongwu Xiang

2010-01-01

19

On convergence and optimality of genetic algorithms  

Microsoft Academic Search

An action of genetic algorithm could be represented in the search space as a random Markovian process. The question concerning its asymptotic stability properties is discussed. Conditions under which genetic algorithm is convergent, are formulated. Then the existence of an operator to which infinite long iterations of the genetic algorithms tend, is shown. This operator describes optimal genetic algorithm in

Witold Kosinski; Stefan Kotowski; Zbyszek Michalewicz

2010-01-01

20

A genetic algorithm programming environment: Splicer  

Microsoft Academic Search

Genetic algorithms have been used to solve parameter optimization problems and for machine learning. Basic genetic algorithm concepts are introduced. The authors discuss genetic algorithm applications, and present results of a project to develop a software tool-a genetic algorithm programming environment-called Splicer

Steven E. Bayer; Lui Wang

1991-01-01

21

Genetic algorithm attributes for component selection  

Microsoft Academic Search

This paper uses a genetic algorithm for component selection given a user-defined system layout, a database of components, and a defined set of design specifications. A genetic algorithm is a search method based on the principles of natural selection. An introduction to genetic algorithms is presented, and genetic algorithm attributes that are useful for component selection are explored. A comparison

Susan E. Carlson

1996-01-01

22

GENETIC-ALGORITHM BASED IMAGE COMPRESSION  

Microsoft Academic Search

In this paper we analyze the image compression problem using genetic clustering algorithms based on the pixels of the image. The main problem to solve is to find an algorithm that performs this clustering efficiently. Nowadays the possibility of solving clustering problems with genetic algorithms is being studied. In this paper we make use of genetic algorithms to obtain an

G. Merlo; P. Britos

23

Genetic K-means algorithm  

Microsoft Academic Search

In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partition of a given data into a specified number of clusters. GA's used earlier in clustering employ either an expensive crossover operator to generate valid child chromosomes from parent chromosomes or a costly fitness function or both. To circumvent these expensive operations, we hybridize

K. Krishna; M. Narasimha Murty

1999-01-01

24

On classification tools for genetic algorithms  

Microsoft Academic Search

Some tools to measure convergence properties of genetic algorithms are introduced. A classification proced ure is proposed for genetic algorithms based on a conjecture: the entropy and the fractal dimension of trajectories produced by them are quantities that characterize the classes of the algorithms. The role of these quantities as invariants of the algorithm classes is discussed together with the

Stefan Kotowski; Witold Kosinski; Zbigniew Michalewicz; Piotr Synak; Lukasz Brocki

2008-01-01

25

Genetic algorithms for the traveling salesman problem  

Microsoft Academic Search

This paper is a survey of genetic algorithms for the traveling salesman problem. Genetic algorithms are randomized search techniques that simulate some of the processes observed in natural evolution. In this paper, a simple genetic algorithm is introduced, and various extensions are presented to solve the traveling salesman problem. Computational results are also reported for both random and classical problems

Jean-Yves Potvin

1996-01-01

26

Selecting earthmoving equipment fleets using genetic algorithms  

Microsoft Academic Search

This paper presents an application of simulation optimization in construction utilizing genetic algorithms. The paper focuses on the use of genetic algorithms (GAs) as a tool for optimizing the total cost of earthmoving operations accounting for available equipment models to contractors and their corresponding quantities. The developed genetic algorithm has a powerful computational utility that increases its efficiency. The fitness

Mohamed Marzouk; Osama Moselhi

2002-01-01

27

Theory of Genetic Algorithms ---extended abstract---  

E-print Network

some methods for examining the fundamental properties of genetic algorithms ([Hol75, Jon75, Gol89, MitTheory of Genetic Algorithms ---extended abstract--- Thomas B¨ack \\Lambda , Jeannette M. de Graaf.e., strings of a fixed length consisting of zeroes and ones. Genetic algorithms are a subfield of evolutionary

Kosters, Walter

28

An introduction to genetic algorithms for electromagnetics  

Microsoft Academic Search

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

Randy L. Haupt

1995-01-01

29

Problem solving with genetic algorithms and Splicer  

NASA Technical Reports Server (NTRS)

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

Bayer, Steven E.; Wang, Lui

1991-01-01

30

Genetic Algorithms in Solving Graph Partitioning Problem  

Microsoft Academic Search

\\u000a The Graph Partitioning Problem (GPP) is one of the fundamental multimodal combinatorial problems that has many applications\\u000a in computer science. Many algorithms have been devised to obtain a reasonable approximate solution for the GP problem. This\\u000a paper applies different Genetic Algorithms in solving GP problem. In addition to using the Simple Genetic Algorithm (SGA),\\u000a it introduces a new genetic algorithm

Sahar Shazely; Hoda Baraka; Ashraf H. Abdel-wahab; Hanan Kamal

1999-01-01

31

Genetic Algorithms Viewed as Anticipatory Systems  

NASA Astrophysics Data System (ADS)

This paper proposes a new version of genetic algorithms—the anticipatory genetic algorithm AGA. The performance evaluation included in the paper shows that AGA is superior to traditional genetic algorithm from both speed and accuracy points of view. The paper also presents how this algorithm can be applied to solve a complex problem: image annotation, intended to be used in content based image retrieval systems.

Mocanu, Irina; Kalisz, Eugenia; Negreanu, Lorina

2010-11-01

32

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

33

Scalability Problems of Simple Genetic Algorithms  

Microsoft Academic Search

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

Dirk Thierens

1999-01-01

34

Clustering Based on Genetic Algorithms  

Microsoft Academic Search

Clustering is an important abstraction process and it plays a vital role in both pattern recognition and data mining. Partitional\\u000a algorithms are frequently used for clustering large data sets. K-means algorithm is the most popular partitional clustering\\u000a algorithm; its fuzzy, rough, probabilistic and neural network are also popular. However, a major problem with the K-means\\u000a algorithm and its variants is

M. Narasimha Murty; Rashmin Babaria; Chiranjib Bhattacharyya

2008-01-01

35

A genetic algorithm for replica server placement  

NASA Astrophysics Data System (ADS)

Modern distribution systems use replication to improve communication delay experienced by their clients. Some techniques have been developed for web server replica placement. One of the previous studies was Greedy algorithm proposed by Qiu et al, that needs knowledge about network topology. In This paper, first we introduce a genetic algorithm for web server replica placement. Second, we compare our algorithm with Greedy algorithm proposed by Qiu et al, and Optimum algorithm. We found that our approach can achieve better results than Greedy algorithm proposed by Qiu et al but it's computational time is more than Greedy algorithm.

Eslami, Ghazaleh; Toroghi Haghighat, Abolfazl

2012-01-01

36

Cognitive Radio — Genetic Algorithm Approach  

NASA Astrophysics Data System (ADS)

Cognitive Radio (CR) is relatively a new technology, which intelligently detects a particular segment of the radio spectrum currently in use and selects unused spectrum quickly without interfering the transmission of authorized users. Cognitive Radios can learn about current use of spectrum in their operating area, make intelligent decisions, and react to immediate changes in the use of spectrum by other authorized users. The goal of CR technology is to relieve radio spectrum overcrowding, which actually translates to a lack of access to full radio spectrum utilization. Due to this adaptive behavior, the CR can easily avoid the interference of signals in a crowded radio frequency spectrum. In this research, we discuss the possible application of genetic algorithms (GA) to create a CR that can respond intelligently in changing and unanticipated circumstances and in the presence of hostile jammers and interferers. Genetic algorithms are problem solving techniques based on evolution and natural selection. GA models adapt Charles Darwin's evolutionary theory for analysis of data and interchanging design elements in hundreds of thousands of different combinations. Only the best-performing combinations are permitted to survive, and those combinations "reproduce" further, progressively yielding better and better results.

Reddy, Y. B.

2005-03-01

37

Dynamic Populations in Genetic Algorithms Zhanshan (Sam) Ma  

E-print Network

Dynamic Populations in Genetic Algorithms Zhanshan (Sam) Ma University of Idaho Computer Science of genetic algorithms (GA). In particular, we test five dynamic population-sizing patterns: random. General Terms Algorithms. Keywords: Dynamic Population, Fluctuating Population, Genetic Algorithms

Krings, Axel W.

38

Genetic algorithms approach to voltage optimization  

Microsoft Academic Search

The authors consider the use of genetic algorithms as a measure of voltage optimization of electric power system. Genetic algorithms are optimization and learning techniques based on natural selection and natural population genetics. A formation of a power system is encoded to a string of characters called an artificial chromosome the initial population of strings are generated at random, and

Takeshi Haida; Yoshiakira Akimoto

1991-01-01

39

RPSGAe - Reduced Pareto Set Genetic Algorithm: A Multiobjective Genetic Algorithm with Elitism  

Microsoft Academic Search

In this paper a Multiobjective Optimization Genetic Algorithm, named Reduced Pareto Set Genetic Algorithm with Elitism (RPSGAe), is presented and its performance assessed. The algorithm is compared with other MOEAs using three difficult problems from the literature and a sophisticated statistical comparison technique. The preliminary results obtained showed that the RPSGAe outperform the other algorithms tested.

A. Gaspar-Cunha

40

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

41

Structure-Specified Real Coded Genetic Algorithms with Applications  

Microsoft Academic Search

Absract This chapter intends to present a brief review of genetic search algorithms and introduce a new type of genetic algorithms (GAs) called the real coded structural genetic algorithm (RSGA) for function optimization. The new genetic model combines the advantages of traditional real genetic algorithm (RGA) with structured genetic algorithm (SGA). This specific feature makes it able to solve more

Chun-Liang Lin; Ching-Huei Huang; Chih-Wei Tsai

42

Solve Zero-One Knapsack Problem by Greedy Genetic Algorithm  

Microsoft Academic Search

In order to overcome the disadvantages of the traditional genetic algorithm and improve the speed and precision of the algorithm, the author improved the selection strategy, integrated the greedy algorithm with the genetic algorithm and formed the greedy genetic algorithm. The paper discussed the basic idea and method to solve the zero-one knapsack problem using this greedy genetic algorithm. The

Yuxiang Shao; Hongwen Xu; Weiming Yin

2009-01-01

43

Dynamic Fuzzy Logic Control of GeneticAlgorithm Probabilities.  

E-print Network

?? Genetic algorithms are commonly used to solve combinatorial optimizationproblems. The implementation evolves using genetic operators (crossover, mutation,selection, etc.). Anyway, genetic algorithms like some other… (more)

Feng, Yi

2008-01-01

44

Genetic Algorithms for Optimal Reservoir Dispatching  

Microsoft Academic Search

The fundamental guidelines for genetic algorithm to optimal reservoir dispatching have been introduced. It is concluded that with three basic generators selection, crossover and mutation genetic algorithm could search the optimum solution or near-optimal solution to a complex water resources problem. Alternative formulation schemes of a GA are considered. The real-value coding is proved significantly faster than binary coding, and

Chang Jian-Xia; Huang Qiang; Wang Yi-min

2005-01-01

45

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

46

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

47

Quadruped Gait Learning Using Cyclic Genetic Algorithms  

E-print Network

and in particular, Genetic Algorithms, have previously been used to develop gaits for legged (primarily hexapod]. In a previous work Parker made use of cyclic genetic algorithms to develop walking gaits for a hexapod robot [5]. Each of the six legs of this hexapod robot could only move vertically and horizontally and the number

Parker, Gary B.

48

Optimization of Transform Coefficients via Genetic Algorithm  

E-print Network

Optimization of Transform Coefficients via Genetic Algorithm Steven Becke CS 470 ­Project Write.................................................................................................................... 22 #12;1 Optimization of Transform Coefficients via Genetic Algorithm Steven Becke Abstract discovered in recent years for image compression is Wavelet Transforms. Wavelet transforms can dramatically

Mock, Kenrick

49

Using Genetic Algorithms for Album Page Layouts  

Microsoft Academic Search

We describe a system that uses a genetic algorithm to interactively generate personalized album pages for visual content collections on the Internet. The system has three modules: preprocessing, page creation, and page layout. We focus on the details of the genetic algorithm used in the page-layout task.

Joe Geigel; Alexander C. Loui

2003-01-01

50

Optimizing alphabet using genetic algorithms  

Microsoft Academic Search

Data compression algorithms were usually designed for data processing symbol by symbol. The input symbols of these algorithms are usually taken from the ASCII table, i.e. the size of the input alphabet is 256 symbols which are representable by 8-bit numbers. Several other techniques were developed-syllable-based compression, which uses the syllable as a basic compression symbol, and word-based compression, which

Jan Platos; Pavel Kromer

2011-01-01

51

Adaptive Elitist-Population Based Genetic Algorithm for Multimodal Function  

E-print Network

;Adaptive Elitist-Population Based Genetic Algorithm 1161 some fundamental dilemmas in EAs implementationAdaptive Elitist-Population Based Genetic Algorithm for Multimodal Function Optimization Kwong genetic operators. Incorporation of the technique in any known evolutionary algorithm leads

Coello, Carlos A. Coello

52

An Introduction to Genetic Algorithms and Evolution Strategies  

Microsoft Academic Search

Genetic Algorithms and Evolution Strategies represent two of the three major Evolutionary Algorithms. This paper examines the history, theory and mathematical background, applications, and the current direction of bo th Genetic Algorithms and Evolution Strategies. Evolutionary Algorithms can be divided into three main areas of research: Genetic Algorithms (GA) (from which both Genetic Programming (which some researchers argue is a

Mehrdad Dianati; Insop Song; Mark Treiber

53

GASP: a new Genetic Algorithm (based on) Surviving Probability  

Microsoft Academic Search

A. Carvajal-Rodríguez , GASP : a new Genetic Algorithm (based on) Surviving Probability . Online Journal of Bioinformatics 5:23-31, 2004. A new basic genetic algorithm, called GASP (Genetic Algorithm Surviving Probability) is described. The algorithm differs in some essential properties compared to other genetic algorithms (GA's) and is more accurate than traditional GA's in solving some general problems. In GASP

A. Carvajal-Rodríguez

54

On Genetic Algorithms for Boolean Matrix Factorization  

Microsoft Academic Search

Matrix factorization or factor analysis is an important task in the analysis of high dimensional real world data. There are several well known methods and algorithms for factorization of real data but they are rather inefficient when dealing with binary information. In this paper we introduce background and initial version of genetic algorithm for binary matrix factorization.

Václav Snásel; Jan Platos; Pavel Krömer

2008-01-01

55

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

56

EQUIVALENCE CLASS ANALYSIS OF GENETIC ALGORITHMS  

Microsoft Academic Search

The conventional understanding of genetic algorithms de- pends upon analysis by schemata and the notion of intrinsic parallelism. For this reason, only -ary string representa- tions have had any formal basis and non-standard represent- ations and operators have been regarded largely as heurist- ics, rather than principled algorithms. This paper extends the analysis to general representations through identifica- tion of

Nicholas J. Radcliffe

1990-01-01

57

Implicit alternative splicing for genetic algorithms  

Microsoft Academic Search

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

Philipp Rohlfshagen; John A. Bullinaria

2007-01-01

58

REPRESENTING RECTILINEAR STEINER TREES IN GENETIC ALGORITHMS  

E-print Network

- clidean plane. The rectilinear Steiner problem seeks a rectilinear Steiner tree of minimum length algorithm for the rectilinear Steiner problem. The #12;rst is based on Prufer's proof of Cayley's for- mula|and compares the two codings in a genetic algorithm for the rectilinear Steiner problem, which seeks a shortest

Julstrom, Bryant A.

59

Genetic algorithm based tomographic flow visualization  

E-print Network

the field of evolutionary computing. For verification of the technique, both axisymmetric and asymmetric phantom density fields are tested for a limited number of projections under interferometric visualization. For the hybrid genetic algorithm...

Lyons, Donald Paul

2012-06-07

60

Genetic algorithms and supernovae type Ia analysis  

SciTech Connect

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) {identical_to} P{sub DE}/{rho}{sub 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, E-mail: Charalampos.Bogdanos@th.u-psud.fr, E-mail: nesseris@nbi.dk [LPT, Universite de Paris-Sud-11, Bat. 210, 91405 Orsay CEDEX (France)] [LPT, Universite de Paris-Sud-11, Bat. 210, 91405 Orsay CEDEX (France)

2009-05-15

61

Job-shop scheduling using genetic algorithm  

Microsoft Academic Search

Job-shop scheduling, a typical NP-complete problem, is an important step in planning and manufacturing control of CIMS environments. Researches on job-shop scheduling focus on knowledge-based approaches and heuristic searching which are useful apart from the difficulty of obtaining knowledge. Genetic algorithms are optimization methods which use the ideas of the evolution of nature. Simple as genetic algorithms are, they are

Wu Ying; Li Bin

1996-01-01

62

A Distributed Pool Architecture for Genetic Algorithms  

E-print Network

for the degree of MASTER OF SCIENCE Approved by: Co-Chairs of Committee, Jennifer Welch Nancy Amato Committee Members, Takis Zourntos Head of Department, Valerie Taylor December 2009 Major Subject: Computer Engineering iii ABSTRACT A Distributed Pool Architecture... for Genetic Algorithms. (December 2009) Gautam Samarendra N Roy, B. Tech., Indian Institute of Technology Guwahati Co?Chairs of Advisory Committee: Dr. Jennifer Welch Dr. Nancy Amato The genetic algorithm paradigm is a well-known heuristic for solving many...

Roy, Gautam

2011-02-22

63

Genetic algorithms as global random search methods  

NASA Technical Reports Server (NTRS)

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that that schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solution and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Peck, Charles C.; Dhawan, Atam P.

1995-01-01

64

Genetic algorithms as global random search methods  

NASA Technical Reports Server (NTRS)

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Peck, Charles C.; Dhawan, Atam P.

1995-01-01

65

Genetic Algorithms for multiple objective vehicle routing  

E-print Network

The talk describes a general approach of a genetic algorithm for multiple objective optimization problems. A particular dominance relation between the individuals of the population is used to define a fitness operator, enabling the genetic algorithm to adress even problems with efficient, but convex-dominated alternatives. The algorithm is implemented in a multilingual computer program, solving vehicle routing problems with time windows under multiple objectives. The graphical user interface of the program shows the progress of the genetic algorithm and the main parameters of the approach can be easily modified. In addition to that, the program provides powerful decision support to the decision maker. The software has proved it's excellence at the finals of the European Academic Software Award EASA, held at the Keble college/ University of Oxford/ Great Britain.

Geiger, Martin Josef

2008-01-01

66

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

67

Genetic Algorithms and Evolutionary Darrell Whitley  

E-print Network

for parameter optimization problems, scheduling applications and design optimization. In terms of fielded the assembly lines of major automobile, truck and tractor manu­ facturing companies, and genetic algorithms have been used to design turbine engines currently used on commercial passenger aircraft. Genetic

Whitley, Darrell

68

Evolving blackbox quantum algorithms using genetic programming  

E-print Network

address a potential deficiency of the quantum decision tree model used to prove lower bounds on the queryEvolving blackbox quantum algorithms using genetic programming RALF STADELHOFER,1 WOLFGANG BANZHAF. In this paper we present a genetic programming system that uses some new techniques to develop and improve

Suter, Dieter

69

GPU-based Parallel Hybrid Genetic Algorithms  

E-print Network

Abstract—Over the last years, interest in hybrid metaheuristics has risen considerably in the field of optimization. Combinations of algorithms such as genetic algorithms (GAs) and local search (LS) methods have provided very powerful search algorithms. However, due to their complexity, the computational time of the solution search exploration remains exorbitant when large problem instances are to be solved. Therefore, the use of GPU-based parallel computing is required as a complementary way to speed up the search. This paper presents a new methodology to design and implement efficiently and effectively hybrid genetic algorithms on GPU accelerators. I. SCHEME OF PARALLELIZATION The adaptation of hybrid GAs on GPU requires to take into account at the same time the characteristics and underlined issues of the GPU architecture and the metaheuristics parallel models. Since the evaluation of the neighborhood is generally the time-consuming

Thé Van Luong; Nouredine Melab; El-ghazali Talbi

70

Genetic Algorithm-Based Text Clustering Technique  

Microsoft Academic Search

A modified variable string length genetic algorithm, called MVGA, is proposed for text clustering in this paper. Our algorithm\\u000a has been exploited for automatically evolving the optimal number of clusters as well as providing proper data set clustering.\\u000a The chromosome is encoded by special indices to indicate the location of each gene. More effective version of evolutional\\u000a steps can automatically

Wei Song; Soon Cheol Park

2006-01-01

71

A Genetic Algorithm Based Modification on the LTS Algorithm for Large Data Sets  

Microsoft Academic Search

The authors introduce an algorithm for estimating the least trimmed squares (LTS) parameters in large data sets. The algorithm performs a genetic algorithm search to form a basic subset that is unlikely to contain outliers. Rousseeuw and van Driessen (2006) suggested drawing independent basic subsets and iterating C-steps many times to minimize LTS criterion. The authors 'algorithm constructs a genetic

M. Hakan Satman

2012-01-01

72

An investigation of messy genetic algorithms  

NASA Technical Reports Server (NTRS)

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

Goldberg, David E.; Deb, Kalyanmoy; Korb, Bradley

1990-01-01

73

ORIGINAL PAPER A self-organizing random immigrants genetic algorithm  

E-print Network

ORIGINAL PAPER A self-organizing random immigrants genetic algorithm for dynamic optimization optimization problems Á Random immigrants 1 Introduction In recent years evolutionary algorithms (EAs) have

Yang, Shengxiang

74

Using genetic algorithms for developing amorphous silicon atomistic model  

Microsoft Academic Search

In this paper, the author presents a computer algorithm for the generation of a high-quality continuous random networks using a genetic algorithm (GA). Genetic algorithms are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence. As a one can guess, genetic algorithms are inspired by Darwin's theory about evolution. Simply said, solution to a problem

Somia M. El-Hefnawy

2003-01-01

75

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

76

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.

77

Genetic algorithm solution for partial digest problem.  

PubMed

One of the fundamental problems in computational biology is the construction of physical maps of chromosomes from the hybridisation experiments between unique probes and clones of chromosome fragments. Before introducing the shotgun sequencing method, Partial Digest Problem (PDP) was an intractable problem used to construct the physical maps of DNA sequence in molecular biology. In this paper, we develop a novel Genetic Algorithm (GA) for solving the PDP. This algorithm is implemented and compared with well-known existing algorithms on different types of random and real instances data, and the obtained results show the efficiency of our algorithm. Also, our GA is adapted to handle the erroneous data and their efficiency is presented for the large instances of this problem. PMID:24084239

Ahrabian, Hayedeh; Ganjtabesh, Mohammad; Nowzari-Dalini, Abbas; Razaghi-Moghadam-Kashani, Zahra

2013-01-01

78

Hybrid Approach to Optimal Packing Using Genetic Algorithm and Coulomb Potential Algorithm  

Microsoft Academic Search

It is difficult and computationally time-consuming to find the best possible solutions for blank packing problems, because they include a lot of underlying combinational conditions. This paper presents two approaches for packing two-dimensional irregular-shaped polygonal elements—a real-encoded genetic algorithm and a hybrid algorithm using a real-encoded genetic algorithm and a local optimization algorithm. The local optimization algorithm presented is a

Biswajit Mahanty; Rajneesh Kumar Agrawal; Shrikrishna Shrin; Sourish Chakravarty

2007-01-01

79

An Asymptotic Theory of Genetic Algorithms  

Microsoft Academic Search

The Freidlin-Wentzell theory deals with the study of random perturbations of dynamical systems. We build several models of genetic algorithms by randomly perturbing simple processes. The asymptotic dynamics of the resulting processes is analyzed with the powerful tools developed by Freidlin and Wentzell and later by Azencott, Catoni and Trouvé in the framework of the generalized simulated annealing. First, a

Raphaël Cerf

1995-01-01

80

Hierarchical Two-Population Genetic Algorithm  

Microsoft Academic Search

This paper proposes a new hierarchical two-population genetic algorithm (2PGA). The 2PGA scheme constitutes of two differently sized populations containing individuals of similar fitness or cost function values. The smaller population, the elite population, consists of the best individuals, whereas the larger population contains less fit individuals. These populations have different characteristics, such as size and mutation probability, based on

Jarno Martikainen; Seppo J. Ovaska

2006-01-01

81

Binary wavefront optimization using a genetic algorithm  

NASA Astrophysics Data System (ADS)

We demonstrate the use of a genetic algorithm with binary amplitude modulation of light through turbid media. We apply this method to binary amplitude modulation with a digital micromirror device. We achieve the theoretical maximum enhancement of 64 with 384 segments, and an enhancement of 320 with 6144 segments.

Zhang, Xiaolong; Kner, Peter

2014-12-01

82

Genetic Algorithms: Artificial Selection vs Natural Selection  

Microsoft Academic Search

Genetic Algorithms (GAs) are a stochastic searching and optimizing method inspired by the biological mechanism of natural selection and evolution. To improve the searching power of GAs for complicated problems, many deterministic measures, particularly, experience and\\/or expert knowledge-based heuristic rules, have been studied in the existing literature. This paper proposes a potentially more useful general methodology of integrating deterministic strategy

Xiao-Bing Hu; Ezequiel Di Paolo

83

Genetic Algorithms in Timetabling and Scheduling  

Microsoft Academic Search

This thesis investigates the use of genetic algorithms (GAs) for solving a range of timetabling and scheduling problems. Such problems are very hard in general, and GAs offer a useful and successful alternative to existing techniques. A framework is presented for GAs to solve modular timetabling problems in educational institutions. The approach involves three components: declaring problemspecific constraints, constructing a

Hsiao-lan Fang

1994-01-01

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

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

86

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

87

Energetic Operation Planning Using Genetic Algorithms  

Microsoft Academic Search

This paper investigates the application of genetic algorithms to optimize large, nonlinear complex systems, particularly the optimization of the operation planning of hydrothermal power systems. Several of the current studies to solve this kind of problem are based on nonlinear programming. This approach presents some deficiencies, such as difficult convergence, oversimplification of the original problem, or difficulties related to the

P. Leite; A. Carneiro; A. Carvalho

2001-01-01

88

Energetic operation planning using genetic algorithms  

Microsoft Academic Search

This paper investigates the application of genetic algorithms to optimize large, nonlinear complex systems, particularly the optimization of the operation planning of hydrothermal power systems. Several of the current studies to solve this kind of problem are based on nonlinear programming. This approach presents some deficiencies, such as difficult convergence, oversimplification of the original problem or difficulties related to the

Patricia Teixeira Leite; A. A. F. M. Carneiro

2002-01-01

89

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

90

Convergence properties of simple genetic algorithms  

NASA Technical Reports Server (NTRS)

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

Bethke, A. D.; Zeigler, B. P.; Strauss, D. M.

1974-01-01

91

Genetic Algorithms for Evolving Computer Chess Programs  

E-print Network

by learning from databases of (human) grandmaster games. At first the organisms are evolved to mimic be evolved by learning from other chess programs [21] and human chess players [19]. It also provided a method1 Genetic Algorithms for Evolving Computer Chess Programs Omid E. David1, H. Jaap van den Herik2

Koppel, Moshe

92

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

93

Genetic algorithms in plasma diagnostic analysis  

NASA Astrophysics Data System (ADS)

A novel sophisticated technique for data reduction utilising Bayesian statistics and a genetic algorithm has been developed by the authors. The technique gives superior signal recovery in poor signal-to-noise ratio conditions compared to simple linear least-squares methods. In this paper, the method is discussed along with a detailed case study: the Thomson scattering diagnostic of a magnetically confined plasma.

Millar, A. P.; McDonald, D. C.; Diver, D. A.

2000-03-01

94

Genetic Algorithm Based on Sugeno Integral  

Microsoft Academic Search

For the actual need of future research and application, this paper proposes a new method that is a new fuzzy control system of fuzzy integral-genetic algorithm (FI-GA). By fuzzy integral, it can study comprehensive evaluation of population diversity and individual quantity on three attributes: individual difference extent, the difference extent of individual's fitness and the difference extent of population lifetime,

Zhilong Wu; Jinjie Song; Caipo Zhang

2009-01-01

95

L. D. Davis, Handbook of Genetic Algorithms.  

E-print Network

into a significant sub-area of artificial intelligence and machine learning. Nowa- days one can find severalReview of L. D. Davis, Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold, 1991. This split roughly parallels the split of artificial intelligence research into work on engineering tools

Mitchell, Melanie

96

GSA: scheduling and allocation using genetic algorithm  

Microsoft Academic Search

This paper describes a unique approach to scheduling and allocation problem in high-level synthesis using genetic algorithm (GA). This approach is different from a previous attempt using GA [l] in many respects. Our contributions include: a new chromosomal representation for scheduling and two subproblems of allocation; and two novel crossover operators to generate legal schedules. The approach has been tested

Ali Shahid; Muhammed S. T. Benten; Sadiq M. Sait

1994-01-01

97

A genetic algorithm for assembly line balancing  

Microsoft Academic Search

Assembly line balancing is a very important aspect in any mass production setup. However, finding the optimal balance is a very difficult proposition because of the computational complexity involved. Hence sub-optimal solutions are preferred over optimal solutions. In this work, a genetic algorithm (GA) is presented for obtaining good quality solutions for assembly line balancing problems. A major feature of

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

1996-01-01

98

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

99

Lightweight telescope structure optimized by genetic algorithm  

Microsoft Academic Search

We designed the optics supporting structure (OSS) of a 3.8 m segmented mirror telescope by applying genetic algorithm optimization. The telescope is the first segmented mirror telescope in Japan whose primary mirror consists of 18 petal shaped segment mirrors. The whole mirror is supported by 54 actuators (3 actuators per each segment). In order to realize light-weight and stiff telescope

Mikio Kurita; Hiroshi Ohmori; Masashi Kunda; Hiroaki Kawamura; Noriaki Noda; Takayuki Seki; Yuji Nishimura; Michitoshi Yoshida; Shuji Sato; Tetsuya Nagata

2010-01-01

100

Regular Grammatical Inference: A Genetic Algorithm Approach  

Microsoft Academic Search

Grammatical inference is the problem of inferring a grammar, given a set of positive samples which the inferred grammar should\\u000a accept and a set of negative samples which the grammar should not accept. Here we apply genetic algorithm for inferring regular\\u000a languages. The genetic search is started from maximal canonical automaton built from structurally complete sample. In view\\u000a of limiting

Pravin Pawar; G. Nagaraja

2002-01-01

101

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

102

Using Disruptive Selection to Maintain Diversity in Genetic Algorithms  

Microsoft Academic Search

Genetic algorithms are a class of adaptive search techniques based on the principles of population genetics. The metaphor underlying genetic algorithms is that of natural evolution. With their great robustness, genetic algorithms have proven to be a promising technique for many optimization, design, control, and machine learning applications. A novel selection method, disruptive selection, has been proposed. This method adopts

Ting Kuo; Shu-Yuen Hwang

1997-01-01

103

Multiharmonic Waveform Fitting of Periodic Signals using Genetic Algorithms  

Microsoft Academic Search

A genetic algorithm is used to estimate the parameters of periodic signals in multiharmonic waveform fitting. This algorithm is used to find the fundamental frequency which can then be used in a multiple linear least-squares (LS) waveform fitting algorithm. This procedure is applied to different multiharmonic signals with very good results. The main advantage of genetic algorithms when compared with

Fernando M. Janeiro; P. M. Ramos

2007-01-01

104

A New Genetic Algorithm for Community Detection  

NASA Astrophysics Data System (ADS)

With the rapidly grown evidence that various systems in nature and society can be modeled as complex networks, community detection in networks becomes a hot research topic in many research fields. This paper proposes a new genetic algorithm for community detection. The algorithm uses the fundamental measure criterion modularity Q as the fitness function. A special locus-based adjacency encoding scheme is applied to represent the community partition. The encoding scheme is suitable for the community detection based on the reason that it determines the community number automatically and reduces the search space distinctly. In addition, the corresponding crossover and mutation operators are designed. The experiments in three aspects show that the algorithm is effective, efficient and steady.

Shi, Chuan; Wang, Yi; Wu, Bin; Zhong, Cha

105

Genetic Algorithms for Approximating Solutions to POMDPs Chris Wells  

E-print Network

University of California, Berkeley 387 Soda Hall #1776 Berkeley, CA 94720­1776 Christopher Lusena and Judy. Initializing the pop­ ulation of the genetic algorithm is done using smaller genetic algorithms. The selection

Goldsmith, Judy

106

Towards a Genetic Programming Algorithm for Automatically Evolving Rule Induction Algorithms  

E-print Network

. In general, the program evolved by GP can produce the same solution humans use to solve the target problemTowards a Genetic Programming Algorithm for Automatically Evolving Rule Induction Algorithms Gisele for auto- matically evolving computer programs. This work proposes a genetic pro- gramming algorithm

Fernandez, Thomas

107

Multi-population Genetic Algorithm for Feature Selection  

Microsoft Academic Search

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

Huming Zhu; Licheng Jiao; Jin Pan

2006-01-01

108

A New Self-adjusting Immune Genetic Algorithm  

Microsoft Academic Search

The genetic algorithm based on immunity has recently been an appealing research methodology in evolutionary computation. Aiming\\u000a to cope with the problems of genetic algorithms, i.e., the solution is apt to trap into a local optimum and the convergence\\u000a speed is slow, this paper proposes a new self-adjusting immune genetic algorithm, called SaiGa (Self-adjusted immune Genetic algorithm), which seeks for

Shaojie Qiao; Changjie Tang; Shucheng Dai; Mingfang Zhu; Binglun Zheng

2008-01-01

109

Applying Genetic Algorithm to Modeling Nonlinear Transfer Functions  

E-print Network

. FUNDAMENTALS OF THE GENETIC ALGORITHM Rapid progress in the field of computer technology and numerical methodsApplying Genetic Algorithm to Modeling Nonlinear Transfer Functions Sergey L. Loyka Abstract- A genetic algorithm technique for the approximation of nonlinear transfer functions is proposed

Loyka, Sergey

110

CEPM 1: selecting earthmoving equipment fleets using genetic algorithms  

Microsoft Academic Search

This paper presents an application of simulation optimization in construction utilizing genetic algorithms. The paper focuses on the use of genetic algorithms (GAs) as a tool for optimizing the total cost of earthmoving operations accounting for available equipment models to contractors and their corresponding quantities. The developed genetic algorithm has a powerful computational utility that increases its efficiency. The fitness

Mohamed Marzouk; Osama Moselhi

2002-01-01

111

Designing a fuzzy model by adaptive macroevolution genetic algorithms  

Microsoft Academic Search

In this paper the adaptive macroevolution genetic algorithms are proposed to identify three different types of fuzzy models. Several newly established techniques, such as adaptive choice function and macroevolution, are adopted into the simple genetic algorithms to improve the optimization capability. The genetic algorithms used here are controlled to retain the best solution in the population until a better one

Yo-Ping Huang; Sheng-Fang Wang

2000-01-01

112

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

113

Modeling the model Characteristics and behavior of genetic algorithms  

E-print Network

Modeling the model Characteristics and behavior of genetic algorithms Author. Janeen Neri Progress in data files containing the fitness distribution and genetic algorithm specifications to be tested, and normalizes the fitness data for easier manipulation. A de- tailed pseudocode outline of the genetic algorithm

114

A Robust Genetic Algorithm for Resource Allocation in Project Scheduling  

Microsoft Academic Search

Genetic algorithms have been applied to many different optimization problems and they are one of the most promising metaheuristics. However, there are few published studies concerning the design of efficient genetic algorithms for resource allocation in project scheduling. In this work we present a robust genetic algorithm for the single-mode resource constrained project scheduling problem. We propose a new representation

Javier Alcaraz; Concepción Maroto

2001-01-01

115

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

116

GADO: A GENETIC ALGORITHM FOR CONTINUOUS DESIGN OPTIMIZATION  

E-print Network

of the Seventh International Conference on Genetic Algorithms, with Haym Hirsh as co­author [ Rasheed and HirshGADO: A GENETIC ALGORITHM FOR CONTINUOUS DESIGN OPTIMIZATION BY KHALED MOHAMED RASHEED GADO: A Genetic Algorithm for Continuous Design Optimization by Khaled Mohamed Rasheed Dissertation

Rasheed, Khaled

117

GENETIC ALGORITHMS FOR GRAPH LAYOUTS WITH GEOMETRIC CONSTRAINTS  

E-print Network

this method with the use of genetic algorithms. The authors have applied their model to graph layoutsGENETIC ALGORITHMS FOR GRAPH LAYOUTS WITH GEOMETRIC CONSTRAINTS Dana Vrajitoru Intelligent Systems and not only on precision. KEY WORDS genetic algorithms, graph theory, geometric modeling 1 Introduction

Vrajitoru, Dana

118

Genetic algorithm using real parameters for array antenna design optimisation  

Microsoft Academic Search

The use of genetic algorithms (GAs) in the design of antennas has become increasingly popular. This is due to their versatility and ability to perform a rigorous search in complex multi-modal search spaces. Much attention has previously been placed on binary genetic algorithms. However, few authors have looked at the use of continuous (decimal) genetic algorithms, particularly for antenna design.

Yee Hui Lee; Andrew C. Marvin; S. J. Porter

1999-01-01

119

The parameter-less genetic algorithm in practice  

E-print Network

was to make genetic algorithms (GAs) easier to use and to make them * Corresponding author. E-mail addressesThe parameter-less genetic algorithm in practice Fernando G. Lobo a,*, David E. Goldberg b a Area 2003 Abstract The parameter-less genetic algorithm was introduced a couple of years ago as a way

Lobo, Fernando

120

Real Coded Genetic Algorithm Optimization of Long Term Reservoir Operation  

Microsoft Academic Search

An optimization and simulation model holds promise as an efficient and robust method for long term reservoir operation, an increasingly important facet of managing water resources. Recently, genetic algorithms have been demonstrated to be highly effective optimization methods. According to previous studies, a real coded genetic algorithm (RGA) has many advantages over a binary coded genetic algorithm. Accordingly, this work

Li Chen

2003-01-01

121

Optimal hydrogenerator governor tuning with a genetic algorithm  

Microsoft Academic Search

The authors investigate the application of a genetic algorithm for optimizing the gains of a proportional-plus-integral controller for a hydrogenerator plant. The genetic algorithm was 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

J. E. Lansberry; L. Wozniak; D. E. Goldberg

1992-01-01

122

Inverter for microturbines based on multiobjective genetic algorithm  

Microsoft Academic Search

In this paper, authors present a new design method for pulse width modulation inverters in microturbines by using a multiobjective genetic algorithm. The design problem is converted to an equivalent optimization problem, and then a multiobjective genetic algorithm is adopted to find a solution. The genetic algorithm is proposed to design a fuzzy controller. In this GA approach, an individual

F. Jurado; M. Valverde

2005-01-01

123

Improved Genetic Algorithms to Solving Constrained Optimization Problems  

Microsoft Academic Search

The slow convergence speed and the lack of effective constraint handling strategies are the major concerns when applying genetic algorithms (Gas) to constrained optimization problem. An improved genetic algorithm was proposed by dividing population into three parts: optimal subpopulation, elitists subpopulation and spare subpopulation. We applied genetic algorithm on three subpopulations with different evolutionary strategies. Isolation of optimal subpopulation was

Zhu Can; Liang Xi-Ming; Zhou Shu-renhu

2009-01-01

124

Birefringent filter design by use of a modified genetic algorithm  

E-print Network

Birefringent filter design by use of a modified genetic algorithm Mengtao Wen and Jianping Yao A modified genetic algorithm is proposed for the optimization of fiber birefringent filters. The orientation angles and the element lengths are determined by the genetic algorithm to minimize the sidelobe levels

Yao, Jianping

125

Holographic diffuser design using a modified genetic algorithm  

E-print Network

Holographic diffuser design using a modified genetic algorithm Mengtao Wen Jianping Yao, MEMBER Singapore 639798 Abstract. A modified genetic algorithm is proposed for the optimization of holographic diffusers for diffuse IR wireless home networking. The novel algorithm combines the conventional genetic

Yao, Jianping

126

Doherty Amplifier Optimization Using Robust Genetic Algorithm and Unscented Transform  

E-print Network

Doherty Amplifier Optimization Using Robust Genetic Algorithm and Unscented Transform Marcos L@eee.ufg.br, granato@eee.ufg.br Abstract-- A robust genetic circuit optimizer using Unscented Transform and Non-dominated Sorting Genetic Algorithm-II is presented. The algorithm provides significant decrease in compu- tational

Paris-Sud XI, Université de

127

A hybrid genetic algorithm for resolving closely spaced objects  

NASA Technical Reports Server (NTRS)

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

Abbott, R. J.; Lillo, W. E.; Schulenburg, N.

1995-01-01

128

Application of Genetic Algorithms in Seismic Tomography  

NASA Astrophysics Data System (ADS)

In the earth sciences several inverse problems that require data fitting and parameter estimation are nonlinear and can involve a large number of unknown parameters. Consequently, the application of analytical inversion or optimization techniques may be quite restrictive. In practice, most analytical methods are local in nature and rely on a linearized form of the problem in question, adopting an iterative procedure using partial derivatives to improve an initial model. This approach can lead to a dependence of the final model solution on the starting model and is prone to entrapment in local misfit minima. Moreover, the calculation of derivatives can be computationally inefficient and create instabilities when numerical approximations are used. In contrast to these local minimization methods, global techniques that do not rely on partial derivatives, are independent of the form of the data misfit criterion, and are computationally robust. Such methods often use random processes to sample a selected wider span of the model space. In this situation, randomly generated models are assessed in terms of their data-fitting quality and the process may be stopped after a certain number of acceptable models is identified or continued until a satisfactory data fit is achieved. A new class of methods known as genetic algorithms achieves the aforementioned approximation through novel model representation and manipulations. Genetic algorithms (GAs) were originally developed in the field of artificial intelligence by John Holland more than 20 years ago, but even in this field it is less than a decade that the methodology has been more generally applied and only recently did the methodology attract the attention of the earth sciences community. Applications have been generally concentrated in geophysics and in particular seismology. As awareness of genetic algorithms grows there surely will be many more and varied applications to earth science problems. In the present work, the application of hybrid genetic algorithms in seismic tomography is examined and the efficiency of least squares and genetic methods as representative of the local and global optimization, respectively, is presented and evaluated. The robustness of both optimization methods has been tested and compared for the same source-receiver geometry and characteristics of the model structure (anomalies, etc.). A set of seismic refraction synthetic (noise free) data was used for modeling. Specifically, cross-well, down-hole and typical refraction studies using 24 geophones and 5 shoots were used to confirm the applicability of the genetic algorithms in seismic tomography. To solve the forward modeling and estimate the traveltimes, the revisited ray bending method was used supplemented by an approximate computation of the first Fresnel volume. The root mean square (rms) error as the misfit function was used and calculated for the entire random velocity model for each generation. After the end of each generation and based on the misfit of the individuals (velocity models), the selection, crossover and mutation (typical process steps of genetic algorithms) were selected continuing the evolution theory and coding the new generation. To optimize the computation time, since the whole procedure is quite time consuming, the Matlab Distributed Computing Environment (MDCE) was used in a multicore engine. During the tests, we noticed that the fast convergence that the algorithm initially exhibits (first 5 generations) is followed by progressively slower improvements of the reconstructed velocity models. Thus, to improve the final tomographic models, a hybrid genetic algorithm (GA) approach was adopted by combining the GAs with a local optimization method after several generations, on the basis of the convergence of the resulting models. This approach is shown to be efficient, as it directs the solution search towards a model region close to the global minimum solution.

Soupios, Pantelis; Akca, Irfan; Mpogiatzis, Petros; Basokur, Ahmet; Papazachos, Constantinos

2010-05-01

129

Fashion sketch design by interactive genetic algorithms  

NASA Astrophysics Data System (ADS)

Computer aided design is vitally important for the modern industry, particularly for the creative industry. Fashion industry faced intensive challenges to shorten the product development process. In this paper, a methodology is proposed for sketch design based on interactive genetic algorithms. The sketch design system consists of a sketch design model, a database and a multi-stage sketch design engine. First, a sketch design model is developed based on the knowledge of fashion design to describe fashion product characteristics by using parameters. Second, a database is built based on the proposed sketch design model to define general style elements. Third, a multi-stage sketch design engine is used to construct the design. Moreover, an interactive genetic algorithm (IGA) is used to accelerate the sketch design process. The experimental results have demonstrated that the proposed method is effective in helping laypersons achieve satisfied fashion design sketches.

Mok, P. Y.; Wang, X. X.; Xu, J.; Kwok, Y. L.

2012-11-01

130

Seismic velocity inversion with genetic algorithms  

Microsoft Academic Search

We use genetic algorithms to find geologically plausible sub-surface models from seismic travel-time data. Given a sub-surface model, the physics of wave propagation through refractive media can be used to compute travel times for seismic waves. However, in practice, we have to solve the inverse problem: travel-times are available and the problem is to infer sub-surface structure. This inverse problem

S. J. Louis; Qinxue Chen; Satish Pullammanappallil

1999-01-01

131

Genetic algorithm for robotic telescope scheduling  

E-print Network

This work was inspired by author experiences with a telescope scheduling. Author long time goal is to develop and further extend software for an autonomous observatory. The software shall provide users with all the facilities they need to take scientific images of the night sky, cooperate with other autonomous observatories, and possibly more. This works shows how genetic algorithm can be used for scheduling of a single observatory, as well as network of observatories.

Kubanek, Petr

2010-01-01

132

Design of hyperbolic metamaterials by genetic algorithm  

NASA Astrophysics Data System (ADS)

We explain the design of one dimensional Hyperbolic Metamaterials (HMM) using a genetic algorithm (GA) and provide sample applications including the realization of negative refraction. The design method is a powerful optimization approach to find the optimal performance of such structures, which "naturally" finds HMM structures that are globally optimized for specific applications. We explain how a fitness function can be incorporated into the GA for different metamaterial properties.

Goforth, Ian A.; Alisafaee, Hossein; Fullager, Daniel B.; Rosenbury, Chris; Fiddy, Michael A.

2014-09-01

133

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

2011-06-01

134

Using a genetic algorithm to solve fluid-flow problems  

SciTech Connect

Genetic algorithms are based on the mechanics of the natural selection and natural genetics processes. These algorithms are finding increasing application to a wide variety of engineering optimization and machine learning problems. In this paper, the authors demonstrate the use of a genetic algorithm to solve fluid flow problems. Specifically, the authors use the algorithm to solve the one-dimensional flow equations for a pipe.

Pryor, R.J. (Sandia National Lab., Albuquerque, NM (USA))

1990-06-01

135

V.: A genetic engineering approach to genetic algorithms  

E-print Network

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.

John S. Gero; Vladimir Kazakov

136

Estimating Photometric Redshifts Using Genetic Algorithms  

NASA Astrophysics Data System (ADS)

Photometry is used as a cheap and easy way to estimate redshifts of galaxies, which would otherwise require considerable amounts of expensive telescope time. However, the analysis of photometric redshift datasets is a task where it is sometimes difficultto achievea high classification accuracy. This work presents a custom Genetic Algorithm (GA) for mining the Hubble Deep Field North (HDF-N) datasets to achieve accurate IF-THEN classification rules. This kind of knowledge representation has the advantage of being intuitively comprehensible to the user, facilitating astronomers' interpretation of discovered knowledge. The GA is tested againstthe state of the art decision tree algorithm C5.0 [Rulequest, 2005] in two datasets, achieving better classification accuracy and simplerrule sets in both datasets.

Miles, Nicholas; Freitas, Alex; Serjeant, Stephen

137

A Heuristic Genetic Algorithm for Solving Resource Allocation Problems  

Microsoft Academic Search

In the paper, a heuristic genetic algorithm for solving resource allocation problems is proposed. The resource allocation problems are to allocate resources to activities so that the fitness becomes as optimal as possible. The objective of this paper is to develop an efficient algorithm to solve resource allocation problems encountered in practice. Various genetic algorithms are studied and a heuristic

Zne-Jung Lee; Shun-Feng Su; Chou-Yuan Lee; Yao-Shan Hung

2003-01-01

138

A PARALLEL GENETIC ALGORITHM FOR SOLVING THE SCHOOL TIMETABLING PROBLEM  

Microsoft Academic Search

Genetic algorithms (GA) have been applied to a number of optimisation problems with some success The algorithms mimic the process of natural selection, with the effect of creating a number of potentially optimal solutions to some complex search problem. One of the major disadvantages of genetic algorithms is that they are very slow. In this paper we discuss the application

D. ABRAMSON; J. ABELA

1992-01-01

139

Crowding clustering genetic algorithm for multimodal function optimization  

Microsoft Academic Search

Interest in multimodal function optimization is expanding rapidly since real-world optimization problems often require location of multiple optima in a search space. In this paper, we propose a novel genetic algorithm which combines crowding and clustering for multimodal function optimization, and analyze convergence properties of the algorithm. The crowding clustering genetic algorithm employs standard crowding strategy to form multiple niches

Qing Ling; Gang Wu; Zaiyue Yang; Qiuping Wang

2008-01-01

140

Hybrid Genetic Algorithm for Flow Shop Scheduling Problem  

Microsoft Academic Search

The flow shop scheduling problem (FSSP) is a NP-HARD combinatorial problem with strong industrial background. Among the meta-heuristics, genetic algorithms attracted a lot of attention. However, lacking the major evolution direction, the effectiveness of regular genetic algorithm is restricted. In this paper, the particle swarm optimization algorithm (PSO) is introduced for better initial group. By combining PSO with GA, a

Jianchao Tang; Guoji Zhang; Binbin Lin; Bixi Zhang

2010-01-01

141

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

142

GENETIC ALGORITHM DESIGN OF NETWORKS CONSIDERING ALL-TERMINAL RELIABILITY  

E-print Network

GENETIC ALGORITHM DESIGN OF NETWORKS CONSIDERING ALL-TERMINAL RELIABILITY Berna Dengiz and Fulya by placement of the nodes and the links between nodes. In this study, a genetic algorithm (GA) is presented algorithm, all-terminal reliability 1 Corresponding author. 1. INTRODUCTION The design of reliable

Smith, Alice E.

143

The Allele Metamodel - Developing a Common Language for Genetic Algorithms  

Microsoft Academic Search

Due to the lot of difierent Genetic Algorithm variants, en- codings, and attacked problems, very little general theory is available to explain the internal functioning of Genetic Algorithms. Consequently it is very di-cult for researchers to flnd a common language to document quality improvements of newly developed algorithms. In this paper the authors present a new Allele Meta-Model enabling a

Stefan Wagner; Michael Affenzeller

2005-01-01

144

A New Approach to Evolutionary Computation: Segregative Genetic Algorithms (SEGA)  

Microsoft Academic Search

This paper looks upon the standard genetic algorithm as an artiflcial self-organizing process. With the purpose to provide concepts that make the algorithm more open for scalability on the one hand, and that flght premature convergence on the other hand, this paper presents two extensions of the standard genetic algorithm without introducing any problem speciflc knowledge, as done in many

Michael Afienzeller

145

A New Approach to Evolutionary Computation: Segregative Genetic Algorithms (SEGA)  

Microsoft Academic Search

This paper looks upon the standard genetic algorithm as an artificial self-organizing process. With the purpose to provide concepts that make the algorithm more open for scalability on the one hand, and that fight premature convergence on the other hand, this paper presents two extensions of the standard genetic algorithm without introducing any problem specific knowledge, as done in many

Michael Affenzeller

2001-01-01

146

A hybrid genetic algorithm for synthesis of heat exchanger networks  

Microsoft Academic Search

A new hybrid genetic algorithm for optimal design of heat exchanger networks is developed. The mathematical model used in the algorithm is based on an explicit solution of stream temperatures of heat exchanger networks with the stage-wise superstructure. By taking heat transfer areas and heat capacity flow rates as genes in the genetic algorithm, the thermal performance and total cost

Xing Luo; Qing-Yun Wen; Georg Fieg

2009-01-01

147

Inversion of seismic refraction data using genetic algorithms  

Microsoft Academic Search

The use of genetic algorithms in geophysical inverse problems is a relatively recent development and of- fers many advantages in dealing with the nonlinearity inherent in such applications. However, in their appli- cation to specific problems, as with all algorithms, prob- lems of implementation arise. After extensive numerical tests, we implemented a genetic algorithm to efficiently invert several sets of

Fabio Boschetti; Mike C. Dentithz; Ron D. List

1996-01-01

148

Time table scheduling using Genetic Algorithms employing guided mutation  

Microsoft Academic Search

Genetic Algorithms, a class of evolutionary optimization techniques offer benefits of being probabilistic, requiring no auxiliary knowledge in comparison to conventional search methods such as calculus based, enumerative and random strategies. This paper discusses a Genetic Algorithm based university time table scheduling algorithm satisfying constraints that avoid clash of faculty, class room slots, etc. The paper exploits the rank based

Vinayak Sapru; Kaushik Reddy; B. Sivaselvan

2010-01-01

149

Improved genetic algorithm for the design of stiffened composite panels  

SciTech Connect

The stacking sequence of a composite laminate is often restricted to a small set of fiber orientations. The design of this stacking sequence is a combinatorial optimization problem which is suitable for genetic algorithms. Often, multiple solutions with similar performance are available, and genetic algorithms help find multiple rather than a single solution. However, genetic algorithms often require very large computational costs. Previous work by the authors on the use of genetic algorithms for designing stiffened composite panels revealed both the above strength and weakness of the genetic algorithm. The present paper is concerned with the use of several improvements to the basic genetic algorithm developed previously, and the effect of these improvements on computational cost and reliability of the algorithm.

Nagendra, S.; Gureal, A.; Jestin, D.; Watson, L.; Haftka, R.; Kraft, C.

1994-12-31

150

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

151

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

152

Genetic Algorithms To provide a background and understanding of basic genetic  

E-print Network

and have been successfully applied to complex engineering optimisation problems. Genetic Algorithms1 Genetic Algorithms Objectives To provide a background and understanding of basic genetic algorithms and some of their applications. ·a basic genetic algorithm ·the basic discussion ·the applications

Qu, Rong

153

Fractal Dimension of Trajectory as Invariant of Genetic Algorithms  

Microsoft Academic Search

Convergence properties of genetic algorithms are investigated. For them some measures are introduced. A classification procedure\\u000a is proposed for genetic algorithms based on a conjecture: the entropy and the fractal dimension of trajectories produced by\\u000a them are quantities that characterize the classes of the algorithms. The role of these quantities as invariants of the algorithm\\u000a classes is presented. The present

Stefan Kotowski; Witold Kosinski; Zbigniew Michalewicz; Jakub Nowicki; Bartosz Przepiórkiewicz

2008-01-01

154

Integrating Genetic Algorithm, Tabu Search Approach for Job Shop Scheduling  

Microsoft Academic Search

This paper presents a new algorithm based on integrating Genetic Algorithms\\u000aand Tabu Search methods to solve the Job Shop Scheduling problem. The idea of\\u000athe proposed algorithm is derived from Genetic Algorithms. Most of the\\u000ascheduling problems require either exponential time or space to generate an\\u000aoptimal answer. Job Shop scheduling (JSS) is the general scheduling problem and\\u000ait

R. Thamilselvan; P. Balasubramanie

2009-01-01

155

Chemical Genetic Algorithms - Evolutionary Optimization of Binary-to-Real-Value Translation in Genetic Algorithms  

Microsoft Academic Search

A chemical genetic algorithm (CGA) in which several types of molecules (information units) react with each other in a cell is proposed. Not only the information in DNA, but also smaller molecules responsible for the transcription and translation of DNA into amino acids, are adaptively changed during evolution, which optimizes the fundamental mapping from binary substrings in DNA (genotype) to

Hideaki Suzuki; Hidefumi Sawai; Wojciech Piaseczny

2006-01-01

156

Segregative Genetic Algorithms (SEGA): A hybrid superstructure upwards compatible to genetic algorithms for retarding premature convergence  

Microsoft Academic Search

Many problems of combinatorial optimization belong to the class of NP-complete problems and can be solved efficiently only by heuristics. Both, GeneticAlgorithms and Evolution Strategies have a number of drawbacks that reduce their applicability to that kind of problems. During the last decades plenty of work has been investigated in order to introduce new coding standards and operators especially for

Michael Affenzeller

2001-01-01

157

Segregative Genetic Algorithms (SEGA): A Hybrid Superstructure Upwards Compatible to Genetic Algorithms for Retarding Premature Convergence  

Microsoft Academic Search

Many problems of combinatorial optimization belong to the class of NP-complete problems and can be solved e-ciently only by heuristics. Both, Genetic Algorithms and Evolution Strategies have a number of drawbacks that reduce their applicability to that kind of problems. During the last decades plenty of work has been investigated in order to introduce new coding standards and operators especially

Michael Afienzeller

2001-01-01

158

Instrument design and optimization using genetic algorithms  

SciTech Connect

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.

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

2006-10-15

159

A Comparative Study of Immune System Based Genetic Algorithms in Dynamic Environments  

E-print Network

transformation and corresponding immune system based genetic algorithm in dynamic environments. Categories--Heuristic methods General Terms Algorithms Keywords Immune system based genetic algorithms, transformation, memoryA Comparative Study of Immune System Based Genetic Algorithms in Dynamic Environments Shengxiang

Yang, Shengxiang

160

Image processing meta-algorithm development via genetic manipulation of existing algorithm graphs  

NASA Astrophysics Data System (ADS)

Automatic algorithm generation for image processing applications is not a new idea, however previous work is either restricted to morphological operates or impractical. In this paper, we show recent research result in the development and use of meta-algorithms, i.e. algorithms which lead to new algorithms. Although the concept is generally applicable, the application domain in this work is restricted to image processing. The meta-algorithm concept described in this paper is based upon out work in dynamic algorithm. The paper first present the concept of dynamic algorithms which, on the basis of training and archived algorithmic experience embedded in an algorithm graph (AG), dynamically adjust the sequence of operations applied to the input image data. Each node in the tree-based representation of a dynamic algorithm with out degree greater than 2 is a decision node. At these nodes, the algorithm examines the input data and determines which path will most likely achieve the desired results. This is currently done using nearest-neighbor classification. The details of this implementation are shown. The constrained perturbation of existing algorithm graphs, coupled with a suitable search strategy, is one mechanism to achieve meta-algorithm an doffers rich potential for the discovery of new algorithms. In our work, a meta-algorithm autonomously generates new dynamic algorithm graphs via genetic recombination of existing algorithm graphs. The AG representation is well suited to this genetic-like perturbation, using a commonly- employed technique in artificial neural network synthesis, namely the blueprint representation of graphs. A number of exam. One of the principal limitations of our current approach is the need for significant human input in the learning phase. Efforts to overcome this limitation are discussed. Future research directions are indicated.

Schalkoff, Robert J.; Shaaban, Khaled M.

1999-07-01

161

Removing the Genetics from the Standard Genetic Algorithm  

Microsoft Academic Search

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

Shumeet Baluja; Rich Caruana

1995-01-01

162

Modeling and Analysis of Genetic Algorithms Using Neural Networks  

NASA Astrophysics Data System (ADS)

Vose's genetic algorithm model assuming an infinite population is useful for a theoretical analysis. However, it is generally difficult to know transitions of infinite populations. In this paper, we propose a method for modeling genetic algorithms for infinite populations by using neural networks. We use a neural network for estimating deterministic transitions of infinite populations from stochastic data obtained through observing a process of a genetic algorithm for finite populations. Then the trained network approximates a mapping (or a vector field) which characterizes the genetic algorithm. Our method introduces a framework for analyzing genetic algorithms from the viewpoint of neural networks. In this paper, we use a mixture-of-experts architecture for modeling and show that an optimization problem, which the genetic algorithm solves, is represented as a combination of some other optimization problems corresponding to expert networks.

Imai, Jun-ichi; Yoshikawa, Takeshi; Shioya, Hiroyuki; Da-te, Tsutomu

2002-09-01

163

A Genetic Algorithm for Designing Constellations with Low Error Floors  

E-print Network

and Electrical Engineering West Virginia UniveGenetic Algorithm March 21, 2008 1 / 21 #12;Outline 1 Introduction Department of Computer Science and Electrical Engineering West Virginia UniveGenetic Algorithm March 21, 2008 Valenti et al. ( Lane Department of Computer Science and Electrical Engineering West Virginia UniveGenetic

Valenti, Matthew C.

164

New approach to railway noise modeling employing Genetic Algorithms  

Microsoft Academic Search

Main goal of this paper was to describe an innovative method of noise prediction based on Genetic Algorithms. First part of the paper addresses the problem of growing noise, mainly in the context of a unified method for measuring noise. Further, Genetic Algorithms are described with regards to their fundamental features. Further a description is provided as to how Genetic

Ma?gorzata Szwarc; Andrzej Czy?ewski

2011-01-01

165

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

166

A New Genetic Algorithm for Set Covering Problems  

E-print Network

are subsequently ordered by the genetic algorithm. Fitness assignment is handled by the decoder, which transforms inherit good parts from old solutions. � Coding: Transformation such that genetic operators can be appliedA New Genetic Algorithm for Set Covering Problems Annual Operational Research Conference 42

Aickelin, Uwe

167

Genetic algorithm optimization applied to electromagnetics: a review  

Microsoft Academic Search

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

Daniel S. Weile; Eric Michielssen

1997-01-01

168

A modified decision tree algorithm based on genetic algorithm for mobile user classification problem.  

PubMed

In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389

Liu, Dong-sheng; Fan, Shu-jiang

2014-01-01

169

Genetic Algorithms for the Use in Combinatorial Problems  

Microsoft Academic Search

Turbo code interleaver optimization is a NP-hard combinatorial optimization problem attractive for its complexity and variety\\u000a of real world applications. In this paper, we investigate the usage and performance of recent variant of genetic algorithms,\\u000a higher level chromosome genetic algorithms, on the turbo code optimization task. The problem as well as higher level chromosome\\u000a genetic algorithms, that can be use

Václav Snásel; Jan Platos; Pavel Krömer; Nabil Ouddane

2009-01-01

170

A study of hybrid parallel genetic algorithm model  

Microsoft Academic Search

Genetic algorithms is facing the low evolution rate and difficulties to meet real-time requirements when handing large-scale combinatorial optimization problems. In this paper, we propose a coarse-grained-master-slave hybrid parallel genetic algorithm model based on multi-core cluster systems. This model integrates the message-passing model and the shared-memory model. We use message-passing model—MPI among nodes which correspond to coarse-grained Parallel Genetic Algorithm

Zhu-rong Wang; Tao Ju; Du-wu Cui; Xin-hong Hei

2011-01-01

171

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

172

Training product unit neural networks with genetic algorithms  

NASA Technical Reports Server (NTRS)

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

Janson, D. J.; Frenzel, J. F.; Thelen, D. C.

1991-01-01

173

Genetic algorithm-based optimization for cognitive radio networks  

Microsoft Academic Search

Genetic algorithms are well suited for optimization problems involving large search spaces. In this paper, we present several approaches designed to enhance the convergence time and\\/or improve the performance results of genetic algorithm-based search engine for cognitive radio networks, including techniques such as population adaptation, variable quantization, variable adaptation, and multi-objective genetic algorithms (MOGA). Note that the time required for

Si Chen; Timothy R. Newman; Joseph B. Evans; Alexander M. Wyglinski

2010-01-01

174

Cyclic Genetic Algorithm with Conditional Branching PredatorPrey Scenario  

E-print Network

generation, genetic algorithm, hexapod Introduction Evolving controllers autonomous legged robots reduce levels. CGAs were successfully past evolve single­loop robot cycles cycles hexapod robots area coverage

Parker, Gary B.

175

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] [Pennsylvania State Univ., University Park, PA (United States). Dept. of Electrical Engineering

1995-12-01

176

Application of a hybrid genetic algorithm to airline crew scheduling  

Microsoft Academic Search

This paper discusses the development and application of a hybrid genetic algorithmto airline crew scheduling problems. The hybrid algorithm consists of a steady-stategenetic algorithm and a local search heuristic. The hybrid algorithm was tested on a set of fortyreal-world problems. It found the optimal solution for half the problems, and good solutionsfor nine others. The results were compared to those

David Levine

1996-01-01

177

Escaping Hierarchical Traps with Competent Genetic Algorithms  

Microsoft Academic Search

To solve hierarchical problems, one must be able to learn the linkage, represent partial so- lutions eciently, and assure eective nich- ing. We propose the hierarchical Bayesian optimization algorithm which combines the Bayesian optimization algorithm, local struc- tures in Bayesian networks, and a powerful niching technique. Additionally, we propose a class of hierarchically decomposable prob- lems, called hierarchical traps, which

Martin Pelikan; E. Goldberg

2001-01-01

178

New decision tree based on genetic algorithm  

Microsoft Academic Search

The decision tree based on the k-means algorithm has recently been proposed. However, the drawback of the k-means algorithm is that the users must determine the number of branches for each node before the decision tree is designed. The users are usually hard to determine the number of branches for each node. In this study, the new decision tree with

Shiueng-Bien Yang; Shen-I Yang

2010-01-01

179

Offline Handwriting Recognition using Genetic Algorithm  

E-print Network

Handwriting Recognition enables a person to scribble something on a piece of paper and then convert it into text. If we look into the practical reality there are enumerable styles in which a character may be written. These styles can be self combined to generate more styles. Even if a small child knows the basic styles a character can be written, he would be able to recognize characters written in styles intermediate between them or formed by their mixture. This motivates the use of Genetic Algorithms for the problem. In order to prove this, we made a pool of images of characters. We converted them to graphs. The graph of every character was intermixed to generate styles intermediate between the styles of parent character. Character recognition involved the matching of the graph generated from the unknown character image with the graphs generated by mixing. Using this method we received an accuracy of 98.44%.

Kala, Rahul; Shukla, Anupam; Tiwari, Ritu

2010-01-01

180

PDE Nozzle Optimization Using a Genetic Algorithm  

NASA Technical Reports Server (NTRS)

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

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

2000-01-01

181

Energy Efficient Real-Time DVS based on Genetic Algorithm  

Microsoft Academic Search

This paper proposes a novel real-time dynamic voltage scheduling algorithm(GA-DVS) based on genetic algorithm for periodically real-time task set. Based on a mathematical system model in the real situation, the GA-DVS algorithm is different from classical DVS algorithms, some critical parts of which are specially designed, such as encoding, the fitness function, the crossover\\/mutation\\/repair operator and the termination condition; GA-DVS

Jin Jian Xun; Wang Huayong; Wun Nian; Wu Dexin; Wang Jian Fen

2008-01-01

182

A hybrid of the genetic algorithm and concurrent simplex  

E-print Network

the reason why it ran too slowly for our particular problem, and will touch upon some of the earlier attempts at faster ? converging hybrid genetic algorithms. Chapter III will propose a new hybrid algorithm which is better suited to speeding up... the convergence time on the metabolic modeling problem than past solutions. A. The Innards of the Genetic Algorithm A genetic algorithm is a kind of search; it can be put to most of the same problems to which breadth-first or depth-first searches are applied...

Randolph, David Ethan

2012-06-07

183

Impedance Measurements Using Genetic Algorithms and Multiharmonic Signals  

Microsoft Academic Search

In this paper, a procedure to measure impedances using data-acquisition boards and genetic algorithms is developed. This approach to impedance measurements has the advantage of being low cost. The multiharmonic acquired waveforms are characterized using a genetic algorithm that finds the frequency of the signal, which, in turn, is used in a multiple linear least-squares (LS) waveform-fitting algorithm. The magnitude

Fernando M. Janeiro; Pedro M. Ramos

2009-01-01

184

Further Research on Feature Selection and Classification Using Genetic Algorithms  

Microsoft Academic Search

. This paper summarizes work onan approach that combines feature selectionand data classification using Genetic Algorithms.First, it describes our use of GeneticAlgorithms combined with a K-nearestneighbor algorithm to optimize classificationby searching for an optimal feature weighting,essentially warping the feature spaceto coalesce individuals within groups andto separate groups from one another. Thisapproach has proven especially useful withlarge data sets where standard

William F. Punch III; Erik D. Goodman; Min Pei; Lai Chia-shun; Paul D. Hovland; Richard J. Enbody

1993-01-01

185

Using Genetic Algorithms to Optimize Operating System Parameters  

E-print Network

Using Genetic Algorithms to Optimize Operating System Parameters Dror G. Feitelson Michael Naaman files containing information about the local workload, and genetic algorithms are used to select of parame­ ters that can be modified by the system administrator in order to tune system performance

Feitelson, Dror

186

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

187

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

188

A Hybrid Genetic Algorithm for Routing Optimization in IP Networks  

E-print Network

engineering are demonstrated. Keywords ­ IP Traffic engineering, genetic algorithm, bandwidth-delay sensitiveA Hybrid Genetic Algorithm for Routing Optimization in IP Networks Utilizing Bandwidth and Delay traffic engineering, which relies on conventional, destination-based routing protocols. We introduce

Riedl, Anton

189

Niching genetic algorithms for optimization in electromagnetics. I. Fundamentals  

Microsoft Academic Search

Niching methods extend genetic algorithms and permit the investigation of multiple optimal solutions in the search space. In this paper, we review and discuss various strategies of niching for optimization in electromagnetics. Traditional mathematical problems and an electromagnetic benchmark are solved using niching genetic algorithms to show their interest in real world optimization

B. Sareni; L. Krahenbuhl; A. Nicolas

1998-01-01

190

Genetic Algorithm in Solution of Inverse Heat Conduction Problems  

Microsoft Academic Search

This report demonstrates the use of a genetic algorithm search in the solution of an inverse problem. The genetic algorithm is used to solve the one-dimensional inverse heat conduction problem using numerical data generated by solution of the corresponding direct problem. Both “pure” and noisy data are considered. If used with regularization, the method is shown to yield reasonable results

Miroslav Raudenský; Keith A. Woodbury; J. Kral; T. Brezina

1995-01-01

191

An Improved Immune Genetic Algorithm for Distribution Network Reconfiguration  

Microsoft Academic Search

On the basis of analyzing the insufficiency of genetic algorithm in the solution of distribution network reconfiguration, an improved immune genetic algorithm (IIGA) is proposed. The key of IIGA lies in the construction of vaccine pool and the design of immune operator. The vaccine pool can be created and updated automatically, and the immune operator consists of vaccination and immunoassay.

Chao-xue Wang; An-jun Zhao; Hui Dong; Zhi-jie Li

2009-01-01

192

Wavelet-Based Signal approximation with Genetic Algorithms  

Microsoft Academic Search

In this paper, the usability of genetic algorithms for signa l approximation is discussed. Due to recent developments in the field of signal approximation by wavelets, this work concentrates on signal approximation by wavelet-like functions. Signals are approximated by a finite linear combination of elementary functions and a genetic algorithm is employed to find the coefficients to such an approximation.

Marc M. Lankhorst; Marten D. Van Der Laan

1995-01-01

193

Genetic Algorithms as Global Random Search Methods: An Alternative Perspective  

Microsoft Academic Search

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be

Charles C. Peck; Atam P. Dhawan

1995-01-01

194

Critical heat flux function approximation using genetic algorithms  

Microsoft Academic Search

Function approximation is the problem of finding a system that best explains the relationship between input variables and an output variable. We propose two hybrid genetic algorithms (GAs) of parametric and nonparametric models for function approximation. The former GA is a genetic nonlinear Levenberg-Marquardt algorithm of parametric model. We designed the chromosomes in a way that geographically exploits the relationships

Yung-Keun Kwon; Byung-Ro Moon; Sung-Deok Hong

2005-01-01

195

A hybrid grouping genetic algorithm for bin packing  

Microsoft Academic Search

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

Emanuel Falkenauer

1996-01-01

196

A Pareto Frontier for Full Stern Submarines via Genetic Algorithm  

E-print Network

A Pareto Frontier for Full Stern Submarines via Genetic Algorithm by Mark W. Thomas B.S. Electrical of Philosophy in Hydrodynamics at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 1998 c flMark W. Thomas, 1998 Submarines via Genetic Algorithm by Mark W. Thomas Submitted to the Department of Ocean Engineering

Coello, Carlos A. Coello

197

CYCLIC GENETIC ALGORITHMS FOR THE LOCOMOTION OF HEXAPOD ROBOTS  

Microsoft Academic Search

Robotics control problems, such as gait coordination, require sequential solutions where a series of actions is continually repeated. Genetic Algorithms that do parameter optimization have not been widely applied to these cyclic sequential decision problems; although some form of evolutionary computation would be well suited for the adaptability required. In this paper we introduce Cyclic Genetic Algorithms, which were developed

GARY B. PARKER; GREGORY J. E. RAWLINS

1996-01-01

198

Technical Report No. 494 Using Cyclic Genetic Algorithms  

E-print Network

automata for a small hexapod robot are generated by a cyclic genetic algorithm. From these automata of the hexapod's ``nervous system'' is part of a general environment for experimentation with multi involves a form of genetic algorithm generating locomotion control in very simple hexapod agents. Frequent

Parker, Gary B.

199

CYCLIC GENETIC ALGORITHMS FOR EVOLVING MULTI-LOOP CONTROL PROGRAMS  

E-print Network

, genetic algorithms, sensors, learning, search, hexapod 1. INTRODUCTION The Cyclic Genetic Algorithm (CGA for hexapod gaits and area coverage path planning. However, in order for a robot to react properly to sensor walls and locate the desired target. The agent modeled in simulation is a hexapod robot equipped

Parker, Gary B.

200

The deterministic genetic algorithm: implementation details and some results  

Microsoft Academic Search

Recent literature on genetic algorithms provides a controversial discussion on the efficiency of this particular class of randomized optimization procedures; despite several encouraging empirical results, recent theoretical analyses have argued that in most cases, the runtime behavior of genetic algorithms is increased by at least a factor of ln(n) with n denoting the number of parameters to be optimized. It

Ralf Salomon

1999-01-01

201

Discovery of maximal distance codes using genetic algorithms  

Microsoft Academic Search

An application of genetic algorithms to the problem of discovering communication codes with properties useful for error corrections is described. Search spaces for these codes are so large as to rule out any exhaustive search strategy. Coding theory provides a rich and interesting domain for genetic algorithms. There are some coding problems about which a lot is known and good

Kejitan Dontas; Kenneth De Jong

1990-01-01

202

A parallel genetic algorithm for the set partitioning problem  

Microsoft Academic Search

In this dissertation the author reports on his efforts to develop a parallel genetic algorithm and apply it to the solution of set partitioning problem -- a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. He developed a distributed steady-state genetic algorithm in conjunction with a specialized local search heuristic for solving

David Levine

1994-01-01

203

Two new robust genetic algorithms for the flowshop scheduling problem  

Microsoft Academic Search

The flowshop scheduling problem (FSP) has been widely studied in the literature and many techniques for its solution have been proposed. Some authors have concluded that genetic algorithms are not suitable for this hard, combinatorial problem unless hybridization is used. This work proposes new genetic algorithms for solving the permutation FSP that prove to be competitive when compared to many

Rubén Ruiz; Concepción Maroto; Javier Alcaraz

2006-01-01

204

Quadratic Approximation-Based Coordinate Change in Genetic Algorithms  

Microsoft Academic Search

This paper proposes a procedure for space coordinate change, inside genetic algorithms, based on convex quadratic approximations of the general nonlinear objective function. It is shown that in the transformed coordinates the genetic algorithm is able to And the problem optimum in less iterations and with greater proportion of successful attempts. The proposed procedure employs only the objective function samples

Elizabeth F. Wanner; Frederico G. Guimaraes; Ricardo H. C. Takahashi; Peter J. Fleming

2006-01-01

205

Cell planning using genetic algorithm and tabu search  

Microsoft Academic Search

This paper investigates the performance of the genetic algorithm and tabu search in solving the optimization problem of base station location. Optimization refers to maximizing radio coverage while minimizing equipment and maintenance costs. A comparative analysis of both the genetic algorithm and tabu search was undertaken. The effects of changing the population size as well as the type of selection

Rodney S. Rambally; Avinash Maharajh

2009-01-01

206

Reducing internal fragmentation in segregated free lists using genetic algorithms  

Microsoft Academic Search

In this paper we present an approach for improving memory efficiency using genetic algorithms. More precisely, we improve the internal memory fragmentation by finding the optimal configuration of a segregated free lists data structure. We have used trace instrumentation to generate the workload of memory allocations and deallocations from significant scenarios.The genetic algorithm used the workload as input to generate

Christian Del Rosso

2006-01-01

207

Viscous single and multicomponent airfoil design with genetic algorithms  

Microsoft Academic Search

An optimization procedure aimed at the design of multicomponent airfoils for high-lift applications is described. The procedure is based on a multiobjective genetic algorithm; two flow solvers have been coupled with the genetic algorithm: a viscous–inviscid interaction method, based on an Euler flow solver and an integral boundary layer routine, and a method based on a full potential flow solver.

D Quagliarella; A Vicini

2001-01-01

208

Designing highlift airfoils using genetic algorithms D. Quagliarella, A. Vicini  

E-print Network

Designing high­lift airfoils using genetic algorithms D. Quagliarella, A. Vicini C.I.R.A., Centro layer flow field solver capable of working in high­lift conditions. Examples of unconstrained of multi­component airfoils for high lift applications, based on a multi­objective genetic algorithm

Coello, Carlos A. Coello

209

GAPRUS -GENETIC ALGORITHMS BASED PIPE ROUTING USING TESSELLATED OBJECTS  

E-print Network

and sometimes discontinuous problems like pipe routing. Besides, due to limitations in the representation of 3D problems involving 3D freeform obstacles is demonstrated. Key words: Pipe Routing, Genetic AlgorithmsGAPRUS - GENETIC ALGORITHMS BASED PIPE ROUTING USING TESSELLATED OBJECTS Sunand Sandurkar Software

Chen, Wei

210

Aerosol Layer Discrimination using Laser Radar and Genetic Algorithms  

Microsoft Academic Search

A technique has been developed to retrieve the height of the top of the aerosol layer from Micro Pulse Lidar (MPL) datasets. The technique combines first derivative estimates of normalized relative backscatter profiles with genetic algorithm refinements. The genetic algorithm is used to explore the gradient profiles to produce temporally coherent results. I. INTRODUCTION The distribution of aerosols over altitude

Jo Ann Parikh; Nimmi C. Parikh Sharma

2006-01-01

211

Performance Improvement of Genetic Algorithms by Adaptive Grid Workflows  

Microsoft Academic Search

In this paper we present improvement of the performance of Grid Direct Acyclic Graph (DAG) workflow genetic algorithm by harnessing the power of High Level Petri-Nets workflow model. Genetic Algorithms are very powerful optimization technique that is easily parallelized using different approaches which makes it ideal for the Grid. The High Level Petri-Net workflow model greatly outperforms currently available DAG

Boro Jakimovski; Dragan Sahpaski; Goran Velinov

2009-01-01

212

Genetic algorithm for text clustering based on latent semantic indexing  

Microsoft Academic Search

In this paper, we develop a genetic algorithm method based on a latent semantic model (GAL) for text clustering. The main difficulty in the application of genetic algorithms (GAs) for document clustering is thousands or even tens of thousands of dimensions in feature space which is typical for textual data. Because the most straightforward and popular approach represents texts with

Wei Song; Soon Cheol Park

2009-01-01

213

Darwinian Evolution in Parallel Universes: A Parallel Genetic Algorithm for  

E-print Network

Darwinian Evolution in Parallel Universes: A Parallel Genetic Algorithm for Variable Selection Mu outcome of interest commonly arises in various industrial engineering applications. The genetic algorithm modification. Our idea is to run a number of GAs in parallel without allowing each GA to fully converge

Zhu, Mu

214

Automatic Tuning of Agent-Based Models Using Genetic Algorithms  

E-print Network

Automatic Tuning of Agent-Based Models Using Genetic Algorithms Beno^it Calvez and Guillaume lead to a radical modification of the dynamics of the whole system. The development and the parameter on suggesting the use of genetic algorithms. The idea is to capture in the fitness func- tion the goal

Paris-Sud XI, Université de

215

Using Genetic Algorithms for Supervised Concept Learning WILLIAM M. SPEARS  

E-print Network

irregularities. The two language forms generally used are decision trees 17 and rules 15 . Another importantUsing Genetic Algorithms for Supervised Concept Learning WILLIAM M. SPEARS Navy Center for Applied Science Department George Mason University Fairfax, VA 22030, USA ABSTRACT Genetic Algorithms (GAs) have

216

A COMPARISON OF GENETIC ALGORITHMS AND OTHER MACHINE LEARNING SYSTEMS  

E-print Network

machine learning approach investigated in this research is genetic algorithms (GA's); decision trees with the common­disease research task, although decision trees also demonstrated certain strengths. AutoclassA COMPARISON OF GENETIC ALGORITHMS AND OTHER MACHINE LEARNING SYSTEMS ON A COMPLEX CLASSIFICATION

Congdon, Clare Bates

217

Study on the combination of genetic algorithms and ant Colony algorithms for solving fuzzy job shop scheduling problems  

Microsoft Academic Search

by using a single algorithm to deal with fuzzy job shop scheduling problems, it is difficult to get a satisfied solution. In this paper we propose a combined strategy of algorithms to solve fuzzy job shop scheduling problems. This startegy adopts genetic algorithms and ant colony algorithms as a parallel asynchronous search algorithm. In addition, according to the characteristics of

Xiaoyu Song; Yunlong Zhu; Chaowan Yin; Fuming Li

2006-01-01

218

Study on the combination of genetic algorithms and ant Colony algorithms for solving fuzzy job shop scheduling problems  

Microsoft Academic Search

By using a single algorithm to deal with fuzzy job shop scheduling problems, it is difficult to get a satisfied solution. In this paper we propose a combined strategy of algorithms to solve fuzzy job shop scheduling problems. This strategy adopts genetic algorithms and ant colony algorithms as a parallel asynchronous search algorithm. In addition, according to the characteristics of

Xiaoyu Song; Yunlong Zhu; Chaowan Yin; Fuming Li

2006-01-01

219

Video scene retrieval with interactive genetic algorithm  

Microsoft Academic Search

This paper proposes a video scene retrieval algorithm based on emotion. First, abrupt\\/gradual shot boundaries are detected in the video clip of representing a specific story. Then, five video features such as \\

Hun-woo Yoo; Sung-bae Cho

2007-01-01

220

Higher-Order Quantum-Inspired Genetic Algorithms  

E-print Network

This paper presents a theory and an empirical evaluation of Higher-Order Quantum-Inspired Genetic Algorithms. Fundamental notions of the theory have been introduced, and a novel Order-2 Quantum-Inspired Genetic Algorithm (QIGA2) has been presented. Contrary to all QIGA algorithms which represent quantum genes as independent qubits, in higher-order QIGAs quantum registers are used to represent genes strings which allows modelling of genes relations using quantum phenomena. Performance comparison has been conducted on a benchmark of 20 deceptive combinatorial optimization problems. It has been presented that using higher quantum orders is beneficial for genetic algorithm efficiency, and the new QIGA2 algorithm outperforms the old QIGA algorithm which was tuned in highly compute intensive metaoptimization process.

Robert Nowotniak; Jacek Kucharski

2014-07-02

221

Closed Loop System Identification with Genetic Algorithms  

NASA Technical Reports Server (NTRS)

High performance control design for a flexible space structure is challenging since high fidelity plant models are di.cult to obtain a priori. Uncertainty in the control design models typically require a very robust, low performance control design which must be tuned on-orbit to achieve the required performance. Closed loop system identi.cation is often required to obtain a multivariable open loop plant model based on closed-loop response data. In order to provide an accurate initial plant model to guarantee convergence for standard local optimization methods, this paper presents a global parameter optimization method using genetic algorithms. A minimal representation of the state space dynamics is employed to mitigate the non-uniqueness and over-parameterization of general state space realizations. This control-relevant system identi.cation procedure stresses the joint nature of the system identi.cation and control design problem by seeking to obtain a model that minimizes the di.erence between the predicted and actual closed-loop performance.

Whorton, Mark S.

2004-01-01

222

Casting riser design optimization using genetic algorithms  

SciTech Connect

The design of rigging systems for castings in the foundry is largely based on past experience and empirical rules. Recent literature shows that attempts are being made to adopt a more scientific approach towards rigging design (location and size of risers, proper orientation of the casting, determination of parting plane, etc.) through the use of rule-based expert systems, process simulation and other tools. Riser design is a key element in the optimization of the overall rigging system since riser designs with large safety margins reduce yield and increase cost. This paper describes a methodology to optimize the riser design. A genetic algorithm is used for simplicity as well as robustness. Values of selected riser design parameters are examined using a modulus based approach to optimize the riser yield while achieving functional performance (i.e. effectiveness of the riser to adequately feed regions where shrinkage-type defects have a tendency of forming). Since the optimization is carried out on the solid model of the riser, the resultant design of the riser, together with the casting can be directly sent to a rapid prototyping system for production of a pattern.

Guleyupoglu, S.; Upadhya, G.; Paul, A.J.; Yu, K.O. [Concurrent Technologies Corp., Johnstown, PA (United States); Hill, J.L. [Univ. of Alabama, Tuscaloosa, AL (United States). Engineering Science and Mechanics Dept.

1995-12-31

223

Robot path planning using a genetic algorithm  

NASA Technical Reports Server (NTRS)

Robot path planning can refer either to a mobile vehicle such as a Mars Rover, or to an end effector on an arm moving through a cluttered workspace. In both instances there may exist many solutions, some of which are better than others, either in terms of distance traversed, energy expended, or joint angle or reach capabilities. A path planning program has been developed based upon a genetic algorithm. This program assumes global knowledge of the terrain or workspace, and provides a family of good paths between the initial and final points. Initially, a set of valid random paths are constructed. Successive generations of valid paths are obtained using one of several possible reproduction strategies similar to those found in biological communities. A fitness function is defined to describe the goodness of the path, in this case including length, slope, and obstacle avoidance considerations. It was found that with some reproduction strategies, the average value of the fitness function improved for successive generations, and that by saving the best paths of each generation, one could quite rapidly obtain a collection of good candidate solutions.

Cleghorn, Timothy F.; Baffes, Paul T.; Wang, Liu

1988-01-01

224

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

225

Sub-population policies for a parallel multiobjective genetic algorithm with applications to wing design  

E-print Network

Sub-population policies for a parallel multiobjective genetic algorithm with applications to wing. 2. GENETIC ALGORITHM WITH SUB-POPULATIONS The algorithm here described, that will be called Virtual Sub- population Genetic Algorithm (VSGA), is an extension of the multi-objective genetic algorithm

Coello, Carlos A. Coello

226

Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous  

NASA Technical Reports Server (NTRS)

The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.

Karr, C. L.; Freeman, L. M.; Meredith, D. L.

1990-01-01

227

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization  

NASA Technical Reports Server (NTRS)

A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Holst, Terry L.

2005-01-01

228

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization  

NASA Technical Reports Server (NTRS)

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Holst, Terry L.

2004-01-01

229

Learning Linear Operators by Genetic Algorithms  

Microsoft Academic Search

In this paper we consider the situation where we do not know a linear operator but instead have only a set of example functional points of the form such that . This problem can be analysed from the viewpoint of numerical linear algebra or learning algorithms. The later is the focus of this work. Firstly, we present a method found

JEAN FABER; R ICARDO N. THESS; G ILSON A. GIRALDI

230

Genetic algorithm optimization of feedback control systems  

Microsoft Academic Search

In this paper we are concerned with Smart Materials that contain many actuators and sensors along with digital signal processing electronics that allow for the implementation of a control algorithm. Smart Materials have been proposed for the active control of sound from a vibrating structure. Here we investigate the design of structural control systems for these Smart Structures for noise

Douglas K. Lindner; Gregory A. Zvonar; George C. Kirby; Grant M. Emery

1996-01-01

231

Direct DCT indexing using genetic algorithm concepts  

Microsoft Academic Search

It is highly desirable in terms of speed and computational costs to perform image indexing and retrieval in the compressed domain. The exponential growth of digital media on both the WWW (Reddy and Fletcher, 1998) and home imaging equipment has prompted the development of faster, more accurate indexing algorithms. Successful techniques have the ability to summarise the features of an

A. Armstrong; J. Jiang

2002-01-01

232

Using genetic algorithms for developing amorphous silicon atomistic model  

NASA Astrophysics Data System (ADS)

In this paper, the author presents a computer algorithm for the generation of a high-quality continuous random networks using a genetic algorithm (GA). Genetic algorithms are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence. As a one can guess, genetic algorithms are inspired by Darwin's theory about evolution. Simply said, solution to a problem solved by genetic algorithms is evolved. This paper formulates the amorphous silicon atomistic model problem such that a genetic algorithm can be designed to solve it. A population of models are generated randomally at the start. A sequence of genetic processes such as individuals regeneration, feature cross-over and mutation are performed to produce new generations of the models. After many generations the optimal solution is reached. A series of computer simulations are used to predict many of the structural and electronic properties of the amorphous silicon. The results are compared with the experimental values for these physical parameters mentioned in the literature for testing the model accuracy. Also, a comparison between the suggested model and the other famous computer-based algorithms is presented. The results are discussed.

El-Hefnawy, Somia M.

2003-07-01

233

Evolving homeostatic tissue using genetic algorithms.  

PubMed

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. PMID:21419156

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

2011-08-01

234

Decomposition and immune genetic algorithm for scheduling large job shops  

Microsoft Academic Search

A decomposition and optimization algorithm is presented for large-scale job shop scheduling problems in which the total weighted tardiness must be minimized. In each iteration, a new subproblem is first defined by a heuristic approach and then solved using a genetic algorithm. We construct a fuzzy controller to calculate the characteristic values which describe the the bottleneck jobs in different

Rui Zhang; Cheng Wu

2008-01-01

235

Application of Genetic Algorithm in Architectural Conceptual Design  

Microsoft Academic Search

A genetic algorithm for supporting architectural conceptual design is presented in this paper. The algorithm adopts mathematical expression binary tree based coded approach, corresponding crossover and mutation operations, and the combination of objective function and interaction with designers for getting fitness values to generate simple curves. The selected shapes are dealt with via 3D visualizing technology to form entities. These

LIU Hong; LI Yan

2006-01-01

236

An Improved Genetic Algorithm for Reactive Power Optimization  

Microsoft Academic Search

In this paper, an Improve Genetic Algorithm (IGA) is applied to solve reactive power optimization (RPO) problem. The PRO problem is a highly nonlinear complex optimization problem and can be solved by enumeration method if without the advantages of evolutionary algorithms. The IGA modifies chromosomes based on the fundamentals of virtual population method. Stochastic crossover schemes are also employed in

Guang Ya Yang; Zhao Yang Dong

237

CYCLIC GENETIC ALGORITHMS FOR EVOLVING MULTILOOP CONTROL PROGRAMS  

E-print Network

algorithms, sensors, learning, search, hexapod 1. INTRODUCTION Cyclic Genetic Algorithm (CGA), a variant of traditional successfully been used evolve single loop control programs hexapod gaits and area coverage path modeled simulation a hexapod robot equipped with sensors. The task of learning search behavior autonomous

Parker, Gary B.

238

Introduction to Genetic Algorithms Peter G. Anderson, Computer Science Department  

E-print Network

#12;1 Introduction to Genetic Algorithms Peter G. Anderson, Computer Science Department Rochester, selective breeding, "survival of the fittest." We will present the fundamental algorithms and present Computers To... Synchronize like the fireflies. Discover FSMs given the sentences. Control robots. 2000/05/0

Anderson, Peter G.

239

Method of mechanism synthesis by hybrid genetic algorithm  

E-print Network

algorithm was utilized to search for the global minima. A benefit of the genetic algorithm method is that in this search for the global minima it will also locate a family of alternative solutions that possess near optimal outputs. Having these alternative...

O'Neil, Robert Anthony

2012-06-07

240

A novel algorithm combining finite state method and genetic algorithm for solving crude oil scheduling problem.  

PubMed

A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method. PMID:24772031

Duan, Qian-Qian; Yang, Gen-Ke; Pan, Chang-Chun

2014-01-01

241

A Novel Algorithm Combining Finite State Method and Genetic Algorithm for Solving Crude Oil Scheduling Problem  

PubMed Central

A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method. PMID:24772031

Duan, Qian-Qian; Yang, Gen-Ke; Pan, Chang-Chun

2014-01-01

242

Genetic Algorithm Based Damage Control For Shipboard Power Systems  

E-print Network

Power system level. The proposed method used a constrained binary genetic algorithm to find an optimal network configuration. An optimal network configuration is a configuration which restores all of the de-energized loads that are possible...

Amba, Tushar

2010-07-14

243

Variable ordering optimization of ROBDD using genetic algorithm  

E-print Network

for large-sized Boolean functions because of the hugh computation time and lack of computer memory. The above mentioned weaknesses can be overcome by simplifying the procedure of genetic algorithm, improving construction methods of ROBDD, and developing new...

Ha, Chunghun

2012-06-07

244

Mobile transporter path planning using a genetic algorithm approach  

NASA Technical Reports Server (NTRS)

The use of an optimization technique known as a genetic algorithm for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the Space Station which must be able to reach any point of the structure autonomously. Specific elements of the genetic algorithm are explored in both a theoretical and experimental sense. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research. However, trajectory planning problems are common in space systems and the genetic algorithm provides an attractive alternative to the classical techniques used to solve these problems.

Baffes, Paul; Wang, Lui

1988-01-01

245

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

246

FPGA implementation of four-step genetic search algorithm  

Microsoft Academic Search

Genetic algorithm (GA) has been applied to block matching algorithm (BMA) and demonstrates positively its capability in BMA. Four-step genetic search (4GS) has been proposed. The mean square error (MSE) performance of 4GS is close to FS. The computational cost of 4GS is close to the well known three-step search (3SS). Realization of 4GS can be applied in video encoding

Man F. So; Angus Wu

1999-01-01

247

Hardware implementation of four-step genetic search algorithm  

Microsoft Academic Search

Genetic Algorithm (GA) has been applied to Block Matching Algorithm (BMA) and demonstrates positively its capability in BMA. Four-step genetic search (4GS) has been proposed recently (So and Wu, 1998). The mean square error (MSE) performance of 4GS is close to FS. The computational cost of 4GS is close to the well known three-step search (3SS). Realization of 4GS can

Man F. So; Angus Wu

1999-01-01

248

Parameters optimization on DHSVM model based on a genetic algorithm  

Microsoft Academic Search

Due to the multiplicity of factors including weather, the underlying surface and human activities, the complexity of parameter\\u000a optimization for a distributed hydrological model of a watershed land surface goes far beyond the capability of traditional\\u000a optimization methods. The genetic algorithm is a new attempt to find a solution to this problem. A genetic algorithm design\\u000a on the Distributed-Hydrology-Soil-Vegetation model

Changqing Yao; Zhifeng Yang

2009-01-01

249

Estimation of evolvability genetic algorithm and dynamic environments  

Microsoft Academic Search

This article investigates the of applicability of adding evolvability promoting mechanisms to a genetic algorithm to enhance\\u000a its ability to handle perpetually novel dynamic environments, especially one that has stationary periods allowing the Genetic\\u000a Algorithm (GA) to converge on a temporary global optimum. We utilize both biological and evolutionary computation (EC) definitions\\u000a of evolvability to create two measures: one based

Yao Wang; Mark Wineberg

2006-01-01

250

Circuit Tolerance Design Using an Improved Genetic Algorithm  

Microsoft Academic Search

In this paper, we present an improved genetic algorithm to solve the worst-case circuit tolerance design problem, which has many design parameters and constraints. The evolutionary design approach, which is called a quality-engineering-based genetic algorithm (QEGA), with a penalty function is proposed for solving the constrained optimization problem. The QEGA approach is able to explore a wide design parameter space

Jinn-tsong Tsai; Jyh-horng Chou

2006-01-01

251

Genetic-Algorithm Tool For Search And Optimization  

NASA Technical Reports Server (NTRS)

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

Wang, Lui; Bayer, Steven

1995-01-01

252

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

253

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

254

Optimization of Radar Absorber Structures Using Genetic Algorithms  

Microsoft Academic Search

In this paper, a real-valued genetic algorithm (GA) is implemented to construct Radar Absorbing Materials RAM by searching\\u000a the characteristics (thickness T, permittivity?, permeability? and conductivity ?) which ensure the minimization of the reflectivity\\u000a on a frequency band. The genetic algorithms used the reflectivity in fitness function to direct the research to the best configuration.\\u000a Here in, we dealt with

Nadia Lassouaoui; Habiba Hafdallah Ouslimani; Alain Priou

255

Improved Reservoir Operation Using Hybrid Genetic Algorithm and Neurofuzzy Computing  

Microsoft Academic Search

A hybrid genetic and neurofuzzy computing algorithm was developed to enhance efficiency of water management for a multipurpose\\u000a reservoir system. The genetic algorithm was applied to search for the optimal input combination of a neurofuzzy system. The\\u000a optimal model structure is modified using the selection index (SI) criterion expressed as the weighted combination of normalized\\u000a values of root mean square

Panuwat Pinthong; Ashim Das Gupta; Mukand Singh Babel; Sutat Weesakul

2009-01-01

256

Using Genetic Algorithms to optimize ACS-TSP Marcin L. Pilat and Tony White  

E-print Network

-TSP algorithm as a Genetic Algorithm modification to ACS-TSP. The algorithm uses a GA to evolve a populationUsing Genetic Algorithms to optimize ACS-TSP Marcin L. Pilat and Tony White School of Computer,arpwhite}@scs.carleton.ca Abstract. We propose the addition of Genetic Algorithms to Ant Colony System (ACS) applied to improve

White, Tony

257

A theoretical investigation of a parallel genetic algorithm  

SciTech Connect

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 communicating sequential genetic algorithms. This paper includes an investigation of the theoretical allocation of trials to schemata by PGA's in general and by a particular PGA (which performs communication in a uniformly random manner) along with an experimental validation of an assumption made in the theoretical investigation. 17 refs., 4 figs.

Pettey, C.C.; Leuze, M.R.

1989-01-01

258

A parametric building energy cost optimization tool based on a genetic algorithm  

E-print Network

that the overall building energy cost is minimized. A metaheuristic: genetic algorithm was identified as the solution algorithm and was implemented in the problem under study. Through two case studies, the impacts of the three genetic algorithm parameters, namely...

Tan, Xiaowei

2007-09-17

259

A Hybrid Genetic Algorithm to Solve Zero-One Knapsack Problem  

Microsoft Academic Search

\\u000a In order to overcome the disadvantages of the traditional genetic algorithm and improve the speed and precision of the algorithm,\\u000a the author improved the selection strategy, integrated the greedy algorithm with the genetic algorithm and formed the hybrid\\u000a genetic algorithm. The paper discussed the basic idea and method to solve the zero-one knapsack problem using this hybrid\\u000a genetic algorithm. The

Qing Chen; Yuxiang Shao

260

Multiobjective Genetic Algorithm for Three-Phase PWM Inverter in Microturbines  

Microsoft Academic Search

In this article, authors present a new design method for PWM inverters in microturbines by using a multiobjective genetic algorithm. More precisely, the design problem is converted to an equivalent optimization problem, and then a multiobjective genetic algorithm is adopted to find a solution. The genetic algorithm is proposed to design a fuzzy controller. In this genetic algorithm approach, an

F. Jurado; M. Valverde

2005-01-01

261

Genetic algorithms for optimal design of underground reinforced concrete tube structure  

Microsoft Academic Search

Applying genetic algorithms to optimal design of underground reinforced concrete tube structure, the author develops the optimal model of structural design based on genetic algorithms for underground reinforced concrete tube. An example of the reinforced concrete tube structure is calculated by the proposed computer programs based on genetic algorithms optimal model, and the result indicates that using genetic algorithms for

Sheng-Li Zhao; Min-Qiang Li; Ji-Song Kou; Yan Liu

2004-01-01

262

Algorithmic aspects of genetic sequences and relative Kolmogorov complexity  

E-print Network

Algorithmic aspects of genetic sequences and relative Kolmogorov complexity Alla Grigorieva St/~dima Abstract In this paper we use the fundamental de#12;nition of the relative Kolmogorov com- plexity of #12;nite objects for genetic sequences. We investigate di#11;erent types of regularities of such sequences

Grigoriev, Dima

263

Feature Selection Methods: Genetic Algorithms vs. Greedylike Search  

E-print Network

Feature Selection Methods: Genetic Algorithms vs. Greedy­like Search Haleh Vafaie and Ibrahim F, especially in artificial intelligence. The main issues in developing feature selection techniques feature selection methods, the Importance Score (IS) which is based on a greedy­like search and a genetic

George Mason University

264

Use of a genetic algorithm for compact stellarator coil design  

E-print Network

Use of a genetic algorithm for compact stellarator coil design W.H. Miner, Jr., P.M. Valanju Fusion than those obtained with traditional methods. A new coil design procedure which uses a genetic transform at the magnetic axis and the plasma edge (global shear), a measure of the degree of quasi

265

Genetic programming can be used to automatically discover algorithms for  

E-print Network

of larger devices is in progress. Current experimental quantum computing hardware is based on the use of ionABSTRACT Genetic programming can be used to automatically discover algorithms for quantum computers of genetic programming to quantum computation and vice versa. 1. Quantum Computing Quantum computers

Spector, Lee

266

The Rank-scaled Mutation Rate for Genetic Algorithms  

Microsoft Academic Search

A novel method of individual level adaptive mutation rate control called the rank-scaled mutation rate for genetic algo- rithms is introduced. The rank-scaled mutation rate controlled genetic algorithm varies the mutation parameters based on the rank of each individual within the population. Thereby the distribution of the fitness of the papulation is taken into consideration in forming the new mutation

Mike Sewell; Jagath Samarabandu; Ranga Rodrigo; Kenneth McIsaac

267

CYCLIC GENETIC ALGORITHMS FOR THE LOCOMOTION OF HEXAPOD ROBOTS  

E-print Network

CYCLIC GENETIC ALGORITHMS FOR THE LOCOMOTION OF HEXAPOD ROBOTS GARY B. PARKER and GREGORY J. E, adaptive gait development for hexapod robots, was the impetus for this new kind of evolutionary computation, but it can be applied to other robotics domains. KEYWORDS: genetic, evolutionary, robot, hexapod, gait

Parker, Gary B.

268

EVOLVING HEXAPOD GAITS USING A CYCLIC GENETIC ALGORITHM  

E-print Network

EVOLVING HEXAPOD GAITS USING A CYCLIC GENETIC ALGORITHM GARY B. PARKER, DAVID W. BRAUN, AND INGO is an integral part of a legged robot control. Hexapod gaits require the coordination of the simultaneous. KEYWORDS: genetic, cyclic, robot, hexapod, control. INTRODUCTION Autonomous hexapod robots can be useful

Parker, Gary B.

269

A parallel genetic algorithm for the set partitioning problem  

SciTech Connect

In this dissertation the author reports on his efforts to develop a parallel genetic algorithm and apply it to the solution of set partitioning problem -- a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. He developed a distributed steady-state genetic algorithm in conjunction with a specialized local search heuristic for solving the set partitioning problem. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. The authors found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulation found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, high-quality integer feasible solutions were found for problems with 36,699 and 43,749 integer variables, respectively. A notable limitation they found was the difficulty solving problems with many constraints.

Levine, D. [Argonne National Lab., IL (United States). Mathematics and Computer Science Division.

1994-05-01

270

Differential Evolution and Genetic Algorithms for the Linear Ordering Problem  

Microsoft Academic Search

Linear ordering problem (LOP) is a well know NP-hard optimization problem attractive for its complexity, rich collection of\\u000a testing data and variety of real world applications. It is also a popular benchmark for novel optimization and metaheuristic\\u000a algorithms. In this paper, we compare the performance of genetic algorithms and differential evolution as efficient metaheuristic\\u000a solvers of the LOP.

Václav Snásel; Pavel Krömer; Jan Platos

2009-01-01

271

A simple genetic algorithm for multiple sequence alignment  

Microsoft Academic Search

ABSTRACT. Multiple sequence,alignment plays an important,role in molecular sequence,analysis. An alignment is the arrangement,of two (pairwise alignment) or more,(multiple alignment) sequences of ‘residues’ (nucleotides or amino,acids) that maximizes,the similarities between them. Algorithmically, the problem consists of opening and extending gaps in the sequences,to maximize,an objective function (measurement of similarity). A simple genetic algorithm was developed and implemented in the software

C. Gondro; B. P. Kinghorn

2007-01-01

272

Use of a genetic algorithm to analyze robust stability problems  

SciTech Connect

This note resents a genetic algorithm technique for testing the stability of a characteristic polynomial whose coefficients are functions of unknown but bounded parameters. This technique is fast and can handle a large number of parametric uncertainties. We also use this method to determine robust stability margins for uncertain polynomials. Several benchmark examples are included to illustrate the two uses of the algorithm. 27 refs., 4 figs.

Murdock, T.M.; Schmitendorf, W.E.; Forrest, S.

1990-01-01

273

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

2006-01-01

274

A comparative study of Monte Carlo simple genetic algorithm and noisy genetic algorithm for cost-effective sampling network design under uncertainty  

Microsoft Academic Search

This study evaluates and compares two methodologies, Monte Carlo simple genetic algorithm (MCSGA) and noisy genetic algorithm (NGA), for cost-effective sampling network design in the presence of uncertainties in the hydraulic conductivity (K) field. Both methodologies couple a genetic algorithm (GA) with a numerical flow and transport simulator and a global plume estimator to identify the optimal sampling network for

Jianfeng Wu; Chunmiao Zheng; Calvin C. Chien; Li Zheng

2006-01-01

275

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

276

INVARIANT SUBSETS OF THE SEARCH SPACE AND THE UNIVERSALITY OF A GENERALIZED GENETIC ALGORITHM  

Microsoft Academic Search

In this paper we shall give a mathematical description of a gen- eral evolutionary heuristic search algorithm which allows to see a very special property which slightly generalized binary genetic algorithms have compar- ing to other evolutionary computation techniques. It turns out that such a generalized genetic algorithm, which we call a binary semi-genetic algorithm, is capable of encoding virtually

BORIS MITAVSKIY

277

Membrane structure active flatness control using genetic algorithm with online objective reweighting  

Microsoft Academic Search

This paper presents experimental studies on active flatness control of a membrane structure using genetic algorithm (GA). Different from the previous algorithms with a single objective function, a genetic algorithm with online objective reweighting capability is proposed here. This genetic algorithm implements an objective function that re-weights its objective online and the objective function is the flatness plus tension. The

Ryan Orszulik; Jinjun Shan; Michael Stachowsky

2011-01-01

278

A simultaneous parameter adaptation scheme for genetic algorithms with application to phased array synthesis  

Microsoft Academic Search

Genetic algorithms are commonly used to solve many optimization and synthesis problems. An important issue facing the user is the selection of genetic algorithm parameters, such as mutation rate, mutation range, and number of crossovers. This paper demonstrates a real-valued genetic algorithm that simultaneously adapts several such parameters during the optimization process. This adaptive algorithm is shown to outperform its

Daniel W. Boeringer; Douglas H. Werner; David W. Machuga

2005-01-01

279

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

280

Genetic algorithms and the search for viable string vacua  

NASA Astrophysics Data System (ADS)

Genetic Algorithms are introduced as a search method for finding string vacua with viable phenomenological properties. It is shown, by testing them against a class of Free Fermionic models, that they are orders of magnitude more efficient than a randomised search. As an example, three generation, exophobic, Pati-Salam models with a top Yukawa occur once in every 1010 models, and yet a Genetic Algorithm can find them after constructing only 105 examples. Such non-deterministic search methods may be the only means to search for Standard Model string vacua with detailed phenomenological requirements.

Abel, Steven; Rizos, John

2014-08-01

281

Genetic Algorithms and the Search for Viable String Vacua  

E-print Network

Genetic Algorithms are introduced as a search method for finding string vacua with viable phenomenological properties. It is shown, by testing them against a class of Free Fermionic models, that they are orders of magnitude more efficient than a randomised search. As an example, three generation, exophobic, Pati-Salam models with a top Yukawa occur once in every 10^{10} models, and yet a Genetic Algorithm can find them after constructing only 10^5 examples. Such non-deterministic search methods may be the only means to search for Standard Model string vacua with detailed phenomenological requirements.

Steven Abel; John Rizos

2014-04-29

282

Genetic Algorithms and the Search for Viable String Vacua  

E-print Network

Genetic Algorithms are introduced as a search method for finding string vacua with viable phenomenological properties. It is shown, by testing them against a class of Free Fermionic models, that they are orders of magnitude more efficient than a randomised search. As an example, three generation, exophobic, Pati-Salam models with a top Yukawa occur once in every 10^{10} models, and yet a Genetic Algorithm can find them after constructing only 10^5 examples. Such non-deterministic search methods may be the only means to search for Standard Model string vacua with detailed phenomenological requirements.

Abel, Steven

2014-01-01

283

Application of Genetic Algorithm to Hexagon-Based Motion Estimation  

PubMed Central

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

Cheng, Wan-Shu

2014-01-01

284

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

285

The Ordered Clustered Travelling Salesman Problem: A Hybrid Genetic Algorithm  

PubMed Central

The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances. PMID:24701148

Ahmed, Zakir Hussain

2014-01-01

286

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

287

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.

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

2012-09-01

288

Genetic Algorithms: A New Method for Neutron Beam Spectral Characterization  

Microsoft Academic Search

A revolutionary new concept for solving the neutron spectrum unfolding problem using genetic algorithms (GAs) has recently been introduced. GAs are part of a new field of evolutionary solution techniques that mimic living systems with computer-simulated chromosome solutions that mate, mutate, and evolve to create improved solutions. The original motivation for the research was to improve spectral characterization of neutron

David W. Freeman

2000-01-01

289

Optimizing Interleaver for Turbo Codes by Genetic Algorithms  

Microsoft Academic Search

Since the appearance in 1993, first approaching the Shannon limit, the Turbo Codes give a new direction for the channel encoding field, especially since they were adopted for multiple norms of telecommunications, such as deeper communication. To obtain an excellent performance it is necessary to design robust turbo code interleaver. We are investigating genetic algorithms as a promising optimization method

P. Kromer; V. Snasel; J. Platos; P. N. Ouddane

2007-01-01

290

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

291

Multiobjective Genetic Algorithms for Pump Scheduling in Water Supply  

E-print Network

1 Multiobjective Genetic Algorithms for Pump Scheduling in Water Supply by Dragan A. Savic, Godfrey Introduction Seeking cost reduction and energy savings in water supply by improving the operation of pumps to the basic elements of a water supply system, remarkable reductions in operation costs can be achieved

Coello, Carlos A. Coello

292

HARDWARE IMPLEMENTATION OF A PARALLELIZED GENETIC ALGORITHM FOR  

E-print Network

HARDWARE IMPLEMENTATION OF A PARALLELIZED GENETIC ALGORITHM FOR TASK SCHEDULING by VIJAY TIRUMALAI in Electrical Engineering in the Department of Electrical and Computer Engineering in the Graduate School of the requirements for the degree of Master of Science in Electrical Engineering. Accepted on behalf of the Faculty

Ricks, Kenneth G.

293

Genetic Algorithms for Cutting Stock Problems: With and Without Contiguity  

Microsoft Academic Search

A number of optimisation problems involve the optimal grouping of a finite set of items into a number of categories subject to one or more constraints. Such problems raise interesting issues in mapping solutions in genetic algorithms. These problems range from the knapsack problem to bin packing and cutting stock problems. This paper describes research involving cutting stock problems. Results

Robert Hinterding; Lutfar Khan

1994-01-01

294

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

295

A Clustering Genetic Algorithm for Actuator Optimization in Flow Control  

Microsoft Academic Search

Active flow control can provide a leap in the perform- nace of engineering configurations. Although a number of sensor and actuator configurations have been proposed the task of identifying optimal parameters for control devices is based on engineering intuition usually gathered from un- controlled flow experiments. Here we propose a clustering genetic algorithm that adaptively identifies critical points in the

Michele Milano; Petros Koumoutsakos

2000-01-01

296

Statistical Dynamics of the Royal Road Genetic Algorithm  

E-print Network

G . . . . . . . . . . . . . . . . . . . . 20 5.6 GA Dynamics as a Flow in Fitness Distribution Space an analytical model for the dynamics of a mutation- only genetic algorithm (GA) is introduced that identifies a new and general mechanism causing metastability in evolutionary dynamics. The GA's population dynamics

Mitchell, Melanie

297

OPTIMUM ACTUATOR SELECTION WITH A GENETIC ALGORITHM FOR AIRCRAFT CONTROL  

E-print Network

be a source of aerodynamic noise and increased observability. Flow control actuators potentially allow been used to identify potential regions for the actuators, a genetic algorithm (GA) is an excellent tool for determining the optimum placement. The use of GA's has been instrumental in achieving good

Coello, Carlos A. Coello

298

Time-Delay System Identification Using Genetic Algorithm -Part Two  

E-print Network

Time-Delay System Identification Using Genetic Algorithm - Part Two: FOPDT/SOPDT Model-Order-Plus-Dead-Time (FOPDT) or Second-Order-Plus-Dead-Time (SOPDT) model approximation to a complicated process system can be carried out through either a kind of model reduction approach or a kind of system identification approach

Yang, Zhenyu

299

PARAMETER ESTIMATION OF PUMPING TEST DATA USING GENETIC ALGORITHM  

Microsoft Academic Search

Proper management of ground water requires estimation of hydraulic properties for aquifer systems such as transmissivity, hydraulic conductivity, storage coefficient and leakage. The pumping test is the standard technique for determining these hydraulic properties, for which graphical methods are widely used. In the present research, the effectiveness of an optimization technique called genetic algorithm (GA), which usually ensures near optimal

Hossam Abdel-Aziz; Ahmed Abdel-Gawad; Hoda Ali El-Hadi

2009-01-01

300

Biased and unbiased random-key genetic algorithms: An ...  

E-print Network

Jan 3, 2013 ... We study the runtime performance of three types of random- ... genetic algorithms as well as in biology, is key for evolution of the ..... the table lists its class, name, dimensions, value of the target ..... A guide to the theory of.

mgcr

301

Synthesis design of metamaterial absorbers using a genetic algorithm  

Microsoft Academic Search

In this paper, a simple and efficient method, genetic algorithm (GA), for synthesis metamaterials is proposed to develop high absorption, wideband and multi-band absorbers. Several steps are added to the GA to higher its efficiency. We present three representative examples, for both single-layer and multilayer, with different reflectivity objectives which are optimized successfully by the GA. This GA based design

Lu Wang; Tao Wang; Yan Nie; Rongzhou Gong

2010-01-01

302

A Genetic Algorithm for Multiobjective Design Optimization in Aerodynamics and  

E-print Network

A Genetic Algorithm for Multiobjective Design Optimization in Aerodynamics and Electromagnetics R. The objective functions in the optimization problem measure the aerodynamic feasibil­ ity based on the drag been optimized with respect to only one discipline such as aerodynamics or electromagnetics. Although

Coello, Carlos A. Coello

303

MOGADES: Multi-Objective Genetic Algorithm with Distributed Environment Scheme  

E-print Network

, when structures such as cars and air plains, electric devices such as circuits and controllers and so [2]. Those are NSGA-II [3], SPEA-II [4], NPGA-II [5] and MOGA [6]. One of the disadvantages] and NSGA-II [3], the advantages and disadvantages of MO- GADES are made clarified. 2 Genetic Algorithms

Coello, Carlos A. Coello

304

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

305

Exploring and Optimizing Dynamic Neural Fields Parameters Using Genetic Algorithms  

E-print Network

of the characteristics needed to adopt complex goal-oriented behaviors. Bubbles can indeed shape the attractor landscape: they either lead to the saturation of the field, the lack of any coherent activity, or the selfExploring and Optimizing Dynamic Neural Fields Parameters Using Genetic Algorithms Jean

Boyer, Edmond

306

Filter Bank Design for Speaker Diarization Based on Genetic Algorithms  

Microsoft Academic Search

Speech recognition systems usually need a feature extraction stage aiming at obtaining the best signal representation. In this article we propose to use genetic algorithms to design a feature extraction method adapted to the speaker diarization task. We present an adaptation of the common MFCC feature extractor which consists in designing a filter bank, with optimized bandwidths. Experiments are carried

C. Charbuillet; B. Gas; M. Chetouani; J. L. Zarader

2006-01-01

307

Complementary Features for Speaker Verification Based on Genetic Algorithms  

Microsoft Academic Search

Speech recognition systems usually need a feature extraction stage aiming at obtaining the best signal representation. State of the art speaker verification systems are based on cepstrals features like MFCC, LFCC or LPCC. In this article, we propose to use a genetic algorithm to provide new features able to complete the LFCC's. We present an adaptation of the common LFCC

C. Charbuillet; B. Gas; M. Chetouani; J. L. Zarader

2007-01-01

308

Crossover Improvement for the Genetic Algorithm in Information Retrieval  

Microsoft Academic Search

Genetic algorithms (GAs) search for good solutions to a problem by operations inspired from the natural selection of living beings. Among their many uses, we can count information retrieval (IR). In this field, the aim of the GA is to help an IR system to find, in a huge documents text collection, a good reply to a query expressed by

Dana Vrajitoru

1998-01-01

309

Genetic Algorithms for Gait Synthesis in a Hexapod Robot  

Microsoft Academic Search

This paper describes the staged evolution of a complex motor pattern generator (CPG) for the control of the leg movements of a six-legged walking robot. The CPG is composed of a network of neurons. In contrast to the main stream work in neural networks, the interconnection weights are altered by a Genetic Algo- rithm (GA), rather than a learning algorithm.

M. Anthony Lewis; Andrew H. Fagg; George A. Bekey

1994-01-01

310

Technical Report No. 494 Using Cyclic Genetic Algorithms  

E-print Network

for a small hexapod robot are generated by a cyclic genetic algorithm. From these automata a Xilinx net list the communication network of an experimental robot colony. This recon guration of the hexapod's nervous system locomotion control in very simple hexapod agents. Frequent redesign of agent control is part of the evolving

Portland State University

311

Change Detection in Satellite Images Using a Genetic Algorithm Approach  

Microsoft Academic Search

In this letter, we propose a novel method for unsupervised change detection in multitemporal satellite images by minimizing a cost function using a genetic algorithm (GA). The difference image computed from the multitemporal satellite images is partitioned into two distinct regions, namely, ??changed?? and ??unchanged,?? according to the binary change detection mask realization from the GA. For each region, the

Turgay Celik

2010-01-01

312

Hybrid real coded genetic algorithm solution to economic dispatch problem  

Microsoft Academic Search

This paper presents a new, two-phase hybrid real coded genetic algorithm (GA) based technique to solve economic dispatch (ED) problem with multiple fuel options. The proposed hybrid scheme is developed in such a way that a simple real coded GA is acting as a base level search, which makes a quick decision to direct the search towards the optimal region,

S. Baskar; P. Subbaraj; M. V. C. Rao

2003-01-01

313

Design of passive control for flexible structure using genetic algorithm  

Microsoft Academic Search

A Genetic Algorithm (GA) based approach for design of H¿ passive control for a flexible structure is presented. The flexible structure has six degrees of freedom and the control system is designed by using the H¿-control theory. The passive control is to minimize the H¿-norm from closed loop transfer function of the system which implies suppressing the magnitude peaks vibration

Roberd Saragih; Taufan Mahardhika

2009-01-01

314

Using genetic algorithms to select inputs for neural networks  

Microsoft Academic Search

The application of neural networks to nuclear power plants for fault diagnostics is a very challenging task. How to select proper input variables for neural networks from hundreds of plant processing variables is crucially important to the success. Genetic algorithms are used in this study to guide the search for optimal combination of inputs for the neural networks to reach

Zhichao Guo; Robert E. Uhrig

1992-01-01

315

Application of genetic algorithms in resource constrained network optimization  

Microsoft Academic Search

There are limited solution techniques available for resource constrained project scheduling problems with stochastic task durations. Due to computational complexity, scheduling heuristics have been found useful for large deterministic problems. In this paper, the authors demonstrate the use of a genetic algorithm to optimize over a linear combination of scheduling heuristics. A simulation model is used to evaluate the performance

J. Pet-Edwards; M. Mollaghasemi

1995-01-01

316

Selecting linguistic classification rules by two-objective genetic algorithms  

Microsoft Academic Search

Shows how two-objective genetic algorithms can be applied to a rule selection problem of linguistic classification rules. First the authors briefly describe a generation method of linguistic classification rules from numerical data. Next the authors formulate a rule selection problem of linguistic classification rules. This problem has two objectives: to maximize the number of correctly classified training patterns and to

Hisao Ishibuchi; Tadahiko Murata; I. B. Turksen

1995-01-01

317

Phase transitions and symmetry breaking in genetic algorithms with crossover  

Microsoft Academic Search

In this paper, we consider the role of the crossover operator in genetic algorithms. Specifically, we study optimisation problems that exhibit many local optima and consider how crossover affects the rate at which the population breaks the symmetry of the problem. As an example of such a problem, we consider the subset sum problem. In doing so, we demonstrate a

Alex Rogers; Adam Prügel-bennett; Nicholas R. Jennings

2006-01-01

318

Genetic-algorithm-based fuzzy control of spacecraft autonomous rendezvous  

Microsoft Academic Search

The combination of the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms is investigated. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy-logic-based expert systems have been used successfully for controlling a number of physical systems, the tasks of selecting acceptable fuzzy membership functions and

L. Michael Freeman

1997-01-01

319

Improved genetic algorithm for the design of stiffened composite panels  

Microsoft Academic Search

The design of composite structures against buckling presents two major challenges to the designer. First, the problem of laminate stacking sequence design is discrete in nature, involving a small set of fiber orientations, which complicates the solution process. Therefore, the design of the stacking sequence is a combinatorial optimization problem which is suitable for genetic algorithms. Second, many local optima

S. Nagendra; D. Jestin; Z. Giirdal; R. T. Haftka; L. T. Watson

1996-01-01

320

LOGISTIC MANAGEMENT USING MULTI-OBJECTIVE GENETIC ALGORITHM  

Microsoft Academic Search

This paper presents the improvement of logistic management using Multi-Objective Genetic Algorithm (MOGA). There are 2 (two) contradictory objectives that will be considered for this paper. They are the total cost and time for the document carriage among the district offices of the Metropolitan Electricity Authority (MEA). It is a stochastic problem like the Traveling Salesman Problem (TSP). However, the

Nattavut Keerativuttitumrong

321

Optimizing the reservoir operating rule curves by genetic algorithms  

Microsoft Academic Search

Genetic algorithms, founded upon the principle of evolution, are applicable to many optimization problems, especially popular for solving parameter optimization problems. Reservoir operating rule curves are the most common way for guiding and managing the reservoir operation. These rule curves traditionally are derived through intensive simulation techniques. The main aim of this study is to investigate the efficiency and effectiveness

Fi-John Chang; Li Chen; Li-Chiu Chang

2005-01-01

322

A Simple Genetic Algorithm for Biomarker Mining Dusan Popovic1  

E-print Network

A Simple Genetic Algorithm for Biomarker Mining Dusan Popovic1 , Alejandro Sifrim1 , Georgios A.Popovic,Alejandro.Sifrim,Georgios.Pavlopoulos,Yves.Moreau, Bart.DeMoor}@esat.kuleuven.be Abstract. We present a method for prognostics biomarker mining based Background The recent advances in high-throughput technologies have opened a wide space of opportunities

323

Reverse HillclimbingGenetic Algorithms and the Busy Beaver Problem  

Microsoft Academic Search

This paper introduces a new analysis tool called reverse hillclimbing, and demonstrateshow it can be used to evaluate the performance of a genetic algorithm. Usingreverse hillclimbing, one can calculate the exact probability that hillclimbing will attainsome point in a landscape. From this, the expected number of evaluations before thepoint is found by hillclimbing can be calculated. This figure can be

Terry Jones; Gregory J. E. Rawlins

1993-01-01

324

Reverse Hillclimbing, Genetic Algorithms and the Busy Beaver Problem  

Microsoft Academic Search

This paper introduces a new analysis tool called {\\\\it reverse hillclimbing}, and demonstrates how it can be used to evaluate the performance of a genetic algorithm. Using reverse hillclimbing, one can calculate the exact probability that hillclimbing will attain some point in a landscape. From this, the expected number of evaluations before the point is found by hillclimbing can be

Terry Jones; Gregory J. E. Rawlins

1993-01-01

325

Genetic Algorithms for Multiple-Choice Optimisation Problems  

E-print Network

Genetic Algorithms for Multiple- Choice Optimisation Problems by Uwe Aickelin (Dipl Kfm, EMBSc to the University of Wales In candidature for the Degree of Doctor of Philosophy European Business Management School-library loan, and for the title and summary to be made available to outside organisations. Signed

Aickelin, Uwe

326

Novel geometry gradient coils for MRI designed by genetic algorithm  

E-print Network

dimensions vu 8 51 52 81 82 93 105 107 108 115 117 122 Vlll Abbreviations CT EPl GA GRASS HPCF HSLMC MPl MRl PET r.f. e.m.f. r.m.s. Computed Tomography Echo Planar Imaging Genetic Algorithm Gradient Recalled Acquisition...

Williams, Guy Barnett

2001-06-19

327

A Genetic Algorithm to Improve an Othello Program  

Microsoft Academic Search

this article, we show how genetic algorithms can be used to evolve the parameters of theevaluation function of an Othello program. We must stress that our method can be used on anyalgorithm using an evaluation function, for any two players game. In the first part of the article,we explain the structure of the Othello program. In the second part, we

Jean-marc Alliot; Nicolas Durand

1995-01-01

328

Harmonic optimization of multilevel converters using genetic algorithms  

Microsoft Academic Search

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

B. Ozpineci; L. M. Tolbert; J. N. Chiasson

2004-01-01

329

Robust load frequency controller design via Genetic algorithm and H?  

Microsoft Academic Search

Three robust load frequency controllers are proposed in this paper. The first is based on Hí control design in order to obtain robustness against uncertainties. The second controller is a reduced model to the Hí controller because the first one is very complex for practical implementation. Genetic algorithm (GA) is used in the third controller to optimize proportional integral differential

M. E. D. Mandour; E. S. Ali; M. E. Lotfy

2010-01-01

330

Multiscale Island Injection Genetic Algorithm for Ground Water Remediation  

Microsoft Academic Search

Genetic algorithms have been shown to be powerful tools for solving a wide variety of water resources optimization problems. Applying these approaches to complex, large-scale applications, which is usually where these methods are most needed, can be difficult due to computational limitations. Large grid sizes are often needed for solving field-scale groundwater remediation design problems. Fine grids usually improve the

Eva Sinha; Barbara Minsker; Meghna Babbar

331

Multiscale island injection genetic algorithms for groundwater remediation  

Microsoft Academic Search

Genetic algorithms have been shown to be powerful tools for solving a wide variety of water resources optimization problems. Applying these approaches to complex, large-scale water resources applications can be difficult due to computational limitations, especially when a numerical model is needed to evaluate different solutions. This problem is particularly acute for solving field-scale groundwater remediation design problems, where fine

Eva Sinha; Barbara S. Minsker

2007-01-01

332

A Random Key Based Genetic Algorithm for the Resource ...  

E-print Network

Jun 30, 2005 ... the classical job shop scheduling problem belongs to the class of NP- ... (2004)), tabu search ... In this paper, we present a new genetic algorithm for solving the ... is always active, so the search space can be safely limited to the set of .... about the structure of the problem. ..... variable neighbourhood search.

x

2005-07-01

333

Object Recognition by Flexible Template Matching using Genetic Algorithms  

Microsoft Academic Search

We demonstrate the use of a Genetic Algorithm (GA) to match a flexible template model to image evidence. The advantage of the GA is that plausible interpretations can be found in a relatively small number of trials; it is also possible to generate multiple distinct interpretation hypotheses. The method has been applied to the interpretation of ultrasound images of the

Andrew Hill; Christopher J. Taylor; Timothy F. Cootes

1992-01-01

334

Aspects of Genetic Algorithm-Designed Fuzzy Logic Controllers.  

National Technical Information Service (NTIS)

The research described in the report is twofold. First, the basic approach to developing a fuzzy logic controller (FLC) using genetic algorithms (GA's) is presented. The GA-designed FLC is developed for a specific physical system, a pH titration system. S...

C. L. Karr, J. W. Fleming, P. A. Vann

1994-01-01

335

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

336

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

337

A hybrid genetic algorithm for manufacturing cell formation1  

E-print Network

manufacturing is the formation of product families and machine cells. This paper presents a new approach product and machine cells simultaneously, so additional methods must be employed to complete the designA hybrid genetic algorithm for manufacturing cell formation1 José Fernando Gonçalves Faculdade de

Fisher, Kathleen

338

Automated Design of Algorithms and Genetic Improvement: Contrast and Commonalities  

E-print Network

. Haraldsson University of Stirling Stirling, Scotland, UK soh@cs.stir.ac.uk John R. Woodward University of Stirling Stirling, Scotland, UK jrw@cs.stir.ac.uk Categories and Subject Descriptors I.2.2 [Automatic Based Soft- ware Engineering (SBSE), Genetic Algorithm (GA) 1. INTRODUCTION Recent decades has seen

Woodward, John

339

A Genetic Algorithm Approach to Focused Software Usage Testing  

E-print Network

A Genetic Algorithm Approach to Focused Software Usage Testing Robert M. Patton, Annie S. Wu Orlando, FL, U.S.A. ABSTRACT Because software system testing typically consists of only a very small the test results from a limited amount of testing based on high-level usage models. It can also be very

Wu, Annie S.

340

Automatic page layout using genetic algorithms for electronic albuming  

Microsoft Academic Search

In this paper, we describe a flexible system for automatic page layout that makes use of genetic algorithms for albuming applications. The system is divided into two modules, a page creator module which is responsible for distributing images amongst various album pages, and an image placement module which positions images on individual pages. Final page layouts are specified in a

Joe Geigel; Alexander C. Loui

2000-01-01

341

A Genetic Algorithm for Simultaneous Localization and Mapping Tom Duckett  

E-print Network

://www.aass.oru.se Abstract--- This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobileA Genetic Algorithm for Simultaneous Localization and Mapping Tom Duckett Centre for Applied data must be used for both mapping and localization. We can separate two major sources of uncertainty

Duckett, Tom

342

A Genetic Algorithm for Simultaneous Localization and Mapping Tom Duckett  

E-print Network

://www.aass.oru.se Abstract-- This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobileA Genetic Algorithm for Simultaneous Localization and Mapping Tom Duckett Centre for Applied data must be used for both mapping and localization. We can separate two major sources of uncertainty

Duckett, Tom

343

Lossless fitness inheritance in genetic algorithms for decision trees  

Microsoft Academic Search

This paper shows that genetic algorithms can build decision trees very quickly, since some of the most obvious tree quality parameters are recursively computed and, therefore, can be re-used across generations of partially similar decision trees. We show that simply storing instance counters at decision tree leaves is enough for fitness to be piecewise computed in a lossless fashion. We

Dimitris Kalles; Athanassios Papagelis

2006-01-01

344

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

345

A parallel genetic algorithm for the set partitioning problem  

SciTech Connect

This paper describes a parallel genetic algorithm developed for the solution of the set partitioning problem- a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their own subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. We found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, high- quality integer feasible solutions were found for problems with 36, 699 and 43,749 integer variables, respectively. A notable limitation we found was the difficulty solving problems with many constraints.

Levine, D.

1996-12-31

346

A Genetic Algorithm for Solving a Capacitated p Median Problem  

Microsoft Academic Search

Facility-location problems have several applications, such as telecommunications, industrial transportation and distribution. One of the most well-known facility-location problems is the p-median problem. This work addresses an application of the capacitated p-median problem to a real-world problem. We propose a genetic algorithm (GA) to solve the capacitated p-median problem. The proposed GA uses not only conventional genetic operators, but also

Elon Santos Correa; Maria Teresinha A. Steiner; Alex A. Freitas; Celso Carnieri

2004-01-01

347

A Genetic Algorithm for the P-Median Problem  

Microsoft Academic Search

Facility-location problems have several applications in telecommunications, industrial transportation and distribution, etc. One of the most well-known facility-location problems is the p-median problem. This work addresses an application of the capacitated p-median problem to a real-world problem. We propose a genetic algorithm (GA) to solve the capacitated p- median problem. The proposed GA uses not only conventional genetic operators but

Elon Santos Correa; Maria Teresinha; A. Steiner; Alex A. Freitas; Celso Carnieri; Centro Politecnico

2001-01-01

348

Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms  

E-print Network

(1995) optimized the drilling schedule and well location in an oil reservoir through a traveling salesman structure with the use of Simulated Annealing. Bittencourt and Horne (1997) approached the well placement optimization problem using a genetic... robust, stochastic, and streamlined optimization method. Genetic Algorithms ?efficiently exploit historical information to speculate on new search points with expected improved performance.? (Goldberg 1989) The GA population is represented by a...

Gibbs, Trevor Howard

2011-08-08

349

Genetic algorithm for extracting rules in discrete domain  

SciTech Connect

We propose a genetic algorithm that evolves families of rules from a set of examples. Inputs and outputs of the problem are discrete and nominal values which makes it difficult to use alternative learning methods that implicitly regard a metric space. A way how to encode sets of rules is presented together with special variants of genetic operators suitable for this encoding. The solution found by means of this process can be used as a core of a rule-based expert system.

Neruda, R.

1995-09-20

350

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

2012-01-01

351

Sampling protein conformations using segment libraries and a genetic algorithm  

NASA Astrophysics Data System (ADS)

We present a new simulation algorithm for minimizing empirical contact potentials for a simplified model of protein structure. The model consists of backbone atoms only (including C?) with the ? and ? dihedral angles as the only degrees of freedom. In addition, ? and ? are restricted to a finite set of 532 discrete pairs of values, and the secondary structural elements are held fixed in ideal geometries. The potential function consists of a look-up table based on discretized inter-residue atomic distances. The minimization consists of two principal elements: the use of preselected lists of trial moves and the use of a genetic algorithm. The trial moves consist of substitutions of one or two complete loop regions, and the lists are in turn built up using preselected lists of randomly-generated three-residue segments. The genetic algorithm consists of mutation steps (namely, the loop replacements), as well as a hybridization step in which new structures are created by combining parts of two "parents'' and a selection step in which hybrid structures are introduced into the population. These methods are combined into a Monte Carlo simulated annealing algorithm which has the overall structure of a random walk on a restricted set of preselected conformations. The algorithm is tested using two types of simple model potential. The first uses global information derived from the radius of gyration and the rms deviation to drive the folding, whereas the second is based exclusively on distance-geometry constraints. The hierarchical algorithm significantly outperforms conventional Monte Carlo simulation for a set of test proteins in both cases, with the greatest advantage being for the largest molecule having 193 residues. When tested on a realistic potential function, the method consistently generates structures ranked lower than the crystal structure. The results also show that the improved efficiency of the hierarchical algorithm exceeds that which would be anticipated from tests on either of the two main elements used independently.

Gunn, John R.

1997-03-01

352

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

353

JavaGenes and Condor: Cycle-Scavenging Genetic Algorithms  

NASA Technical Reports Server (NTRS)

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

Globus, Al; Langhirt, Eric; Livny, Miron; Ramamurthy, Ravishankar; Soloman, Marvin; Traugott, Steve

2000-01-01

354

The Water-Distribution Networks Waterpower Calculation Based on Immune Genetic Algorithm  

Microsoft Academic Search

With the daily becoming enlargement of water distribution networks scale, the problem of water distribution networks waterpower calculation has become more and more complicated, the traditional algorithm has been already unable to satisfy the need of solving. This paper combines the immune algorithm and genetic algorithm together, has proposed a kind of immunity genetic algorithm, and apply it to solve

Quansheng Luo; Jing Tian

2010-01-01

355

Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm  

Microsoft Academic Search

This paper introduces ICET, a new algorithm for cost-sensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors. ICET

P. D. Turney

1995-01-01

356

Prediction for traffic accident severity: comparing the artificial neural network, genetic algorithm, combined genetic algorithm and pattern search methods  

Microsoft Academic Search

This paper focuses on predicting the severity of freeway traffic accidents by employing twelve accident-related parameters in a genetic algorithm (GA), pattern search and artificial neural network (ANN) modelling methods. The models were developed using the input parameters of driver's age and gender, the use of a seat belt, the type and safety of a vehicle, weather conditions, road surface,

Mehmet Metin Kunt; Iman Aghayan; Nima Noii

2011-01-01

357

Harmonic Optimization of Multilevel Converters Using Genetic Algorithms Abstract--In this paper, a genetic algorithm (GA) optimization  

E-print Network

, a genetic algorithm (GA) optimization technique is applied to multilevel inverter to determine optimum switching angles for cascaded multilevel inverters for eliminating some higher order harmonics while maintaining the required fundamental voltage. This technique can be applied to multilevel inverters with any

Tolbert, Leon M.

358

A comparison of binary and continuous genetic algorithm in parameter estimation of a logistic growth model  

NASA Astrophysics Data System (ADS)

Genetic algorithm is an optimization method based on the principles of genetics and natural selection in life organisms. The algorithm begins by defining the optimization variables, defining the cost function (in a minimization problem) or the fitness function (in a maximization problem) and selecting genetic algorithm parameters. The main procedures in genetic algorithm are generating initial population, selecting some chromosomes (individual) as parent's individual, mating, and mutation. In this paper, binary and continuous genetic algorithms were implemented to estimate growth rate and carrying capacity parameter from poultry data cited from literature. For simplicity, all genetic algorithm parameters (selection rate and mutation rate) are set to be constant along implementation of the algorithm. It was found that by selecting suitable mutation rate, both algorithms can estimate these parameters well. Suitable range for mutation rate in continuous genetic algorithm is wider than the binary one.

Windarto, Indratno, S. W.; Nuraini, N.; Soewono, E.

2014-02-01

359

Distributed Query Plan Generation Using Multiobjective Genetic Algorithm  

PubMed Central

A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability. PMID:24963513

Panicker, Shina; Vijay Kumar, T. V.

2014-01-01

360

Acoustic Impedance Inversion of Seismic Data Using Genetic Algorithm  

NASA Astrophysics Data System (ADS)

The inversion of seismic data can be used to constrain estimates of the Earth's acoustic impedance structure. This kind of problem is usually known to be non-linear, high-dimensional, with a complex search space which may be riddled with many local minima, and results in irregular objective functions. We investigate here the performance and the application of a genetic algorithm, in the inversion of seismic data. The proposed algorithm has the advantage of being easily implemented without getting stuck in local minima. The effects of population size, Elitism strategy, uniform cross-over and lower mutation are examined. The optimum solution parameters and performance were decided as a function of the testing error convergence with respect to the generation number. To calculate the fitness function, we used L2 norm of the sample-to-sample difference between the reference and the inverted trace. The cross-over probability is of 0.9-0.95 and mutation has been tested at 0.01 probability. The application of such a genetic algorithm to synthetic data shows that the inverted acoustic impedance section was efficient. Keywords: Seismic, Inversion, acoustic impedance, genetic algorithm, fitness functions, cross-over, mutation.

Eladj, Said; Djarfour, Noureddine; Ferahtia, Djalal; Ouadfeul, Sid-Ali

2013-04-01

361

A novel pipeline based FPGA implementation of a genetic algorithm  

NASA Astrophysics Data System (ADS)

To solve problems when an analytical solution is not available, more and more bio-inspired computation techniques have been applied in the last years. Thus, an efficient algorithm is the Genetic Algorithm (GA), which imitates the biological evolution process, finding the solution by the mechanism of "natural selection", where the strong has higher chances to survive. A genetic algorithm is an iterative procedure which operates on a population of individuals called "chromosomes" or "possible solutions" (usually represented by a binary code). GA performs several processes with the population individuals to produce a new population, like in the biological evolution. To provide a high speed solution, pipelined based FPGA hardware implementations are used, with a nstages pipeline for a n-phases genetic algorithm. The FPGA pipeline implementations are constraints by the different execution time of each stage and by the FPGA chip resources. To minimize these difficulties, we propose a bio-inspired technique to modify the crossover step by using non identical twins. Thus two of the chosen chromosomes (parents) will build up two new chromosomes (children) not only one as in classical GA. We analyze the contribution of this method to reduce the execution time in the asynchronous and synchronous pipelines and also the possibility to a cheaper FPGA implementation, by using smaller populations. The full hardware architecture for a FPGA implementation to our target ALTERA development card is presented and analyzed.

Thirer, Nonel

2014-05-01

362

Genetic algorithms and their use in Geophysical Problems  

SciTech Connect

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

Parker, Paul B.

1999-04-01

363

Design of an acoustic metamaterial lens using genetic algorithms.  

PubMed

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

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

2012-10-01

364

Modified Niched Pareto Multi-objective Genetic Algorithm for Construction Scheduling Optimization  

E-print Network

, a new data structure is proposed to define a solution to the problem and a general Niched Pareto Genetic Algorithm (NPGA) is modified to facilitate optimization procedure. The main features of the proposed Multi-Objective Genetic Algorithm (MOGA...

Kim, Kyungki

2012-10-19

365

Genetic Algorithm Based Optimization of Clustering in Ad Hoc Networks  

E-print Network

In this paper, we have to concentrate on implementation of Weighted Clustering Algorithm with the help of Genetic Algorithm (GA).Here we have developed new algorithm for the implementation of GA-based approach with the help of Weighted Clustering Algorithm (WCA) (4). ClusterHead chosen is a important thing for clustering in adhoc networks. So, we have shown the optimization technique for the minimization of ClusterHeads(CH) based on some parameter such as degree difference, Battery power (Pv), degree of mobility, and sum of the distances of a node in adhoc networks. ClusterHeads selection of adhoc networks is an important thing for clustering. Here, we have discussed the performance comparison between deterministic approach and GA based approach. In this performance comparison, we have seen that GA does not always give the good result compare to deterministic WCA algorithm. Here we have seen connectivity (connectivity can be measured by the probability that a node is reachable to any other node.) is better th...

Nandi, Bhaskar; Paul, Soumen

2010-01-01

366

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

E-print Network

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

Yang, Shengxiang

367

Moving to Smaller Libraries via Clustering and Genetic Algorithms G. Antoniol  

E-print Network

Moving to Smaller Libraries via Clustering and Genetic Algorithms G. Antoniol , M. Di Penta , M the memory requirements of exe- cutables. The approach is organized in two steps. The first step defines genetic algorithms. In particular, a novel genetic algorithm approach, con- sidering the initial clusters

Di Penta, Massimiliano

368

Genetic Algorithms for Quantum Circuit Design Evolving a Simpler Teleportation Circuit  

E-print Network

Genetic Algorithms for Quantum Circuit Design ­Evolving a Simpler Teleportation Circuit­ Taro ever known. keyword: genetic algorithms, quantum teleportation, quantum computer, quantum computing-mentioned previous studies, we apply genetic algorithms to designing a quantum teleportation circuit. Quantum

Michigan, University of

369

An hybrid real genetic algorithm to detect structural damage using modal properties  

Microsoft Academic Search

An hybrid real-coded Genetic Algorithm with damage penalization is implemented to locate and quantify structural damage. Genetic Algorithms provide a powerful tool to solved optimization problems. With an appropriate selection of their operators and parameters they can potentially explore the entire solution space and reach the global optimum. Here, the set-up of the Genetic Algorithm operators and parameters is addressed,

V. Meruane; W. Heylen

2011-01-01

370

Theory of Genetic Algorithms Thomas Back \\Lambda , Jeannette M. de Graaf,  

E-print Network

methods for examining the fundamental properties of genetic algorithms ([Hol75, Jon75, Gol89c, Mit96Theory of Genetic Algorithms Thomas B¨ack \\Lambda , Jeannette M. de Graaf, Joost N. Kok and Walter of a fixed length consisting of zeroes and ones. Genetic algorithms are a subfield of evolutionary

de Graaf, Jeannette

371

In this paper1 we describe a genetic algorithm capable of  

E-print Network

. The schema theorem (Holland, 1975), often called the Fundamental Theorem of Genetic Algorithms, illus- tratesAbstract In this paper1 we describe a genetic algorithm capable of evolving large programs, 1991). In this paper we describe a genetic algorithm which is capable of evolving large programs

Fernandez, Thomas

372

1 Statistical Inference as a Theoretical Founda-tion of Genetic Algorithms  

E-print Network

the "fundamental theorem of genetic algorithms" is either a tautology or wrong, depending on the interpretation1 Statistical Inference as a Theoretical Founda- tion of Genetic Algorithms Research site: GMD of this research are to develop a predictive theory of the Breeder Genetic Algorithm BGA and to solve Grand

373

Comparing Darwinian, Baldwinian, and Lamarckian Search in a Genetic Algorithm for the 4Cycle Problem  

E-print Network

strategies yields the best results. 1 Introduction Genetic algorithms abstract the fundamental processesComparing Darwinian, Baldwinian, and Lamarckian Search in a Genetic Algorithm for the 4­Cycle USA julstrom@eeyore.stcloudstate.edu Abstract Genetic algorithms abstract the fundamen­ tal processes

Julstrom, Bryant A.

374

Genetic Algorithms: A Fundamental Component of an Optimization Toolkit for Improved  

E-print Network

Genetic Algorithms: A Fundamental Component of an Optimization Toolkit for Improved Engineering played by the toolkit's genetic algorithm in providing a robust, general purpose solution to nonlinear. This paper focuses on the role of the Genetic Algorithm in iSIGHT's Optimization Toolkit and its application

Coello, Carlos A. Coello

375

A biased random-key genetic algorithm for the unequal area facility layout problem  

E-print Network

A biased random-key genetic algorithm for the unequal area facility layout problem Jos� Fernando a biased random key genetic algorithm (BRKGA) for the unequal area facility layout problem (UA-FLP) where: Facilities planning and design, facility layout, biased random-key genetic algorithms, random-keys. 1

Resende, Mauricio G. C.

376

On the Influence of Selection Operators on Performances in Cellular Genetic Algorithms  

Microsoft Academic Search

In this paper, we study the influence of the selective pressure on the performance of cellular genetic algorithms. Cellular genetic algorithms are genetic algorithms where the population is embedded on a toroidal grid. This structure makes the propagation of the best so far individual slow down, and allows to keep in the population potentially good solutions. We present two selective

David Simoncini; Philippe Collard; Sébastien Verel; Manuel Clergue

2008-01-01

377

A Random-Key Genetic Algorithm for the Generalized Traveling Salesman Problem  

E-print Network

, GTSP, Genetic algorithms, Random keys, Metaheuristics Corresponding author. 1 #12;1 INTRODUCTION 2 1A Random-Key Genetic Algorithm for the Generalized Traveling Salesman Problem Lawrence V. Snyder for this problem. The method combines a genetic algorithm (GA) with a local tour improvement heuristic. Solutions

Snyder, Larry

378

A Novel Genetic Algorithms for the Automated Design of Performance Driven Digital Circuits  

E-print Network

.ed.ac.uk; TughruI.Arslan@ee.ed.ac.uk Abstract- The authors present a genetic algorithm for the design of highA Novel Genetic Algorithms for the Automated Design of Performance Driven Digital Circuits ` Ben. I. The paper describes the genetic algorithm and the hardware evaluation environment, and provides results

Arslan, Tughrul

379

Investigation Of A New Genetic Algorithm Designed For System-On-Chip Realization  

Microsoft Academic Search

This paper introduces a novel genetic algorithm whose properties have been purposely designed to be suited to hardware implementation. This is distinct from previous hardware designs that have been realized directly from conventional genetic algorithm approaches. To be suitable for hardware implementation, we propose that a genetic algorithm should attempt to both minimize final layout dimensions and reduce execution time

Zhenhuan Zhu; David Mulvaney; Vassilios Chouliaras

2006-01-01

380

Local Search Genetic Algorithm for Optimal Design of Reliable Networks Berna Dengiz and Fulya Altiparmak  

E-print Network

Corresponding author. #12;1 Local Search Genetic Algorithm for Optimal Design of Reliable Networks AbstractLocal Search Genetic Algorithm for Optimal Design of Reliable Networks Berna Dengiz and Fulya Pittsburgh, Pennsylvania 15261 USA aesmith@engrng.pitt.edu Abstract This paper presents a genetic algorithm

Smith, Alice E.

381

A GENERIC EVOLUTIONARY COMPUTATION APPROACH BASED UPON GENETIC ALGORITHMS AND EVOLUTION STRATEGIES  

Microsoft Academic Search

Many problems that are treated by genetic algorithms belong to the class of NP-complete problems. The vantage of genetic algorithms when being applied to such kind of problems lies in the ability to search through the solution space in a broader sense than other heuristic methods that are based upon neighborhood search methods. Nevertheless, also genetic algorithms are frequently faced

Michael Affenzeller

382

An EHW Architecture for Real-Time GPS Attitude Determination Based on Parallel Genetic Algorithm  

Microsoft Academic Search

The paper describes a parallel genetic algorithm for the VLSI implementation of real-time GPS attitude determination systems. The genetic algorithm is based on a fine-grained model and utilises AFM (Ambiguity Function Method) for GPS attitude determination. The paper describes various implementation choices with for the genetic algorithm in order to achieve both functionality and practical performance constraints such as speed,

Jiangning Xu; Tughrul Arslan; Qing Wang; Dejun Wan

2002-01-01

383

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

384

The Simple Genetic Algorithm and the Walsh Transform: part I, Theory  

E-print Network

The Simple Genetic Algorithm and the Walsh Transform: part I, Theory Michael D. Vose Alden H the Fourier transform and the simple genetic algorithm. (For a binary representation, the Walsh transform Fourier transform, one generation of the infinite population simple genetic algorithm can be computed

Wright, Alden H.

385

Seeding the Population: Improved Performance in a Genetic Algorithm for the Rectilinear Steiner Problem  

E-print Network

Steiner problem, genetic algorithms, seeding the pop- ulation. Abstract|A hybrid genetic algorithm of such seeding on a genetic algorithm for the rectilinear Steiner problem, which seeks a shortest rectilinear are called Steiner points. The problem of #12;nding a rectilinear Steiner tree of minimum length on a set

Julstrom, Bryant A.

386

Co-evolving Real-Time Strategy Game Playing Influence Map Trees With Genetic Algorithms  

E-print Network

, scripts, and decision trees as done in most game AI, we use genetic algorithms to evolve game playersCo-evolving Real-Time Strategy Game Playing Influence Map Trees With Genetic Algorithms Chris Miles of Nevada, Reno sushil@cse.unr.edu Abstract-- We investigate the use of genetic algorithms to play real

Louis, Sushil J.

387

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

388

Genetic Algorithm Application in Optimization of Wireless Sensor Networks  

PubMed Central

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

Norouzi, Ali; Zaim, A. Halim

2014-01-01

389

A quantum genetic algorithm with quantum crossover and mutation operations  

NASA Astrophysics Data System (ADS)

In the context of evolutionary quantum computing in the literal meaning, a quantum crossover operation has not been introduced so far. Here, we introduce a novel quantum genetic algorithm that has a quantum crossover procedure performing crossovers among all chromosomes in parallel for each generation. A complexity analysis shows that a quadratic speedup is achieved over its classical counterpart in the dominant factor of the run time to handle each generation.

SaiToh, Akira; Rahimi, Robabeh; Nakahara, Mikio

2013-11-01

390

Application of genetic algorithms for optimization of tire pitch sequences  

Microsoft Academic Search

A simple genetic algorithms (GAs) has been applied to generate the optimum pitch sequence. Though a simple GAs worked properly,\\u000a there was the problem of the premature convergence. To solve this problem, we introduced the new operator named the growth\\u000a and combined it with a simple GAs. The growth operator, which is a kind of the hill-climbing technique, has the

Yukio Nakajima; Akihiko Abe

2000-01-01

391

Number of Dipoles for Electromagnetic Source Modeling Using Genetic Algorithm  

Microsoft Academic Search

The Far-Field distribution of an unsymmetrical printed circuit board can be approximated by an equivalent set of dipoles. Genetic Algorithm is employed to optimize the parameters of the dipoles using Near-Field data. The minimum number of dipoles required to represent the radiation source depends upon the expected complexity of the Far-Field radiation pattern. In general, with the increase in frequency,

Sonal Verma; Rahul Mathur

2010-01-01

392

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

393

Focal-Mechanism Determination in Taiwan by Genetic Algorithm  

Microsoft Academic Search

Abstract We determined the focal-mechanism solutions for earthquakes with mag- nitude ML ?4:0 that occurred in the Taiwan region between,1991 and 2005. First- motion polarities of P waves recorded at over 700 seismic stations in Taiwan were used. Because of the large number of events and stations involved, we implemented the genetic algorithm in a nonlinear global search for the

Y.-M. Wu; L. Zhao; C.-H. Chang; Y.-J. Hsu

2008-01-01

394

Solving a multistage partial inspection problem using genetic algorithms  

SciTech Connect

Traditionally, the multistage inspection problem has been formulated as consisting of a decision schedule where some manufacturing stages receive full inspection and the rest none. Dynamic programming and heuristic methods (like local search) are the most commonly used solution techniques. A highly constrained multistage inspection problem is presented where all stages must receive partial rectifying inspection and it is solved using a real-valued genetic algorithm. This solution technique can handle multiple objectives and quality constraints effectively.

Heredia-Langner, Alejandro (BATTELLE (PACIFIC NW LAB)) [BATTELLE (PACIFIC NW LAB); Montgomery, D C.(Arizona State University) [Arizona State University; Carlyle, W M.(Naval Postgraduate School) [Naval Postgraduate School

2002-01-01

395

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

396

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

397

A Modified Genetic Algorithm for Job Shop Scheduling  

Microsoft Academic Search

As a class of typical production scheduling problems, job shop scheduling is one of the strongly NP-complete combinatorial\\u000a optimisation problems, for which an enhanced genetic algorithm is proposed in this paper. An effective crossover operation\\u000a for operation-based representation is used to guarantee the feasibility of the solutions, which are decoded into active schedules\\u000a during the search process. The classical mutation

L. Wang; D.-Z. Zheng

2002-01-01

398

Constrained identification of virtual CNC drives using a genetic algorithm  

Microsoft Academic Search

This paper presents a genetic algorithm (GA) for identifying virtual models of machine tool drives with minimal intervention\\u000a to the production machine. The proposed solution builds on the “rapid identification” concept reported earlier in literature,\\u000a in which a short series of motion data is captured from the Computer Numerical Control (CNC) and used for closed-loop transfer\\u000a function identification subject to

Wilson Wai-Shing Wong; Kaan Erkorkmaz

2010-01-01

399

Energy-aware distribution of monitoring agents using Genetic Algorithms  

Microsoft Academic Search

In a mobile agent-based monitoring network, mobile monitoring agents travel over the network to detect abnormal conditions in real-time. Each mobile monitoring agent is a damage diagnosis program which can recognize certain type of damage. To achieve effective damage detection and prolong the lifetime of a monitoring network, this paper studies energy-aware distribution of mobile monitoring agents using Genetic Algorithm

Wenjia Liu; Bo Chen

2010-01-01

400

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

401

A Knowledge-Intensive Genetic Algorithm for Supervised Learning  

Microsoft Academic Search

Supervised learning in attribute-based spaces is one of the most popular machine learning problems studied and, consequently, has attracted considerable attention of the genetic algorithm community. The full-memory approach developed here uses the same high-level descriptive language that is used in rule-based systems. This allows for an easy utilization of inference rules of the well-known inductive learning methodology, which replace

Cezary Z. Janikow

1993-01-01

402

Tuning of RLS-active vibration controller using genetic algorithm  

Microsoft Academic Search

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

M. O. Tokhi

2010-01-01

403

Genetic algorithms for determining the topological structure of metallic clusters  

Microsoft Academic Search

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

R. Poteau; G. M. Pastor

1999-01-01

404

Lossless fitness inheritance in genetic algorithms for decision trees  

Microsoft Academic Search

When genetic algorithms are used to evolve decision trees, key tree quality parameters can be recursively computed and re-used\\u000a across generations of partially similar decision trees. Simply storing instance indices at leaves is sufficient for fitness\\u000a to be piecewise computed in a lossless fashion. We show the derivation of the (substantial) expected speedup on two bounding\\u000a case problems and trace

Dimitris Kalles; Athanassios Papagelis

2010-01-01

405

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

406

MAC Protocol for Ad Hoc Networks Using a Genetic Algorithm  

PubMed Central

The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339

Elizarraras, Omar; Panduro, Marco; Mendez, Aldo L.

2014-01-01

407

A genetic algorithm to reduce stream channel cross section data  

USGS Publications Warehouse

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

Berenbrock, C.

2006-01-01

408

Leveraging off genetic algorithms for optimizing AGRIN lenses  

NASA Astrophysics Data System (ADS)

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

Manhart, Paul K.; Sparrold, Scott W.

2000-10-01

409

Automatic Data Filter Customization Using a Genetic Algorithm  

NASA Technical Reports Server (NTRS)

This work predicts whether a retrieval algorithm will usefully determine CO2 concentration from an input spectrum of GOSAT (Greenhouse Gases Observing Satellite). This was done to eliminate needless runtime on atmospheric soundings that would never yield useful results. A space of 50 dimensions was examined for predictive power on the final CO2 results. Retrieval algorithms are frequently expensive to run, and wasted effort defeats requirements and expends needless resources. This algorithm could be used to help predict and filter unneeded runs in any computationally expensive regime. Traditional methods such as the Fischer discriminant analysis and decision trees can attempt to predict whether a sounding will be properly processed. However, this work sought to detect a subsection of the dimensional space that can be simply filtered out to eliminate unwanted runs. LDAs (linear discriminant analyses) and other systems examine the entire data and judge a "best fit," giving equal weight to complex and problematic regions as well as simple, clear-cut regions. In this implementation, a genetic space of "left" and "right" thresholds outside of which all data are rejected was defined. These left/right pairs are created for each of the 50 input dimensions. A genetic algorithm then runs through countless potential filter settings using a JPL computer cluster, optimizing the tossed-out data s yield (proper vs. improper run removal) and number of points tossed. This solution is robust to an arbitrary decision boundary within the data and avoids the global optimization problem of whole-dataset fitting using LDA or decision trees. It filters out runs that would not have produced useful CO2 values to save needless computation. This would be an algorithmic preprocessing improvement to any computationally expensive system.

Mandrake, Lukas

2013-01-01

410

In this paper 1 we describe a genetic algorithm capable of evolving large programs by exploiting two new genetic  

E-print Network

. The schema theorem (Holland, 1975), often called the Fundamental Theorem of Genetic Algorithms, illus­ tratesAbstract In this paper 1 we describe a genetic algorithm capable of evolving large programs; Nowlan & Hinton, 1991). In this paper we describe a genetic algorithm which is capable of evolving large

Pollack, Jordan B.

411

Optimizing models based OPC fragmentation using genetic algorithms  

NASA Astrophysics Data System (ADS)

Models Based Optical Proximity Correction (MBOPC) is used extensively in the semiconductor industry to achieve robust pattern fidelity in modern lithographic processes. Much of the complexity in OPC algorithms is handled by advanced commercial software packages. These packages give users the ability to set many parameters in the OPC code decks which are used to customize the recipes for specific design styles and manufacturing process settings. Some of the most important parameters in traditional OPC recipes are the fragmentation rules, which determine how edges of polygons are fragmented in a traditional edge-based correction algorithm. It is important to find settings which can deliver good results on a wide variety of complex layout styles. One approach to setting these parameters is through a Design of Experiments (DOE) approach where many different settings are tested in a systematic fashion, in an attempt to find appropriate fragmentation rules for a wide variety of layouts. This is a very straight-forward and powerful technique, but it can be very computationally expensive, particularly as the number of independent variables becomes large. In this paper we examine the usefulness of Genetic Algorithm (GA) optimization techniques for setting the fragmentation parameters. Our work is focused on using GAs to tune parameters rather than on core algorithms used in mask data correction. We use challenging metal layout patterns and optimize fragmentation rules to try to minimize residual edge placement errors, while trying to generate fragmentation that does not result in excessive runtime, or mask manufacturing challenges.

Dipaola, Domenico A.; Stobert, Ian

2008-10-01

412

To combine steady-state genetic algorithm and ensemble learning for data clustering  

Microsoft Academic Search

This paper proposes a data clustering algorithm that combines the steady-state genetic algorithm and the ensemble learning method, termed as genetic-guided clustering algorithm with ensemble learning operator (GCEL). GCEL adopts the steady-state genetic algorithm to perform the search task, but replaces its traditional recombination operator with an ensemble learning operator. Therefore, GCEL can avoid the problems of clustering invalidity and

Yi Hong; Sam Kwong

2008-01-01

413

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

414

Evolving retrieval algorithms with a genetic programming scheme  

NASA Astrophysics Data System (ADS)

The retrieval of scene properties (surface temperature, material type, vegetation health, etc.) from remotely sensed data is the ultimate goal of many earth observing satellites. The algorithms that have been developed for these retrievals are informed by physical models of how the raw data were generated. This includes models of radiation as emitted and/or reflected by the scene, propagated through the atmosphere, collected by the optics, detected by the sensor, and digitized by the electronics. To some extent, the retrieval is the inverse of this 'forward' modeling problem. But in contrast to this forward modeling, the practical task of making inferences about the original scene usually requires some ad hoc assumptions, good physical intuition, and a healthy dose of trial and error. The standard MTI data processing pipeline will employ algorithms developed with this traditional approach. But we will discuss some preliminary research on the use of a genetic programming scheme to 'evolve' retrieval algorithms. Such a scheme cannot compete with the physical intuition of a remote sensing scientist, but it may be able to automate some of the trial and error. In this scenario, a training set is used, which consists of multispectral image data and the associated 'ground truth;' that is, a registered map of the desired retrieval quantity. The genetic programming scheme attempts to combine a core set of image processing primitives to produce an IDL (Interactive Data Language) program which estimates this retrieval quantity from the raw data.

Theiler, James P.; Harvey, Neal R.; Brumby, Steven P.; Szymanski, John J.; Alferink, Steve; Perkins, Simon J.; Porter, Reid B.; Bloch, Jeffrey J.

1999-10-01

415

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

416

Genetic Algorithms: A New Method for Neutron Beam Spectral Characterization  

SciTech Connect

A revolutionary new concept for solving the neutron spectrum unfolding problem using genetic algorithms (GAs) has recently been introduced. GAs are part of a new field of evolutionary solution techniques that mimic living systems with computer-simulated chromosome solutions that mate, mutate, and evolve to create improved solutions. The original motivation for the research was to improve spectral characterization of neutron beams associated with boron neutron capture therapy (BNCT). The GA unfolding technique has been successfully applied to problems with moderate energy resolution (up to 47 energy groups). Initial research indicates that the GA unfolding technique may well be superior to popular unfolding methods in common use. Research now under way at Kansas State University is focused on optimizing the unfolding algorithm and expanding its energy resolution to unfold detailed beam spectra based on multiple foil measurements. Indications are that the final code will significantly outperform current, state-of-the-art codes in use by the scientific community.

David W. Freeman

2000-06-04

417

Optimizing Optical Quantum Logic Gates using Genetic Algorithms  

E-print Network

We introduce the method of using an annealing genetic algorithm to the numerically complex problem of looking for quantum logic gates which simultaneously have highest fidelity and highest success probability. We first use the linear optical quantum nonlinear sign (NS) gate as an example to illustrate the efficiency of this method. We show that by appropriately choosing the annealing parameters, we can reach the theoretical maximum success probability (1/4 for NS) for each attempt. We then examine the controlled-z (CZ) gate as the first new problem to be solved. Our goal is to use this method to find the maximum success probability for a CZ gate while maintaining a fidelity of 0.9997. Since the purpose of our algorithm is to optimize a unitary matrix for quantum transformations, it could easily be applied to other areas of interest such as quantum optics and quantum sensors.

Wu, Zhanghan; Uskov, Dmitry; Lee, Hwang; Dowling, Jonathan P

2007-01-01

418

Optimizing Optical Quantum Logic Gates using Genetic Algorithms  

E-print Network

We introduce the method of using an annealing genetic algorithm to the numerically complex problem of looking for quantum logic gates which simultaneously have highest fidelity and highest success probability. We first use the linear optical quantum nonlinear sign (NS) gate as an example to illustrate the efficiency of this method. We show that by appropriately choosing the annealing parameters, we can reach the theoretical maximum success probability (1/4 for NS) for each attempt. We then examine the controlled-z (CZ) gate as the first new problem to be solved. We show results that agree with the highest known maximum success probability for a CZ gate (2/27) while maintaining a fidelity of 0.9997. Since the purpose of our algorithm is to optimize a unitary matrix for quantum transformations, it could easily be applied to other areas of interest such as quantum optics and quantum sensors.

Zhanghan Wu; Sean D. Huver; Dmitry Uskov; Hwang Lee; Jonathan P. Dowling

2007-08-10

419

Operational Optimal Ship Routing Using a Hybrid Parallel Genetic Algorithm  

E-print Network

Optimization of ship routing depends on several parameters, like ship and cargo characteristics, environmental factors, topography, international navigation rules, crew comfort etc. The complex nature of the problem leads to oversimplifications in analytical techniques, while stochastic methods like simulated annealing can be both time consuming and sensitive to local minima. In this work, a hybrid parallel genetic algorithm - estimation of distribution algorithm is developed in the island model, to operationally calculate the optimal ship routing. The technique, which is applicable not only to clusters but to grids as well, is very fast and has been applied to very difficult environments, like the Greek seas with thousands of islands and extreme micro-climate conditions.

O. T. Kosmas; D. S. Vlachos

2008-11-13

420

New evolutionary genetic algorithms for NP-complete combinatorial optimization problems  

Microsoft Academic Search

Evolutionary genetic algorithms have been proposed to solve NP-complete combinatorial optimization problems. A new crossover operator based on group theory has been created. Computational processes motivated by proposed evolutionary genetic algorithms were described as stochastic processes, using population dynamics and interactive markovian chains. The proposed algorithms were used in solving flowshop problems and an asymmetric traveling salesman problem. The experimental

Fam Quang Bac; V. L. Perov

1993-01-01

421

Using a genetic algorithm for 3-D inversion of gravity data in Fuerteventura (Canary Islands)  

Microsoft Academic Search

The use of genetic algorithms in geophysical inverse problems is a relatively recent development and offers many advantages in dealing with the non-linearity inherent in such applications. We have implemented a genetic algorithm to efficiently invert a set of gravity data. Employing several fixed density contrasts, this algorithm determines the geometry of the sources of the anomaly gravity field in

F. G. Montesinos; J. Arnoso; R. Vieira

2005-01-01

422

Analysis of the Numerical Effects of Parallelism on a Parallel Genetic Algorithm  

Microsoft Academic Search

This paper examines the effects of relaxed synchroniza- tion on both the numerical and parallel efficiency of paralle l genetic algorithms (GAs). We describe a coarse-grain geo- graphically structured parallel genetic algorithm. Our ex - periments provide preliminary evidence that asynchronous versions of these algorithms have a lower run time than syn- chronous GAs. Our analysis shows that this improvement

William E. Hart; Scott B. Baden; Richard K. Belew; Scott R. Kohn

1996-01-01

423

Genetic\\/quadratic search algorithm for plant economic optimizations using a process simulator  

Microsoft Academic Search

The genetic\\/quadratic search algorithm (GQSA) is a hybrid genetic algorithms (GA) for optimizing plant economics when a process simulator models the plant. By coupling a regular GA with an algorithm based upon a quadratic search, the required number of objective function evaluations for obtaining an acceptable solution decreases significantly in most cases. The GQSA combines advantages of GA and quadratic

Won-hyouk Jang; Juergen Hahn; Kenneth R. Hall

2005-01-01

424

Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm  

Microsoft Academic Search

This paper introduces ICET, a new algorithm for cost-sensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algo- rithm. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors.

Peter D. Turney

1995-01-01

425

Bitwise operations for GPU implementation of genetic algorithms  

Microsoft Academic Search

Research on the implementation of evolutionary algorithms in graphics processing units (GPUs) has grown in recent years since it significantly reduces the execution time of the algorithm. A relevant aspect, which has received little attention in the literature, is the impact of the memory space occupied by the population in the performance of the algorithm, due to limited capacity of

Martín Pedemonte; Enrique Alba; Francisco Luna

2011-01-01

426

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

427

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

428

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 PMID:19660135

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

2009-01-01

429

Seismic Velocity Inversion with Genetic Algorithms Sushil J. Louis Qinxue Chen  

E-print Network

Seismic Velocity Inversion with Genetic Algorithms Sushil J. Louis Qinxue Chen Genetic Adaptive Pullammanappallil OPTIM LLC. Reno, NV 89557 Abstract We use genetic algorithms to find geologically plausible sub. This inverse problem is fundamental to seismology. To determine the suitability and applicability of genetic

Louis, Sushil J.

430

Designing application-specific neural networks using the structured genetic algorithm  

Microsoft Academic Search

Presents a different type of genetic algorithm called the structured genetic algorithm (SGA) for the design of application-specific neural networks. The novelty of this new genetic approach is that it can determine the network structures and their weights solely by an evolutionary process. This is made possible for the SGA primarily due to its redundant genetic material and a gene

Dipankar Dasgupta; Douglas R. Mcgregor

1992-01-01

431

Matching Algorithms to Problems: An Experimental Test of the Particle Swarm and Some Genetic Algorithms  

E-print Network

Laboratory Washington, DC 20375-5337 spears@aic.nrl.navy.mil Abstract A multimodal problem generator was used researchers to design controlled experiments in which one or more properties of a class of problems can algorithm is an adaptive algorithm based on a social-psychological metaphor; a population of individuals

432

Structural control Architecture Optimization for 3-D Systems Using Advanced Multi-Objective Genetic Algorithms  

E-print Network

Gaussian (LQG) control algorithm is used as the active control strategy. The American Society of Civil Engineers (ASCE) control benchmark building definition is used to develop the building system model. The proposed gene manipulation genetic algorithm...

Cha, Young Jin

2010-01-14

433

Application of genetic algorithms in nonlinear heat conduction problems.  

PubMed

Genetic algorithms are employed to optimize dimensionless temperature in nonlinear heat conduction problems. Three common geometries are selected for the analysis and the concept of minimum entropy generation is used to determine the optimum temperatures under the same constraints. The thermal conductivity is assumed to vary linearly with temperature while internal heat generation is assumed to be uniform. The dimensionless governing equations are obtained for each selected geometry and the dimensionless temperature distributions are obtained using MATLAB. It is observed that GA gives the minimum dimensionless temperature in each selected geometry. PMID:24695517

Kadri, Muhammad Bilal; Khan, Waqar A

2014-01-01

434

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

435

A Hybrid Grouping Genetic Algorithm for Multiprocessor Scheduling  

NASA Astrophysics Data System (ADS)

This paper describes a hybrid grouping genetic algorithm for a multiprocessor scheduling problem, where a list of tasks has to be scheduled on identical parallel processors. Each task in the list is defined by a release date, a due date and a processing time. The objective is to minimize the number of processors used while respecting the constraints imposed by release dates and due dates. We have compared our hybrid approach with two heuristic methods reported in the literature. Computational results show the superiority of our hybrid approach over these two approaches. Our hybrid approach obtained better quality solutions in shorter time.

Singh, Alok; Sevaux, Marc; Rossi, André

436

Integrating GIS and genetic algorithms for automating land partitioning  

NASA Astrophysics Data System (ADS)

Land consolidation is considered to be the most effective land management planning approach for controlling land fragmentation and hence improving agricultural efficiency. Land partitioning is a basic process of land consolidation that involves the subdivision of land into smaller sub-spaces subject to a number of constraints. This paper explains the development of a module called LandParcelS (Land Parcelling System) that integrates geographical information systems and a genetic algorithm to automate the land partitioning process by designing and optimising land parcels in terms of their shape, size and value. This new module has been applied to two land blocks that are part of a larger case study area in Cyprus. Partitioning is carried out by guiding a Thiessen polygon process within ArcGIS and it is treated as a multiobjective problem. The results suggest that a step forward has been made in solving this complex spatial problem, although further research is needed to improve the algorithm. The contribution of this research extends land partitioning and space partitioning in general, since these approaches may have relevance to other spatial processes that involve single or multi-objective problems that could be solved in the future by spatial evolutionary algorithms.

Demetriou, Demetris; See, Linda; Stillwell, John

2014-08-01

437

An Evolved Wavelet Library Based on Genetic Algorithm  

PubMed Central

As the size of the images being captured increases, there is a need for a robust algorithm for image compression which satiates the bandwidth limitation of the transmitted channels and preserves the image resolution without considerable loss in the image quality. Many conventional image compression algorithms use wavelet transform which can significantly reduce the number of bits needed to represent a pixel and the process of quantization and thresholding further increases the compression. In this paper the authors evolve two sets of wavelet filter coefficients using genetic algorithm (GA), one for the whole image portion except the edge areas and the other for the portions near the edges in the image (i.e., global and local filters). Images are initially separated into several groups based on their frequency content, edges, and textures and the wavelet filter coefficients are evolved separately for each group. As there is a possibility of the GA settling in local maximum, we introduce a new shuffling operator to prevent the GA from this effect. The GA used to evolve filter coefficients primarily focuses on maximizing the peak signal to noise ratio (PSNR). The evolved filter coefficients by the proposed method outperform the existing methods by a 0.31?dB improvement in the average PSNR and a 0.39?dB improvement in the maximum PSNR.

Vaithiyanathan, D.; Seshasayanan, R.; Kunaraj, K.; Keerthiga, J.

2014-01-01

438

Hyperspectral image band selection based on genetic algorithm  

NASA Astrophysics Data System (ADS)

Optimum band selection for visual interpretation and classification is an interesting task in conventional remote sensing, and, as an effective means to mitigate the curse of dimensionality, which has assumed growing importance with the availability of hyperspectral remote sensing data. In determining three-channel combination for a informative display in an image-cube and determining feature combination for fast classification, band selection is regarded indispensable in hyperspectral remote sensing. When applied to data acquired from a hyperspectral sensor, which is usually with a set of hundreds of band, however, conventional band selection procedure, of any criterion, becomes not viable with respect to the particularly time consuming. To cope with this pitfall, a method based upon genetic algorithm is proposed in this paper. An experiment, with a 121 band data set, demonstrate the efficiency. For simplification, the algorithm is designed to choose a combination which produces the most informative visual result when used as the top color preference in an image- cube. With little modification in criterion, the algorithm can be used to select features for classification purpose. The corresponding result is also presented in this paper.

Ma, Jiping; Zheng, Zhaobao; Tong, Qingxi; Zheng, Lanfen; Zhang, Bin

2001-09-01

439

Optimization of an Antenna Array Using Genetic Algorithms  

NASA Astrophysics Data System (ADS)

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

Kiehbadroudinezhad, Shahideh; Kamariah Noordin, Nor; Sali, A.; Zainal Abidin, Zamri

2014-06-01

440

Induction of Linear Decision Trees with Real-Coded Genetic Algorithms and k-D Trees  

Microsoft Academic Search

\\u000a Although genetic algorithm-based decision tree algorithms are applied successfully in various classification tasks, their\\u000a execution times are quite long on large datasets. A novel decision tree algorithm, called Real-Coded Genetic Algorithm-based\\u000a Linear Decision Tree Algorithm with k-D Trees (RCGA-based LDT with kDT), is proposed. In the proposed algorithm, a k-D tree\\u000a is built when a new node of a linear

Ng Sai-cheong; Kwong-sak Leung

2005-01-01

441

Genetic Algorithms and Genetic Programming for Multiscale Modeling: Applications in Materials  

E-print Network

Genetic Algorithms and Genetic Programming for Multiscale Modeling: Applications in Materials FOR MULTISCALE MODELING: APPLICATIONS IN MATERIALS SCIENCE AND CHEMISTRY AND ADVANCES IN SCALABILITY BY KUMARA multiscale modeling is essential to advance both the science and synthesis in a wide array of fields

Fernandez, Thomas

442

Fuel management optimization using genetic algorithms and code independence  

SciTech Connect

Fuel management optimization is a hard problem for traditional optimization techniques. Loading pattern optimization is a large combinatorial problem without analytical derivative information. Therefore, methods designed for continuous functions, such as linear programming, do not always work well. Genetic algorithms (GAs) address these problems and, therefore, appear ideal for fuel management optimization. They do not require derivative information and work well with combinatorial. functions. The GAs are a stochastic method based on concepts from biological genetics. They take a group of candidate solutions, called the population, and use selection, crossover, and mutation operators to create the next generation of better solutions. The selection operator is a {open_quotes}survival-of-the-fittest{close_quotes} operation and chooses the solutions for the next generation. The crossover operator is analogous to biological mating, where children inherit a mixture of traits from their parents, and the mutation operator makes small random changes to the solutions.

DeChaine, M.D.; Feltus, M.A.

1994-12-31

443

Research of SoPC-based improved genetic algorithm on shortest path  

NASA Astrophysics Data System (ADS)

The shortest path problem is a classic problem and is unlikely to find an efficient algorithm for solving it directly. It is applied broadly in practice. Thus rapid and effective solving shortest path problem is very important application value in practice. Genetic Algorithm (GA) is a kind of heuristic global optimization search algorithm that simulates the biology evolutionary system. It is resolved efficiently by this improvement Genetic Algorithm. In this paper, a SoPC-based GA framework is proposed .The experiment results show that improved Genetic Algorithm enhances extremely in the same environment.

Ruan, Hang; Ren, Aifeng; Meng, Ming; Zhao, Wei; Luo, Ming

2011-10-01

444

Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm  

PubMed Central

This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749

Svecko, Rajko

2014-01-01

445

Generation of Compliant Mechanisms using Hybrid Genetic Algorithm  

NASA Astrophysics Data System (ADS)

Compliant mechanism is a single piece elastic structure which can deform to perform the assigned task. In this work, compliant mechanisms are evolved using a constraint based bi-objective optimization formulation which requires one user defined parameter ( ?). This user defined parameter limits a gap between a desired path and an actual path traced by the compliant mechanism. The non-linear and discrete optimization problems are solved using the hybrid Genetic Algorithm (GA) wherein domain specific initialization, two-dimensional crossover operator and repairing techniques are adopted. A bit-wise local search method is used with elitist non-dominated sorting genetic algorithm to further refine the compliant mechanisms. Parallel computations are performed on the master-slave architecture to reduce the computation time. A parametric study is carried out for ? value which suggests a range to evolve topologically different compliant mechanisms. The applied and boundary conditions to the compliant mechanisms are considered the variables that are evolved by the hybrid GA. The post-analysis of results unveils that the complaint mechanisms are always supported at unique location that can evolve the non-dominated solutions.

Sharma, D.; Deb, K.

2014-10-01

446

Internal Lattice Reconfiguration for Diversity Tuning in Cellular Genetic Algorithms  

PubMed Central

Cellular Genetic Algorithms (cGAs) have attracted the attention of researchers due to their high performance, ease of implementation and massive parallelism. Maintaining an adequate balance between exploitative and explorative search is essential when studying evolutionary optimization techniques. In this respect, cGAs inherently possess a number of structural configuration parameters that are able to sustain diversity during evolution. In this study, the internal reconfiguration of the lattice is proposed to constantly or adaptively control the exploration-exploitation trade-off. Genetic operators are characterized in their simplest form since algorithmic performance is assessed on implemented reconfiguration mechanisms. Moreover, internal reconfiguration allows the adjacency of individuals to be maintained. Hence, any improvement in performance is only a consequence of topological changes. Two local selection methods presenting opposite selection pressures are used in order to evaluate the influence of the proposed techniques. Problems ranging from continuous to real world and combinatorial are tackled. Empirical results are supported statistically in terms of efficiency and efficacy. PMID:22859973

Morales-Reyes, Alicia; Erdogan, Ahmet T.

2012-01-01

447

Genetic algorithms for geophysical parameter inversion from altimeter data  

NASA Astrophysics Data System (ADS)

A new approach for inverting several geophysical parameters at the same time from altimeter and marine data by implementing genetic algorithms (GAs) is presented. These original techniques of optimization based on non-deterministic rules simulate the evolution of a population of candidate solutions for a given objective function to minimize. They offer a robust and efficient alternative to gradient techniques for non-linear parameter inversion. Here genetic algorithms are used for solving a discrete gravity problem of data associated with an undersea relief, to retrieve seven parameters at the same time: the elastic thickness, the mean ocean depth, the seamount location (longitude/latitude), its amplitude, radius and density from its observed gravity/geoid signature. This approach was also successfully used to adjust lithosphere parameters in the real case of the Rarotonga seamount [21.2°S 159.8°W] in the Southern Cook Islands region, where GA simulations provided robust estimates of these seven parameters. The GA found very realistic values for the mean ocean depth and the seamount amplitude and the precise geographical location of Rarotonga Island. Moreover, the values of elastic thickness (~14-15km) and seamount density (~2850-2870kgm-3) estimated by the GA are consistent with the ones proposed in earlier studies.

Ramillien, Guillaume

2001-11-01

448

Estimating time to full uterine cervical dilation using genetic algorithm.  

PubMed

The objectives of this study were to provide new parameters to better understand labor curves, and to provide a model to predict the time to full cervical dilation (CD). We studied labor curves using the retrospective records of 594 nulliparas, including at term, spontaneous labor onset, and singleton vertex deliveries of normal birth weight infants. We redefined the parameters of Friedman's labor curve, and applied a three-parameter model to the labor curve with a logistic model using the genetic algorithm and the Newton-Raphson method to predict the time necessary to reach full CD. The genetic algorithm is more effective than the Newton-Raphson method for modeling labor progress, as demonstrated by its higher accuracy in predicting the time to reach full CD. In addition, we predicted the time (11.4 hours) to reach full CD using the logistic labor curve using the mean parameters (the power of CD = 0.97 cm/hours, a midpoint of the active phase = 7.60 hours, and the initial CD = 2.11 cm). Our new parameters and model can predict the time to reach full CD, which can aid in the forecasting of prolonged labor and the timing of interventions, with the end goal being normal vaginal birth. PMID:22892163

Hoh, Jeong-Kyu; Cha, Kyung-Joon; Park, Moon-Il; Ting Lee, Mei-Ling; Park, Young-Sun

2012-08-01

449

A genetic algorithm approach to recognition and data mining  

SciTech Connect

We review here our use of genetic algorithm (GA) and genetic programming (GP) techniques to perform {open_quotes}data mining,{close_quotes} the discovery of particular/important data within large datasets, by finding optimal data classifications using known examples. Our first experiments concentrated on the use of a K-nearest neighbor algorithm in combination with a GA. The GA selected weights for each feature so as to optimize knn classification based on a linear combination of features. This combined GA-knn approach was successfully applied to both generated and real-world data. We later extended this work by substituting a GP for the GA. The GP-knn could not only optimize data classification via linear combinations of features but also determine functional relationships among the features. This allowed for improved performance and new information on important relationships among features. We review the effectiveness of the overall approach on examples from biology and compare the effectiveness of the GA and GP.

Punch, W.F.; Goodman, E.D.; Min, Pei [Michigan State Univ., East Lansing, MI (United States)] [and others

1996-12-31

450

Learning lung nodule similarity using a genetic algorithm  

NASA Astrophysics Data System (ADS)

The effectiveness and efficiency of content-based image retrieval (CBIR) can be improved by determining an optimal combination of image features to use in determining similarity between images. This combination of features can be optimized using a genetic algorithm (GA). Although several studies have used genetic algorithms to refine image features and similarity measures in CBIR, the present study is the first to apply these techniques to medical image retrieval. By implementing a GA to test different combinations of image features for pulmonary nodules in CT scans, the set of image features was reduced to 29 features from a total of 63 extracted features. The performance of the CBIR system was assessed by calculating the average precision across all query nodules. The precision values obtained using the GA-reduced set of features were significantly higher than those found using all 63 image features. Using radiologist-annotated malignancy ratings as ground truth resulted in an average precision of 85.95% after 3 images retrieved per query nodule when using the feature set identified by the GA. Using computer-predicted malignancy ratings as ground truth resulted in an average precision of 86.91% after 3 images retrieved. The results suggest that in the absence of radiologist semantic ratings, using computer-predicted malignancy as ground truth is a valid substitute given the closeness of the two precision values.

Seitz, Kerry A., Jr.; Giuca, Anne-Marie; Furst, Jacob; Raicu, Daniela

2012-03-01

451

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

452

Randomized Algorithms  

Microsoft Academic Search

The last decade has witnessed a tremendous growth in the area of randomized algorithms.During this period, randomized algorithms went from being a tool in computational number theory to finding widespread application in many types of algorithms. Two benefits of randomization have spearheaded this growth: simplicity and speed. For many applications, a randomized algorithm is the simplest algorithm available, or the

Rajeev Motwani; Prabhakax Raghavan

1995-01-01

453

Exploring a Financial Product Model with a Two-Population Genetic Algorithm  

E-print Network

Exploring a Financial Product Model with a Two-Population Genetic Algorithm Steven O. Kimbrough two-population genetic algorithm (GA) has been remarkably successful in finding good, feasible for presenting this case study is that we wish to explore the effectiveness of the two- population genetic

Kimbrough, Steven Orla

454

Journal of American Science on Information and Technology On using genetic algorithms for multimodal  

E-print Network

Journal of American Science on Information and Technology On using genetic algorithms ____________________________________________________________________________________________________ Abstract This paper presents a genetic relevance optimisation process performed in an information retrieval : Information retrieval , multiple query evaluation, genetic algorithm, niching hal-00359529,version1-8Feb2009

Paris-Sud XI, Université de

455

A genetic algorithm approach to the integrated inventory-distribution problem  

E-print Network

author. Email: maged@usc.edu We introduce a new genetic algorithm (GA) approach for the integratedA genetic algorithm approach to the integrated inventory- distribution problem TAMER F. ABDELMAGUID inventory distribution problem (IIDP). We present the developed genetic representation and use a randomized

Dessouky, Maged

456

Applied Cloning Techniques for a Genetic Algorithm Used in Evolvable Hardware Design  

E-print Network

Applied Cloning Techniques for a Genetic Algorithm Used in Evolvable Hardware Design Viet C. Trinh of the new method provides good insight on the application of cloning to this domain. Keywords: Genetic into account an artificial biological principle, namely cloning, and implements a genetic algorithm based on it

Wu, Annie S.

457

Which First-Order Logic Clauses Can Be Learned Using Genetic Algorithms?  

Microsoft Academic Search

\\u000a In this paper we present and prove both negative and positive theoretical results concerning the representation and evaluation\\u000a of first-order logic clauses using genetic algorithms. Over the last few years, a few approaches have been proposed aiming\\u000a to combine genetic and evolutionary computation (EC) with inductive logic programming (ILP). The underlying rationale is that\\u000a evolutionary algorithms, such as genetic algorithms,

Flaviu Adrian Marginean

2003-01-01

458

The control of adaptive antenna arrays with genetic algorithms using dominance and diploidy  

Microsoft Academic Search

Adaptation of an antenna array controlled by digital-phase shifters using an advanced operator genetic algorithm is demonstrated. The genetic algorithm continuously optimizes the antenna's received signal-to-noise-plus-interference ratio (SINR) (Applebaum (1976) criterion) under changing interference conditions. Unlike earlier attempts to control adaptive array antennas using evolutionary optimization techniques, the current study uses a genetic algorithm with a population composed of individuals

Daniel S. Weile; Eric Michielssen

2001-01-01

459

Design of two-dimensional recursive filters by using a novel genetic algorithm  

Microsoft Academic Search

In this paper, a novel genetic algorithm, which is called a hybrid Taguchi-genetic algorithm (HTGA), is proposed to solve the design problem of two-dimensional (2D) recursive digital filters. The HTGA approach is a method of combining the traditional genetic algorithm (TGA), which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimum offspring. The Taguchi

Jinn-tsong Tsai; Jyh-horng Chou; Tung-kuan Liu; Chien-han Chen

2005-01-01

460

Two Step Template Matching Method with Correlation Coefficient and Genetic Algorithm  

Microsoft Academic Search

\\u000a This paper presents a rotation invariant template matching method based on two step matching process, cross correlation and\\u000a genetic algorithm. In order to improve the matching performance, the traditional normalized correlation coefficient method\\u000a is combined with genetic algorithm. Normalized correlation coefficient method computes probable local position of the template\\u000a in the scene image. And genetic algorithm computes global position and

Gyeongdong Baek; Sungshin Kim

2009-01-01

461

Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling  

NASA Astrophysics Data System (ADS)

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

Abrahart, R. J.

2004-05-01

462

A Taxation attribute reduction based on genetic algorithm and rough set theory  

Microsoft Academic Search

Selection of taxation attributes is one difficult question in analyzing the sources of taxation. This paper introduces genetic-algorithm-based rough set attribute reduction algorithm into the job of taxation attribute reduction. By referring to the concept of dependability in rough set, this method optimizes the configuration of fitness function, improves the convergence of original algorithm and changes the limitation of current

Xu Linzhang; Han Zhen; Zhang Yanning

2008-01-01

463

Hybrid genetic algorithms for parameter identification of a hysteresis model of magnetostrictive actuators  

Microsoft Academic Search

In this paper, we present an improved hysteresis model for magnetostrictive actuators. To obtain optimal parameters of the model, we study two distinct hybrid strategies: namely, employing a gradient algorithm as a local search operation of a genetic algorithm (GA), and taking the best individual of a GA as the initial value of a gradient algorithm. Here, two different gradient

Jiaju Zheng; Shuying Cao; Hongli Wang; Wenmei Huang

2007-01-01

464

The PID prediction control system using particle swarm optimization and genetic algorithms  

Microsoft Academic Search

In this paper, the particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are employed to optimize the parameters of PID algorithm in order to improve the performance of PID control system. Moreover, we propose the grey model based on grey system theory to combine with PID control to establish the PID prediction control system. The proposed control system can

Guo-Dong Li; Chen-Hong Wang; Shiro Masuda; Daisuke Yamaguchi; Masatake Nagai

2009-01-01

465

An Enhanced Genetic Algorithm for DNA Sequencing by Hybridization with Positive and Negative Errors  

Microsoft Academic Search

This paper describes a genetic algorithm for the DNA se- quencing problem. The algorithm allows the input spectrum to contain both positive and negative errors as could be expected from a hybridiza- tion experiment. The main features of the algorithm include a prepro- cessing step that reduces the size of the input spectrum and an efficient local optimization. In experimental

Thang Nguyen Bui; Waleed A. Youssef

2004-01-01

466

Hyperspectral Feature Extraction using Selective PCA based on Genetic Algorithm with Subgroups  

Microsoft Academic Search

Feature selection and extraction are important applications of hyperspectral images. In this paper, a new algorithm, selective principal component analysis based on genetic algorithm with subgroups (SPCA-GAS) is proposed. This algorithm has the distinct characteristic of combining feature selection and extraction together via the introduction of subgroup concept. SPCA-GAS tries to solve the problem of how many and which bands

Liu Ying; Gu Yanfeng; Zhang Ye

2006-01-01

467

A hybrid of genetic algorithm and particle swarm optimization for recurrent network design  

Microsoft Academic Search

An evolutionary recurrent network which automates the design of recurrent neural\\/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover

Chia-feng Juang

2004-01-01

468

AN EXTENDED AKERS GRAPHICAL METHOD WITH A BIASED RANDOM-KEY GENETIC ALGORITHM FOR JOB-SHOP SCHEDULING  

E-print Network

AN EXTENDED AKERS GRAPHICAL METHOD WITH A BIASED RANDOM-KEY GENETIC ALGORITHM FOR JOB-SHOP.F. Gonçalves and M.G.C. Resende, "An extended Akers graphical method with a biased random-key genetic algorithm algorithm; Biased random-key genetic algorithm; Heuris- tics; Random keys, Graphical approach. Supported

Resende, Mauricio G. C.

469

Fuzzy control with genetic algorithm in a batch bioreactor.  

PubMed

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

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

2013-12-01

470

2-D migration velocity estimation using a genetic algorithm  

NASA Astrophysics Data System (ADS)

We address the problem of velocity estimation in heterogeneous media using a combination of nonlinear inversion and migration velocity analysis. In velocity estimation, the travel time information in seismic reflection data are nonlinearly related to the velocity perturbations in the subsurface. By taking a functional of seismic traces, the migrated data themselves, we define a misfit criterion which greatly reduces the oscillatory nature of the objective function. Migration is inherently a smoothing process; it collapses diffractions, focuses reflected energy and suppresses random noise. We use the lateral consistency of reflectors after migration as a measure of model misfit. If we compare one migrated shot record with an adjacent record, the misfit will be only slightly affected by large velocity variations even though reflectors may show large errors in depth positioning. We search for global minima of the objective function thus defined, using a genetic algorithm (GA) and a linearized inversion scheme. We illustrate the techniques and results from the algorithms by applying them to a realistic scale synthetic data set. The success of a linearized scheme depends strongly on the starting model, while GA does not depend on the choice of the initial population of models.

Jervis, Michael; Stoffa, Paul L.; Sen, Mrinal K.

1993-07-01

471

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

472

Primary chromatic aberration elimination via optimization work with genetic algorithm  

NASA Astrophysics Data System (ADS)

Chromatic Aberration plays a part in modern optical systems, especially in digitalized and smart optical systems. Much effort has been devoted to eliminating specific chromatic aberration in order to match the demand for advanced digitalized optical products. Basically, the elimination of axial chromatic and lateral color aberration of an optical lens and system depends on the selection of optical glass. According to reports from glass companies all over the world, the number of various newly developed optical glasses in the market exceeds three hundred. However, due to the complexity of a practical optical system, optical designers have so far had difficulty in finding the right solution to eliminate small axial and lateral chromatic aberration except by the Damped Least Squares (DLS) method, which is limited in so far as the DLS method has not yet managed to find a better optical system configuration. In the present research, genetic algorithms are used to replace traditional DLS so as to eliminate axial and lateral chromatic, by combining the theories of geometric optics in Tessar type lenses and a technique involving Binary/Real Encoding, Multiple Dynamic Crossover and Random Gene Mutation to find a much better configuration for optical glasses. By implementing the algorithms outlined in this paper, satisfactory results can be achieved in eliminating axial and lateral color aberration.

Wu, Bo-Wen; Liu, Tung-Kuan; Fang, Yi-Chin; Chou, Jyh-Horng; Tsai, Hsien-Lin; Chang, En-Hao

2008-09-01

473

Development of a genetic algorithm for molecular scale catalyst design  

SciTech Connect

A genetic algorithm has been developed to determine the optimal design of a two-component catalyst for the diffusion-limited A + B AB{up_arrow} reaction in which each species is adsorbed specifically on one of two types of sites. Optimization of the distribution of catalytic sites on the surface is achieved by means of an evolutionary algorithm which repeatedly selects the more active surfaces from a population of possible solutions leading to a gradual improvement in the activity of the catalyst surface. A Monte Carlo simulation is used to determine the activity of each of the catalyst surfaces. It is found that for a reacting mixture composed of equal amounts of each component the optimal active site distribution is that of a checkerboard, this solution being approximately 25% more active than a random site distribution. Study of a range of reactant compositions has shown the optimal distribution of catalytically active sites to be dependent on the composition of the ratio of A to B in the reacting mixture. The potential for application of the optimization method introduced here to other catalysts systems is discussed. 27 refs., 7 figs.

McLeod, A.S.; Gladden, L.F.; Johnston, M.E. [Univ. of Cambridge (United Kingdom)] [Univ. of Cambridge (United Kingdom)

1997-04-01

474

Optimization Algorithms  

Microsoft Academic Search

\\u000a The right choice of an optimization algorithm can be crucially important in finding the right solutions for a given optimization\\u000a problem. There exist a diverse range of algorithms for optimization, including gradient-based algorithms, derivative-free\\u000a algorithms and metaheuristics. Modern metaheuristic algorithms are often nature-inspired, and they are suitable for global\\u000a optimization. In this chapter, we will briefly introduce optimization algorithms such

Xin-She Yang

475

A parallel multi-population genetic algorithm for a constrained  

E-print Network

. In this algorithm, projections of cut items were made onto both the horizontal and vertical edges of the stock to be placed into a larger stock rectangle so as to maximize the value of the rectan- gles packed the quality of the solutions and the eectiveness of the proposed algorithm. Keywords: Packing, cutting, two

Resende, Mauricio G. C.

476

Revisiting Bremermann's genetic algorithm. I. Simultaneous mutation of all parameters  

Microsoft Academic Search

Hans Bremermann was one of the pioneers of evolutionary computation. Many of his early suggestions for designing evolutionary algorithms anticipated future inventions, including scaling mutations to be inversely proportional to the number of parameters in the problem, as well as many forms of recombination. This paper explores the gain in performance that occurs when Bremermann's original evolutionary algorithm (H.J. Bremermann

David B. Fogel; Russell W. Anderson

2000-01-01

477

Evolving Retrieval Algorithms with a Genetic Programming Scheme  

E-print Network

.) from remotely sensed data is the ultimate goal of many earth observing satellites. The algorithms The importance of \\end-to-end simulation" is often emphasized 1{3 in the development and assessment of science retrieval algorithms. The front-end of an end-to-end simulation takes scene properties as given

Fernandez, Thomas

478

Maximizing microbial perchlorate degradation using a genetic algorithm: consortia optimization.  

PubMed

Microorganisms in consortia perform many tasks more effectively than individual organisms and in addition grow more rapidly and in greater abundance. In this work, experimental datasets were assembled consisting of all possible selected combinations of perchlorate reducing strains of microorganisms and their perchlorate degradation rates were evaluated. A genetic algorithm (GA) methodology was successfully applied to define sets of microbial strains to achieve maximum rates of perchlorate degradation. Over the course of twenty generations of optimization using a GA, we saw a statistically significant 2.06 and 4.08-fold increase in average perchlorate degradation rates by consortia constructed using solely the perchlorate reducing bacteria (PRB) and by consortia consisting of PRB and accompanying organisms that did not degrade perchlorate, respectively. The comparison of kinetic rates constant in two types of microbial consortia additionally showed marked increases. PMID:23229741

Kucharzyk, Katarzyna H; Soule, Terence; Hess, Thomas F

2013-09-01

479

Strawberry Maturity Neural Network Detectng System Based on Genetic Algorithm  

NASA Astrophysics Data System (ADS)

The quick and non-detective detection of agriculture product is one of the measures to increase the precision and productivity of harvesting and grading. Having analyzed H frequency of different maturities in different light intensities, the results show that H frequency for the same maturity has little influence in different light intensities; Under the same light intensity, three strawberry maturities are changing in order. After having confirmed the H frequency section to distinguish the different strawberry maturity, the triplelayer feed-forward neural network system to detect strawberry maturity was designed by using genetic algorithm. The test results show that the detecting precision ratio is 91.7%, it takes 160ms to distinguish one strawberry. Therefore, the online non-detective detecting the strawberry maturity could be realized.

Xu, Liming

480

Tuning of a neuro-fuzzy controller by genetic algorithm.  

PubMed

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 membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance. PMID:18252294

Seng, T L; Bin Khalid, M; Yusof, R

1999-01-01

481

Optimization of activated sludge designs using genetic algorithms.  

PubMed

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

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

2002-01-01

482

An Implementation of Intrusion Detection System Using Genetic Algorithm  

E-print Network

Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99 benchmark datase...

Hoque, Mohammad Sazzadul; Bikas, Md Abu Naser; 10.5121/ijnsa.2012.4208

2012-01-01

483

A fast and elitist multiobjective genetic algorithm: NSGA-II  

Microsoft Academic Search

Multi-objective evolutionary algorithms (MOEAs) that use non-dominated\\u000d\\u000a\\u0009sorting and sharing have been criticized mainly for: (1) their O(MNsup3\\/sup)\\u000d\\u000a\\u0009computational complexity (where M is the number of objectives and\\u000d\\u000a\\u0009N is the population size); (2) their non-elitism approach; and (3)\\u000d\\u000a\\u0009the need to specify a sharing parameter. In this paper, we suggest\\u000d\\u000a\\u0009a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated\\u000d\\u000a\\u0009Sorting Genetic

Kalyanmoy Deb; Amrit Pratap; Sameer Agarwal; T. Meyarivan

2002-01-01

484

Evolving connectivity between genetic oscillators and switches using evolutionary algorithms.  

PubMed

Although hypothesised there has been little investigation into how complex gene regulatory networks can evolve from simple regulatory motifs through modularisation, duplication and specialisation processes. In order to simulate natural evolution in a computational environment we evolve the connection between a genetic oscillator and a toggle switch motif using an evolutionary algorithm. We observe a connectivity preference between the motifs that is dependent on the coupling arrangement rather than on objective set-up. In addition, our results indicate the existence of a threshold in the connection parameters for the resulting dynamics for a specific coupling arrangement and objective set-up. We demonstrate that simple motifs can successfully be coupled through artificial evolution to form more complex, modular regulatory networks. These findings support, in principle, the above-mentioned hypothesis on evolutionary mechanisms in biological systems. PMID:23796178

Thomas, Spencer Angus; Jin, Yaochu

2013-06-01

485

A fast and precise genetic algorithm for a non-linear fitting problem  

NASA Astrophysics Data System (ADS)

Fitting procedures are currently used in a large set of computational problems and several algorithms have been developed. However, a complication appears when the fitting function is non-linear and non-lineariable. In this case, a Marquardt-Levenberg procedure is generally used, but it often requires interactions with the user. Here a new method is proposed which is based on a genetic algorithm technique. This kind of algorithm allows fitting in a completely automatic mode, without any manipulation over the fitting function. The algorithm developed is generally faster and more precise than traditional genetic algorithms reported in the literature. Its performances are comparable to those in the Marquardt-Levenberg algorithm technique. It has been developed as fitting method for measurements of X-ray tube response. Fitting this response is very important to avoid any patient injuries. The results obtained are reported here and compared to other genetic algorithm implementations, as well as a Marquardt-Levenberg procedure.

Brunetti, Antonio

2000-02-01

486

Propeller performance analysis and multidisciplinary optimization using a genetic algorithm  

NASA Astrophysics Data System (ADS)

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

Burger, Christoph

487

Genetic Algorithms, Pulsar Planets, and Ionized Interstellar Microturbulence  

NASA Astrophysics Data System (ADS)

We probe the intense microturbulence in the Galactic center and the radio-wave scattering it generates by analyzing observations of extragalactic sources, OH and H2O masers, and free-free emission. The region responsible for the enhanced, anisotropic angular broadening of Sgr A* and nearby OH masers is within 150 pc of the Galactic center and has an angular radius ? 1o. The enhanced scattering probably occurs in the interface regions between 107 K gas and molecular clouds and is a manifestation of the energetic processes occurring in the Galactic center. Radio scattering measurements are also used to probe turbulent gas toward the Galactic anticenter. Ionized gas at Galactocentric distances ~50 kpc is suggested by absorption lines in quasar spectra, the appearance of the H I disks of nearby galaxies, and models for low-redshift quasar absorption systems and Galactic 'fountains.' We conducted multifrequency, Very Long Baseline Array (VLBA) observations on twelve extragalactic sources in order to measure their scattering sizes. Seven sources are at | b| < 1o and their lines of sight potentially probe path lengths ~>50 kpc through the disk. We find that the ionized disk is unwarped, has an extent of ?20 kpc, and traces the extent of massive star formation in the outer Galaxy. Planetary companions to neutron stars are challenging to recognize amid the several processes that contribute to pulsar arrival time data. We use a genetic algorithm to search for planetary companions to pulsars. Genetic algorithms are an optimization method that uses biological-like concepts such as survival of the fittest, mutation, and chromosome exchange. The algorithm searches parameter space in the same way that life finds optimal niches in the biological environment-incremental rewarding of successful variations. Fitting for Keplerian orbits requires a search through four non-linear parameters per planet and is especially difficult if there is a large range of planetary masses and orbital periods. We find that the GA is more efficient and more accurate than the downhill simplex and simulated annealing. We confirm the presence of a second planetary companion to PSR B0329+54 and identify possible companions to B1911-04 and B1929+10.

Lazio, T. Joseph W.

1997-10-01

488

Optimizing genetic operator rates using a markov chain model of genetic algorithms  

Microsoft Academic Search

This work is concerned with proposing a robust framework for optimizing operator rates of simple Genetic Algorithms (GAs) during a GA run. The suggested framework is built upon a formerly proposed GA Markov chain model to estimate the optimal values of the operator rates based on the time and the current state of the evolution. Though the proposed framework has

Fatemeh Vafaee; György Turán; Peter C. Nelson

2010-01-01

489

Genetic algorithm involving coevolution mechanism to search for effective genetic information  

Microsoft Academic Search

A new genetic algorithm which exploits an idea of “coevolution” is proposed. The proposed method consists of two GAs: Host GA and Parasite GA. The Host GA searches for the solutions, and these two GAs are closely related to each other. The Parasite GA plays an important role in searching for useful schemata in the Host GA. Furthermore, two methods

Hisashi Handa; Norio Baba; Osamu Katai; Tetsuo Sawaragi; Tadashi Horiuchi

1997-01-01

490

An air traffic flow management method based on mixed genetic algorithms  

NASA Astrophysics Data System (ADS)

With the air traffic congest problem becoming more and more severe, the study of air traffic flow management is more and more important. According to the character of air traffic flow management, the author analyzed the heuristic method and genetic algorithms, later put this two method together and give a new method of air traffic flow management-mixture genetic algorithms, It has global convergence, the simulation result demonstrates that the presented algorithm is effective.

Fu, Ying

2009-12-01

491

Research on immune genetic algorithm for solving the job-shop scheduling problem  

Microsoft Academic Search

To solve the job-shop scheduling problem more effectively, a method based on a novel scheduling algorithm named immune genetic\\u000a algorithm (IGA) was proposed. In this study, the framework of IGA was presented via combining the immune theory and the genetic\\u000a algorithm. The encoding scheme based on processes and the adaptive probabilities of crossover and mutation were adopted, while\\u000a a modified

Xiao-dong Xu; Cong-xin Li

2007-01-01

492

Induction of Quadratic Decision Trees using Genetic Algorithms and k-D Trees  

Microsoft Academic Search

Genetic Algorithm-based Quadratic Decision Tree (GA-based QDT) has been applied successfully in various classification problems with non-linear class boundaries. However, the execution time of GA-based QDT is quite long. In this paper, a new version of GA-based QDT, called Genetic Algorithm-based Quadratic Decision Tree with k-D Tree (GA-based QDT with k-D Tree), is proposed. In the proposed algorithm, a k-D

SAI-CHEONG NG; KWONG-SAK LEUNG

493

A hybrid genetic algorithm \\/ decision tree approach for coping with unbalanced classes  

Microsoft Academic Search

This paper proposes a new approach for coping with the problem of unbalanced classes, where some class(es) is(are) much less frequent than the other(s). The proposed approach is a hybrid genetic algorithm \\/ decision tree system. The genetic algorithm acts as a wrapper, using the output of a decision tree algorithm (the state-of-the-art C5.0) to compute the fitness of population

Deborah R. Carvalho; Bráulio C. Ávila; Alex A. Freitas; Imaculada Conceição

494

The Applications of Genetic Algorithms in Stock Market Data Mining Optimisation  

E-print Network

, NSW 2007, Australia Capital Market CRC, Sydney NSW 2000, Australia Abstract In stock marketThe Applications of Genetic Algorithms in Stock Market Data Mining Optimisation Li Lin, Longbing: Technical trading rule; Genetic Algorithm; sub-domain; Parameter combination; 1 Introduction In stock market

Cao, Longbing

495

Genetic Algorithms Based on Primal-Dual Chromosomes for Royal Road Functions  

E-print Network

Genetic Algorithms Based on Primal-Dual Chromosomes for Royal Road Functions SHENGXIANG YANG on a pair of chromosomes that are primal-dual to each other in the sense of Hamming distance in genotype. We functions for different performance measures. Key-Words: Genetic algorithm, primal-dual chromosomes, schema

Yang, Shengxiang

496

Genetic algorithms – A new technique for solving the neutron spectrum unfolding problem  

Microsoft Academic Search

A new technique utilizing genetic algorithms has been applied to the Bonner sphere neutron spectrum unfolding problem. Genetic algorithms are part of a relatively new field of “evolutionary” solution techniques that mimic living systems with computer-simulated “chromosome” solutions. Solutions mate and mutate to create better solutions. Several benchmark problems, considered representative of radiation protection environments, have been evaluated using the

David W. Freeman; D. Ray Edwards; Albert E. Bolon

1999-01-01

497

Genetic algorithms - A new technique for solving the neutron spectrum unfolding problem  

Microsoft Academic Search

A new technique utilizing genetic algorithms has been applied to the Bonner sphere neutron spectrum unfolding problem. Genetic algorithms are part of a relatively new field of ``evolutionary'' solution techniques that mimic living systems with computer-simulated ``chromosome'' solutions. Solutions mate and mutate to create better solutions. Several benchmark problems, considered representative of radiation protection environments, have been evaluated using the

D. W. Freeman; D. Ray Edwards; A. E. Bolon

1999-01-01

498

Genetic algorithms for adaptive real-time control in space systems  

NASA Technical Reports Server (NTRS)

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

Vanderzijp, J.; Choudry, A.

1988-01-01

499

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

500

New knowledge-based genetic algorithm for excavator boom structural optimization  

NASA Astrophysics Data System (ADS)

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Hua, Haiyan; Lin, Shuwen

2014-03-01