These are representative sample records from Science.gov related to your search topic.
For comprehensive and current results, perform a real-time search at Science.gov.
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  

Microsoft Academic Search

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

Kumara Sastry; David Goldberg; Graham Kendall

3

A genetic algorithm tutorial  

Microsoft Academic Search

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

DARRELL WHITLEY

1993-01-01

4

Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms  

E-print Network

Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms #12;Foundations Algorithm Components Numerical Optimization Genetic Programming 1 Foundations 2 Algorithm Programming Example #12;Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic

Kjellström, Hedvig

5

Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms  

E-print Network

Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms Foundations Algorithm Components Numerical Optimization Genetic Programming 1 Foundations 2 Algorithm Programming Example Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic

Kjellström, Hedvig

6

Genetic Algorithms Stephanie Forrest  

E-print Network

Genetic Algorithms Stephanie Forrest Dept. of Computer Science University of New Mexico Albuquerque algorithm is a computational model of biological evolution. Genetic algorithms are useful, both as search methods for solving problems and for modeling evolutionary sys­ tems. In genetic algorithms, binary

Forrest, Stephanie

7

Modeling Hybrid Genetic Algorithms  

Microsoft Academic Search

This paper looks at how one form of hybrid genetic algorithm can be modeledin the context of the existing models for the simple genetic algorithm; it shouldbe possible to model the integration of other types of local search with geneticalgorithms using the same basic approach. A secondary goal of this paper is toreview the existing models for finite and infinite

Darrell Whitley

1995-01-01

8

Genetic Algorithms: A Survey  

Microsoft Academic Search

Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex landscapes. We introduce the art and science of genetic algorithms and survey current issues in GA theory and practice. We do not present a detailed study, instead, we offer a quick guide into the labyrinth of GA research. First, we

M. Srinivas; Lalit M. Patnaik

1994-01-01

9

Genetic Algorithms and Quantum Computation  

Microsoft Academic Search

Recently, researchers have applied genetic algorithms (GAs) to address some problems in quantum computation. Also, there has been some works in the designing of genetic algorithms based on quantum theoretical concepts and techniques. The so called Quantum Evolutionary Programming has two major sub-areas: Quantum Inspired Genetic Algorithms (QIGAs) and Quantum Genetic Algorithms (QGAs). The former adopts qubit chromosomes as representations

Gilson A. Giraldi; Renato Portugal; Ricardo N. Thess

2004-01-01

10

Genetic algorithm eclipse mapping  

E-print Network

In this paper we analyse capabilities of eclipse mapping technique, based on genetic algorithm optimization. To model of accretion disk we used the "fire-flies" conception. This model allows us to reconstruct the distribution of radiating medium in the disk using less number of free parameters than in other methods. Test models show that we can achieve good approximation without optimizing techniques.

A. V. Halevin

2008-01-21

11

Genetic Algorithm and Graph Partitioning  

Microsoft Academic Search

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

Thang Nguyen Bui; Byung Ro Moon

1996-01-01

12

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

13

Genetic Algorithms and Quantum Computation  

Microsoft Academic Search

Recently, researchers have applied genetic algorithms (GAs) to address some\\u000aproblems in quantum computation. Also, there has been some works in the\\u000adesigning of genetic algorithms based on quantum theoretical concepts and\\u000atechniques. The so called Quantum Evolutionary Programming has two major\\u000asub-areas: Quantum Inspired Genetic Algorithms (QIGAs) and Quantum Genetic\\u000aAlgorithms (QGAs). The former adopts qubit chromosomes as representations

Gilson A. Giraldi; Renato Portugal; Ricardo N. Thess

2004-01-01

14

Production Scheduling and Genetic Algorithms  

Microsoft Academic Search

This treatise deals with the applicability of genetic algorithms to the area of production scheduling. To begin with, an introduction to the principles of genetic algorithms is given. After having outlined a standard genetic algorithm, first approaches to the traveling salesman problem are explained. On this basis, a survey on several approaches to different production scheduling problems is given.

Michael Neubauer; Gesamthochschule Essen

1995-01-01

15

A Genetic Algorithm Tutorial Darrell Whitley  

E-print Network

A Genetic Algorithm Tutorial Darrell Whitley Computer Science Department, Colorado State University algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling

Whitley, Darrell

16

A Genetic Algorithm Tutorial Darrell Whitley  

E-print Network

A Genetic Algorithm Tutorial Darrell Whitley Computer Science Department, Colorado State University algorithm as well as more experimental formsof genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling

Evett, Matthew

17

A Process Algebra Genetic Algorithm  

E-print Network

A genetic algorithm that utilizes process algebra for coding of solution chromosomes and for defining evolutionary based operators is presented. The algorithm is applicable to mission planning and optimization problems. ...

Karaman, Sertac

18

R. D. Field TU Talk Genetic AlgorithmsGenetic Algorithms  

E-print Network

R. D. Field TU Talk Genetic AlgorithmsGenetic Algorithms andand Neural NetworksNeural Networks asas and Neural Networks. #12;R. D. Field TU Talk Min-Max ProblemMin-Max Problem Let MM be a map from the data, ..., ON such that the requirement that Li enhancement times the efficiency, where efficiency = % signal

Field, Richard

19

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

20

Thinned arrays using genetic algorithms  

Microsoft Academic Search

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

Randy L. Haupt

1994-01-01

21

9. Genetic Algorithms 9.1 Introduction  

E-print Network

66 9. Genetic Algorithms 9.1 Introduction The concept of evolution is prevalent in most biological to computational optimisation methods using ``genetic algorithms'' [50]. 9.2 Neural Networks and Genetic Algorithms

Cambridge, University of

22

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

23

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

24

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

25

9. Genetic Algorithms 9.1 Introduction  

E-print Network

66 9. Genetic Algorithms 9.1 Introduction The concept of evolution is prevalent in most biological to computational optimisation methods using "genetic algorithms" [50]. 9.2 Neural Networks and Genetic Algorithms.1) with the function f being non-linear. Genetic algorithms (GAs) is one possible method of solving such a problem

Cambridge, University of

26

Breeder Genetic Algorithms for Airfoil Design Optimisation  

Microsoft Academic Search

A new version of Genetic Algorithms, the Breeder Genetic Algorithms, has been recentlyproposed in literature and successfully applied to the continuous parameter optimisation. Inthis paper we aim to test this technique against a classical discrete Genetic Algorithm on atypical optimisation problem in Aerodynamics, the problem of determining the coordinates ofan airfoil given a surface pressure distribution.1. IntroductionDuring last years Genetic

Ivan De Falco; Renato Del Balio; Antonio Della Cioppa; Ernesto Tarantino

1996-01-01

27

Niching Methods for Genetic Algorithms  

Microsoft Academic Search

Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions. Such domains include classification and machine learning, multimodal function optimization, multiobjective function optimization, and simulation of complex and adaptive systems. This study presents a comprehensive treatment of niching methods and the related topic of population diversity. Its purpose is to analyze existing niching methods

Samir Mahfoud

1995-01-01

28

Genetic algorithms and simulated annealing  

Microsoft Academic Search

This RESEARCH NOTE is a collection of papers on two types of stochastic search techniques-genetic algorithms and simulated annealing. These two techniques have been applied to problems that are both difficult and important, such as designing semiconductor layouts, controlling factories, and making communication networks cheaper, to name a few. Both techniques are modeled on processes found in nature-natural evolution and

Lawrence Davis

1987-01-01

29

Genetic algorithm optimization of entanglement  

E-print Network

We present an application of a genetic algorithmic computational method to the optimization of the concurrence measure of entanglement for the cases of one dimensional chains, as well as square and triangular lattices in a simple tight-binding approach in which the hopping of electrons is much stronger than the phonon dissipation

Jorge C. Navarro-Munoz; H. C. Rosu; R. Lopez-Sandoval

2006-11-13

30

Simultaneous stabilization using genetic algorithms  

SciTech Connect

This paper considers the problem of simultaneously stabilizing a set of plants using full state feedback. The problem is converted to a simple optimization problem which is solved by a genetic algorithm. Several examples demonstrate the utility of this method. 14 refs., 8 figs.

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

1991-01-01

31

Genetic Algorithm for Optimization: Preprocessor and Algorithm  

NASA Technical Reports Server (NTRS)

Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.

Sen, S. K.; Shaykhian, Gholam A.

2006-01-01

32

Algorithms for Human Genetics.  

E-print Network

??Whereas Mendel used breeding experiments and painstakingly counted peas, modern biology increasingly requires computational tools. In the late 1800's probability and experimental genetics were the (more)

KIRKPATRICK, BONNIE

2011-01-01

33

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

34

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

35

A Versatile Genetic Algorithm for Network Planning  

E-print Network

A Versatile Genetic Algorithm for Network Planning Anton Riedl Institute of Communication Networks, a new genetic algorithm is introduced which is used as a versatile tool for solving different types of optimization problems arising in the field of network planning. The genetic algorithm is applied to the minimum

Riedl, Anton

36

Genetic Algorithms Connecting evolution and learning  

E-print Network

Genetic Algorithms · Connecting evolution and learning ­ Apply evolutionary adaptation of Genetic Algorithms · Symbolic codes: each individual represented by a string · Search via biased sampling and a difficult search) · Crossover · Mutation #12;The Essential Genetic Algorithm #12;A Simple GA example #12

Indiana University

37

Genetic Algorithms and Evolutionary Darrell Whitley  

E-print Network

Genetic Algorithms and Evolutionary Computing Darrell Whitley Computer Science Department, Colorado State University Fort Collins, CO 80523 whitley@cs.colostate.edu 1 Introduction Genetic Algorithms are a family of computational models inspired by evolution. Other genetic and evolutionary algorithms include

Whitley, Darrell

38

An Introduction to Genetic Algorithms Kalyanmoy Deb  

E-print Network

An Introduction to Genetic Algorithms Kalyanmoy Deb Kanpur Genetic Algorithms Laboratory (KanGAL) Department of Mechanical Engineering Indian Institute of Technology Kanpur Kanpur, PIN 208 016, India E­mail: deb@iitk.ernet.in Abstract Genetic algorithms (GAs) are search and optimization tools, which work

Srivastava, Kumar Vaibhav

39

Test-Data Generation Using Genetic Algorithms  

Microsoft Academic Search

This paper presents a technique that uses a genetic algorithm for automatic test-data generation. A genetic algorithm is a heuristic that mimics the evolution of natural species in searching for the optimal solution to a problem. In the test-data generation application, the solution sought by the genetic algorithm is test data that causes execution of a given statement, branch, path,

Roy P. Pargas; Mary Jean Harrold; Robert R. Peck

1999-01-01

40

Genetic Algorithms Viewed as Anticipatory Systems  

NASA Astrophysics Data System (ADS)

This paper proposes a new version of genetic algorithmsthe 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

41

Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms  

Microsoft Academic Search

This paper demonstrates how genetic algorithms can be used to discover the structureof a Bayesian network from a given database with cases. The results presented, were obtained byapplying four different types of genetic algorithms -- SSGA (Steady State Genetic Algorithm), GAe(Genetic Algorithm elistist of degree ), hSSGA (hybrid Steady State Genetic Algorithm) and thehGAe (hybrid Genetic Algorithm elitist of degree

Pedro Larraaga; Roberto Murga; Mikel Poza; Cindy Kuijpers

1995-01-01

42

New Results in Astrodynamics Using Genetic Algorithms  

NASA Technical Reports Server (NTRS)

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

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

1998-01-01

43

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

44

Modeling Hybrid Genetic Algorithms Darrell Whitley  

E-print Network

University, Fort Collins, CO 80523 whitley@cs.colostate.edu 1 INTRODUCTION A ``hybrid genetic algorithm algorithms is simple and straight forward. This paper also builds on earlier work by Whitley, Gordon

Whitley, Darrell

45

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

46

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

2011-12-01

47

Excursion-Set-Mediated Genetic Algorithm  

NASA Technical Reports Server (NTRS)

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

Noever, David; Baskaran, Subbiah

1995-01-01

48

Genetic Algorithms for Protein Folding Simulations  

Microsoft Academic Search

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

Ron Unger; John Moult

1993-01-01

49

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

50

A Self Adaptive Hybrid Genetic Algorithm  

Microsoft Academic Search

This paper presents a self-adaptive hybrid genetic algorithm (SAHGA) and compares its performance to a non-adaptive hybrid genetic algorithm (NAHGA) and the simple genetic algorithm (SGA) on two multi-modal test functions with complex geometry. The SAHGA is shown to be far more robust than the NAHGA, providing fast and reliable convergence across a broad range of parameter settings. For the

Felipe P. Espinoza; Barbara S. Minsker; David E. Goldberg

2000-01-01

51

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

52

Floating Entanglement Witness Measure and Genetic Algorithm  

E-print Network

In this paper based on the notion of entanglement witness, a new measure of entanglement called floating entanglement witness measure is introduced which satisfies some of the usual properties of a good entanglement measure. By exploiting genetic algorithm, we introduce a classical algorithm that computes floating entanglement witness measure. This algorithm also provides a method for finding entanglement witness for a given entangled state.

A. Baghbanpourasl; G. Najarbashi; M. Seyedkazemi

2007-08-27

53

Drawing Undirected Graphs with Genetic Algorithms  

Microsoft Academic Search

\\u000a This paper proposes an improved genetic algorithm for producing aesthetically pleasing drawings of general undirected graphs.\\u000a Previous undirected graph drawing algorithms draw large cycles with no chords as concave polygons. In order to overcome such\\u000a disadvantage, the genetic algorithm in this paper designs a new mutation operator single-vertex- neighborhood mutation and\\u000a adds a component aiming at symmetric drawings to the

Qing-guo Zhang; Hua-yong Liu; Wei Zhang; Ya-jun Guo

2005-01-01

54

Genetic Algorithms in Astronomy and Astrophysics  

Microsoft Academic Search

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

P. Charbonneau

1995-01-01

55

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

56

A Hybrid Genetic Algorithm for Classification  

Microsoft Academic Search

In this paper we describe a method for hybridiz ing a genetic algorithm and a k nearest neighbors classification algorithm. We use the genetic algo rithm and a training data set to learn real-valued weights associated with individual attributes in the data set. We use the k nearest neighbors algo rithm to classify new data records based on their weighted

James D. Kelly Jr.; Lawrence Davis

1991-01-01

57

Hybrid genetic algorithms for analogue network synthesis  

Microsoft Academic Search

Network synthesis involves the selection of a suitable network topology and the choice of component values. Genetic algorithms can be used to perform both of these functions, but it is more efficient to adopt a hybrid approach in which a genetic algorithm is used to determine the network topology whilst the component values are obtained by numerical optimization

J. B. Grimbleby

1999-01-01

58

Genetic algorithm-based clustering technique  

Microsoft Academic Search

A genetic algorithm-based clustering technique, called GA-clustering, is proposed in this article. The searching capability of genetic algorithms is exploited in order to search for appropriate cluster centres in the feature space such that a similarity metric of the resulting clusters is optimized. The chromosomes, which are represented as strings of real numbers, encode the centres of a \\

Ujjwal Maulik; Sanghamitra Bandyopadhyay

2000-01-01

59

Genetic algorithms enhanced Kohonen's neural networks  

Microsoft Academic Search

A new approach of using genetic algorithms to improve the learning characteristics of Kohonen's neural networks is proposed in this paper. In the proposed scheme, genetic algorithms are applied to decide initial weights in the Kohonen's classifiers. The competitive learning is then applied to train neural networks. The proposed method was tested on the power system static security assessment and

Shyh-Jier Huang; Chuan-Chang Hung

1995-01-01

60

Optimization Based Image Segmentation by Genetic Algorithms  

E-print Network

Optimization Based Image Segmentation by Genetic Algorithms S. Chabrier1 , C. Rosenberger2 , B them. In this paper, we propose a general scheme to segment images by a genetic algorithm. The developed method uses an evaluation cri- terion which quantifies the quality of an image segmentation result

Paris-Sud XI, Université de

61

Multimodal genetic algorithms-based algorithm for automatic point correspondence  

Microsoft Academic Search

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

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

2010-01-01

62

Design Optimization of electromagnetic actuator by genetic algorithm  

E-print Network

Keywords: finite element method, magnetic force, genetic algorithm, electromagnetic actuator. 1. Introduction ..... diversity, the association of the genetic algorithm approach with ... for solving network design problem European

ELBEZ

2008-02-26

63

A genetic algorithm for use in creative design processes 1 A Genetic Algorithm for use in Creative Design Processes  

E-print Network

A genetic algorithm for use in creative design processes 1 A Genetic Algorithm for use in Creative with natural growth mechanisms applied to architectural design processes. We implement a genetic algorithm.1 Integral evolutionary design There are numerous examples of evolutionary algorithms: genetic algorithms

Boyer, Edmond

64

Genetic symbiosis algorithm for multiobjective optimization problem  

Microsoft Academic Search

Evolutionary algorithms are often well-suited for optimization problems. Since the mid-1980's, interest in multiobjective problems has been expanding rapidly. Various evolutionary algorithms have been developed which are capable of searching for multiple solutions concurrently in a single run. In this paper, we proposed a genetic symbiosis algorithm (GSA) for multi-object optimization problems (MOP) based on the symbiotic concept found widely

Jiangming Mao; K. Hirasawa; Jinlu Hu; J. Murata

2000-01-01

65

Hybrid genetic algorithm for electromagnetic topology optimization  

Microsoft Academic Search

This paper proposes a hybrid genetic algorithm (GA) for electromagnetic topology optimization. A two-dimensional (2-D) encoding technique, which considers the geometrical topology, is first applied to electromagnetics. Then, a 2-D geographic crossover is used as the crossover operator. A novel local optimization algorithm, called the on\\/off sensitivity method, hybridized with the 2-D encoded GA, improves the convergence characteristics. The algorithm

Chang-Hwan Im; Hyun-Kyo Jung; Yong-Joo Kim

2003-01-01

66

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

67

Genetic algorithm based tomographic flow visualization  

E-print Network

A new nonlinear tomographic flow visualization technique for use in limited data situations is developed using techniques from the emerging field of evolutionary computing. The new technique uses both pure and hybrid genetic algorithms from...

Lyons, Donald Paul

1997-01-01

68

From Genetic Algorithms to Efficient Organization  

E-print Network

The work described in this thesis began as an inquiry into the nature and use of optimization programs based on "genetic algorithms." That inquiry led, eventually, to three powerful heuristics that are broadly applicable ...

Yuret, Deniz

1994-05-01

69

Genetic algorithms at UC Davis/LLNL  

SciTech Connect

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

Vemuri, V.R. [comp.

1993-12-31

70

Genetic Algorithms and Supernovae Type Ia Analysis  

E-print Network

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. 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.

C. Bogdanos; Savvas Nesseris

2009-06-29

71

A hybrid genetic algorithm for the open shop scheduling problem  

Microsoft Academic Search

This paper examines the development and application of a hybrid genetic algorithm (HGA) to the open shop scheduling problem. The hybrid algorithm incorporates a local improvement procedure based on tabu search (TS) into a basic genetic algorithm (GA). The incorporation of the local improvement procedure enables the algorithm to perform genetic search over the subspace of local optima. The algorithm

Ching-Fang Liaw

2000-01-01

72

Path Cost Optimization Using Genetic Algorithm with Supervised Crossover Operator  

E-print Network

Path Cost Optimization Using Genetic Algorithm with Supervised Crossover Operator Chi-Tsun Cheng of these algorithms rise dras- tically. Meta-heuristic algorithms such as evolutionary al- gorithms and genetic-based path cost optimization algorithm is proposed. The generic crossover operator in genetic algorithms

Tse, Chi K. "Michael"

73

Terrainosaurus: realistic terrain synthesis using genetic algorithms  

E-print Network

TERRAINOSAURUS REALISTIC TERRAIN SYNTHESIS USING GENETIC ALGORITHMS A Thesis by RYAN L. SAUNDERS Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE... December 2006 Major Subject: Computer Science TERRAINOSAURUS REALISTIC TERRAIN SYNTHESIS USING GENETIC ALGORITHMS A Thesis by RYAN L. SAUNDERS Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements...

Saunders, Ryan L.

2007-04-25

74

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

75

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

76

A Genetic Algorithm System for Predicting Deniz Yuret  

E-print Network

A Genetic Algorithm System for Predicting the OEX Deniz Yuret Michael de la Maza 1 Introduction. To that end, this article describes an application of genetic algorithms to predicting the OEX. 2 What is a genetic algorithm? Genetic algorithms were invented over twenty years ago by John Holland who drew upon

77

A lowerbound result on the power of a genetic algorithm  

E-print Network

A lower­bound result on the power of a genetic algorithm Kihong Park \\Lambda park@cs.bu.edu BU This paper presents a lower­bound result on the computational power of a genetic algorithm in the context of combinatorial optimization. We describe a new genetic algorithm, the merged genetic algorithm, and prove

78

Genetic Algorithms To provide a background and understanding of basic genetic  

E-print Network

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 of the algorithm #12;Genetic Algorithms 1859 Origin of the Species Survival of the Fittest #12;Genetic Algorithms

Qu, Rong

79

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

E-print Network

A comparative study of Monte Carlo simple genetic algorithm and noisy genetic algorithm for cost 2005 Abstract This study evaluates and compares two methodologies, Monte Carlo simple genetic algorithm of uncertainties in the hydraulic conductivity (K) field. Both methodologies couple a genetic algorithm (GA

Zheng, Chunmiao

80

Combinatorial Multiobjective Optimization Using Genetic Algorithms  

NASA Technical Reports Server (NTRS)

The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.

Crossley, William A.; Martin. Eric T.

2002-01-01

81

Facial Composite System Using Genetic Algorithm  

NASA Astrophysics Data System (ADS)

The article deals with genetic algorithms and their application in face identification. The purpose of the research is to develop a free and open-source facial composite system using evolutionary algorithms, primarily processes of selection and breeding. The initial testing proved higher quality of the final composites and massive reduction in the composites processing time. System requirements were specified and future research orientation was proposed in order to improve the results.

Zahradnkov, Barbora; Duchovi?ov, So?a; Schreiber, Peter

2014-12-01

82

Forecasting chaotic time series with genetic algorithms  

NASA Astrophysics Data System (ADS)

This paper proposes the use of genetic algorithms-search procedures, modeled on the Darwinian theories of natural selection and survival of the fittest-to find equations that describe the behavior of a time series. The method permits global forecasts of such series. Very little data are sufficient to utilize the method and, as a byproduct, these algorithms sometimes indicate the functional form of the dynamic that underlies the data. The algorithms are tested with clean as well as with noisy chaotic data, and with the sunspot series.

Szpiro, George G.

1997-03-01

83

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

84

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-dominated Sorting Genetic Algorithm-II is presented. The algorithm provides significant decrease in compu- tational. This research shows the union of UT and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to give circuit de

Paris-Sud XI, Université de

85

A Genetic CascadeCorrelation Learning Algorithm \\Lambda  

E-print Network

A Genetic Cascade­Correlation Learning Algorithm \\Lambda Mitchell A. Potter Computer Science algorithms provide an alternative. Unfortunately, early attempts to use genetic algorithms to train connec. This paper explores an approach in which a traditional genetic algorithm using standard two­point crossover

George Mason University

86

A Genetic Algorithm for Minimax Optimization Problems Jeffrey W. Herrmann  

E-print Network

A Genetic Algorithm for Minimax Optimization Problems Jeffrey W. Herrmann Department of Mechanical-space genetic algorithm as a general technique to solve minimax optimization problems. This algorithm maintains of applications. To illustrate its potential, we use the two-space genetic algorithm to solve a parallel machine

Herrmann, Jeffrey W.

87

Genetic Algorithm Approaches for Actuator Placement  

NASA Technical Reports Server (NTRS)

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

Crossley, William A.

2000-01-01

88

Applying a Genetic Algorithm to Reconfigurable Hardware  

NASA Technical Reports Server (NTRS)

This paper investigates the feasibility of applying genetic algorithms to solve optimization problems that are implemented entirely in reconfgurable hardware. The paper highlights the pe$ormance/design space trade-offs that must be understood to effectively implement a standard genetic algorithm within a modem Field Programmable Gate Array, FPGA, reconfgurable hardware environment and presents a case-study where this stochastic search technique is applied to standard test-case problems taken from the technical literature. In this research, the targeted FPGA-based platform and high-level design environment was the Starbridge Hypercomputing platform, which incorporates multiple Xilinx Virtex II FPGAs, and the Viva TM graphical hardware description language.

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

2004-01-01

89

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.

90

Hyperplane Ranking, Nonlinearity and the Simple Genetic Algorithm  

E-print Network

Hyperplane Ranking, Nonlinearity and the Simple Genetic Algorithm Darrell Whitley Robert B ranking induced by a simple genetic algorithm is highly correlated with the degree of static ranking of their models of genetic algorithms (c.f. [9, 11, 12]). A critical question, then, is to what degree are genetic

Whitley, Darrell

91

Genetic Algorithms, Operators, and DNA Fragment Assembly  

Microsoft Academic Search

We study different genetic algorithm operators for one permutation problem associated with the Human Genome Projectthe assembly of DNA sequence fragments from a parent clone whose sequence is unknown into a consensus sequence corresponding to the parent sequence. The sorted-order representation, which does not require specialized operators, is compared with a more traditional permutation representation, which does require specialized operators.

Rebecca J. Parsons; Stephanie Forrest; Christian Burks

1995-01-01

92

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

93

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

94

Scheduling Multiprocessor Tasks with Genetic Algorithms  

Microsoft Academic Search

In the multiprocessor scheduling problem, a given program is to be scheduled in a given multiprocessor system such that the program's execution time is minimized. This problem being very hard to solve exactly, many heuristic methods for finding a suboptimal schedule exist. We propose a new combined approach, where a genetic algorithm is improved with the introduction of some knowledge

Ricardo C. Corra; Afonso Ferreira; Pascal Rebreyend

1999-01-01

95

Bicriteria transportation problem by hybrid genetic algorithm  

Microsoft Academic Search

In this paper, we present a hybrid genetic algorithm to solve the bicriteria transportation problem. we absorb the concept on spanning tree and adopt the Prfer number as it is capable of equally and uniquely representing all possible basic solutions. We designed the criterion which chromosomes can be always feasibly converted to a transportation tree. In order to improve the

Mitsuo Gen; Kenichi Ida; Yinzhen Li

1998-01-01

96

A Hybrid Genetic Algorithm for School Timetabling  

Microsoft Academic Search

Hybrid Genetic Algorithms apply so called hybrid or repair operators or include problem specific knowledge about the problem domain in their mutation and crossover operators. These operators use local search to repair or avoid illegal or unsuitable assignments or just to improve the quality of the solutions already found.

Peter Wilke; Matthias Grbner; Norbert Oster

2002-01-01

97

Solving Satisfiability Problems with Genetic Algorithms  

E-print Network

, and discuss their pros and cons. 1 Introduction Given a propositional formula like (p1 p2 ¬p3) (¬p1 p2 p3 algorithms. Furthermore, we explore other genetic operators that may be useful to tackle 3-SAT problems strings of length n and truth assignments to n variables. Therefore the search space that we have

Harmeling, Stefan

98

Scramjet missile design using genetic algorithms  

Microsoft Academic Search

The objective of this effort was to show that scramjet powered vehicle designs which are optimized for a given flight condition can be found using predictive modeling tools and a genetic algorithm (GA). This required the assimilation of basic modeling tools for the external aerodynamics, the engine internal ballistics for thrust and additive drag predictions, and for the mass and

R. J. Hartfield; J. E. Burkhalter; R. M. Jenkins

2006-01-01

99

A genetic algorithm for fin profile optimization  

Microsoft Academic Search

In the present work a genetic algorithm is proposed in order to optimize the thermal performances of finned surfaces. The bidimensional temperature distribution on the longitudinal section of the fin is calculated by resorting to the finite elements method. The heat flux dissipated by a generic profile fin is compared with the heat flux removed by the rectangular profile fin

Giampietro Fabbri

1997-01-01

100

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

101

Terrain generation using an Interactive Genetic Algorithm  

Microsoft Academic Search

This paper introduces the Auto Terrain Generation System (ATGS), which is based on an Interactive Genetic Algorithm (IGA) that enables non-specialist users to rapidly generate terrains. The motivation for using an IGA is discussed, existing terrain generation techniques are described and a new approach, based on a fractal terrain engine, is outlined. Graphics engines allow terrains to be specified with

Paul Walsh; Prasad Gade

2010-01-01

102

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

103

Convergence analysis of canonical genetic algorithms  

Microsoft Academic Search

This paper analyzes the convergence properties of the canonical genetic algorithm (CGA) with mutation, crossover and proportional reproduction applied to static optimization problems. It is proved by means of homogeneous finite Markov chain analysis that a CGA will never converge to the global optimum regardless of the initialization, crossover, operator and objective function. But variants of CGA's that always maintain

Gunter Rudolph

1994-01-01

104

Feature Subset Selection Using a Genetic Algorithm  

Microsoft Academic Search

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

Jihoon Yang; Vasant Honavar

1998-01-01

105

System identification and control using genetic algorithms  

Microsoft Academic Search

It is shown how genetic algorithms can be applied for system identification of both continuous and discrete time systems. It is shown that they are effective in both domains and are able to directly identify physical parameters or poles and zeros. This can be useful because changing one physical parameter might affect every parameter of a system transfer function. The

Kristinn Kristinsson; Guy A. Dumont

1992-01-01

106

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

107

Dynamic causal modeling with genetic algorithms.  

PubMed

In the last years, dynamic causal modeling has gained increased popularity in the neuroimaging community as an approach for the estimation of effective connectivity from functional magnetic resonance imaging (fMRI) data. The algorithm calls for an a priori defined model, whose parameter estimates are subsequently computed upon the given data. As the number of possible models increases exponentially with additional areas, it rapidly becomes inefficient to compute parameter estimates for all models in order to reveal the family of models with the highest posterior probability. In the present study, we developed a genetic algorithm for dynamic causal models and investigated whether this evolutionary approach can accelerate the model search. In this context, the configuration of the intrinsic, extrinsic and bilinear connection matrices represents the genetic code and Bayesian model selection serves as a fitness function. Using crossover and mutation, populations of models are created and compared with each other. The most probable ones survive the current generation and serve as a source for the next generation of models. Tests with artificially created data sets show that the genetic algorithm approximates the most plausible models faster than a random-driven brute-force search. The fitness landscape revealed by the genetic algorithm indicates that dynamic causal modeling has excellent properties for evolution-driven optimization techniques. PMID:21094663

Pyka, M; Heider, D; Hauke, S; Kircher, T; Jansen, A

2011-01-15

108

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

109

New Hybrid Genetic Algorithms for the Frequency Assignment Problem  

Microsoft Academic Search

This paper presents a new hybrid genetic algorithm used to solve a frequency assignment problem. The hybrid genetic algorithm presented in this paper uses two original mutation operators. The first mutation operator is based on a greedy algorithm and the second one on an original probabilistic tabu search. The results obtained by our algorithm are better than the best known

Miguel Alabau; Lhassane Idoumghar; Ren Schott

2001-01-01

110

Linear array synthesis using a hybrid genetic algorithm  

Microsoft Academic Search

There has been some interest in the application of genetic algorithms to antenna array synthesis problems. A direct encoding hybrid genetic algorithm is applied to a variable length and fixed number of elements linear array synthesis problem. A comparison is made with published results. The algorithm proposed paper yields substantially better results than the previously published work. The algorithm utilizes

Michael J. Buckley

1996-01-01

111

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

112

Production scheduling and rescheduling with genetic algorithms.  

PubMed

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

Bierwirth, C; Mattfeld, D C

1999-01-01

113

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

114

A hybrid genetic algorithm for the job shop scheduling problems  

Microsoft Academic Search

The Job Shop Scheduling Problem (JSSP) is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on genetic algorithm to address JSSP. We design a scheduling method based on Single Genetic Algorithm (SGA) and Parallel Genetic Algorithm (PGA). In the scheduling method, the representation,

Byung Joo Park; Hyung Rim Choi; Hyun Soo Kim

2003-01-01

115

Hybrid genetic algorithm for optimization problems with permutation property  

Microsoft Academic Search

Permutation property has been recognized as a common but challenging feature in combinatorial problems. Because of their complexity, recent research has turned to genetic algorithms to address such problems. Although genetic algorithms have been proven to facilitate the entire space search, they lack in fine-tuning capability for obtaining the global optimum. Therefore, in this study a hybrid genetic algorithm was

Hsiao-fan Wang; Kuang-yao Wu

2004-01-01

116

Classification with Scaled Genetic Algorithms in a Coevolutionary Setting  

Microsoft Academic Search

This work discusses asymptotic convergence of scaled genetic algorithms in a coevolutionary setting where the underlying population contains fixed numbers of creatures of various types. These types of crea- tures can act on each other in cooperative or competitive manner. The genetic algorithm uses common mutation and crossover operators as well as proportional fitness selection. By a scaled genetic algorithm,

Lothar M. Schmitt

2004-01-01

117

Towards a Genetic Algorithm for Function Optimization Sonja Novkovic  

E-print Network

Towards a Genetic Algorithm for Function Optimization Sonja Novkovic and Davor Sverko Abstract: This article analyses a version of genetic algorithm (GA, Holland 1975) designed for function optimization, such as non-coding segments, elitist selection and multiple crossover. Key words: Genetic algorithm, Royal

118

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 on a genetic algorithm with a novel fitness function and a bagging-like model aver- aging scheme. We signatures developed specially for the colon cancer case. Keywords. genetic algorithm, feature selection

119

Simultaneous Feature Extraction and Selection Using a Masking Genetic Algorithm  

E-print Network

1 Simultaneous Feature Extraction and Selection Using a Masking Genetic Algorithm Michael L. Raymer Structural Analysis and Design Laboratory, Department of Biochemistry, 2 Genetic Algorithms Research among different pattern classes [4,5]. Genetic algorithms (GA's) have been applied to the problem

120

Applying Genetic Algorithm to Modeling Nonlinear Transfer Functions  

E-print Network

Applying Genetic Algorithm to Modeling Nonlinear Transfer Functions Sergey L. Loyka Abstract- A genetic algorithm technique for the approximation of nonlinear transfer functions is proposed of this technique to behavioral-level simulation is also discussed. Keywords­ genetic algorithm, nonlinear transfer

Loyka, Sergey

121

An Analysis of a Simple Genetic Algorithm Yuri Rabinovich  

E-print Network

An Analysis of a Simple Genetic Algorithm Yuri Rabinovich Dept. of Computer Science, Hebrew for a simple, and yet nontrivial, family of genetic algorithms. 1 INTRODUCTION This paper originates optimiza­ tion. In Holland's [1] pioneering work it is sug­ gested that genetic algorithms are likely

Wigderson, Avi

122

Ordering Autonomous Underwater Vehicle Inspection Locations with a Genetic Algorithm  

E-print Network

Ordering Autonomous Underwater Vehicle Inspection Locations with a Genetic Algorithm Brandon Morton@acm.org Abstract--This paper describes a genetic algorithm for solving the traveling salesman problem (TSP (MOOS). The results show that the genetic algorithm performs significantly better than the approach

Idaho, University of

123

An Analysis of a Simple Genetic Algorithm Yuri Rabinovich  

E-print Network

An Analysis of a Simple Genetic Algorithm Yuri Rabinovich Dept. of Computer Science, Hebrew for a simple, and yet nontrivial, family of genetic algorithms. 1 INTRODUCTION This paper originates optimiza- tion. In Holland's [1] pioneering work it is sug- gested that genetic algorithms are likely

Wigderson, Avi

124

A Genetic Algorithm for Simultaneous Localization and Mapping Tom Duckett  

E-print Network

A Genetic Algorithm for Simultaneous Localization and Mapping Tom Duckett Centre for Applied. The fitness values in the genetic algorithm are obtained with a heuristic function that measures of the maps produced, and the search proceeds using a genetic algorithm (GA). GAs are a well-known search

Duckett, Tom

125

A Genetic Algorithm for Grammars James Anderson and Joe Staines  

E-print Network

A Genetic Algorithm for Grammars James Anderson and Joe Staines July 1, 2010 Background training data. 1 #12;A Genetic Algorithm for Grammars Of course, there are many more grammars than be able to search heuristically. Project Proposal We propose a project which uses a genetic algorithm

Goldschmidt, Christina

126

FINE-GRAINED PARALLEL GENETIC ALGORITHM: A STOCHASTIC OPTIMISATION METHOD  

E-print Network

FINE-GRAINED PARALLEL GENETIC ALGORITHM: A STOCHASTIC OPTIMISATION METHOD A. Muhammad1 , A.King@solent.ac.uk Abstract This paper presents a fine-grained parallel genetic algorithm with mutation rate as a control in the simulated annealing [Lundy'86, Otten'89, and Romeo'85]. The parallel genetic algorithm presented here

Bargiela, Andrzej

127

GENETIC ALGORITHMS FOR PARTITIONING SETS WILLIAM A. GREENE  

E-print Network

GENETIC ALGORITHMS FOR PARTITIONING SETS WILLIAM A. GREENE Computer Science Department University into subsets such that their sums are as nearly equal as possible. We devise a new genetic algorithm, Eager. Keywords: genetic algorithm, set partitioning, Equal Piles Problem, map coloring. 1. Introduction

Greene, William A.

128

A Genetic Algorithm for Simultaneous Localization and Mapping Tom Duckett  

E-print Network

A Genetic Algorithm for Simultaneous Localization and Mapping Tom Duckett Centre for Applied. The fitness values in the genetic algorithm are obtained with a heuristic function that measures of the maps produced, and the search proceeds using a genetic algorithm (GA). GAs are a well­known search

Duckett, Tom

129

The Evolution of Understanding: A Genetic Algorithm Model of the  

E-print Network

The Evolution of Understanding: A Genetic Algorithm Model of the Evolution of Communication Michael.edu Keywords: evolution of communication, genetic algorithm, self-organization #12;Abstract Much animal such understanding. Using a genetic algorithm implemented on a computer, I demonstrate that a significant though

Levin, Michael

130

Genetic algorithm dynamics on a rugged landscape Stefan Bornholdt*  

E-print Network

Genetic algorithm dynamics on a rugged landscape Stefan Bornholdt* Institut fu¨r Theoretische manuscript received 5 December 1997 The genetic algorithm is an optimization procedure motivated on a maximum entropy ansatz. Finally it is applied to modeling the dynamics of a genetic algorithm

Bornholdt, Stefan

131

Dynamic fuzzy control of genetic algorithm parameter coding.  

PubMed

An algorithm for adaptively controlling genetic algorithm parameter (GAP) coding using fuzzy rules is presented. The fuzzy GAP coding algorithm is compared to the dynamic parameter encoding scheme proposed by Schraudolph and Belew. The performance of the algorithm on a hydraulic brake emulator parameter identification problem is investigated. Fuzzy GAP coding control is shown to dramatically increase the rate of convergence and accuracy of genetic algorithms. PMID:18252316

Streifel, R J; Marks, R J; Reed, R; Choi, J J; Healy, M

1999-01-01

132

Predicting mining activity with parallel genetic algorithms  

USGS Publications Warehouse

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

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

2005-01-01

133

Genetic algorithms in adaptive fuzzy control  

NASA Technical Reports Server (NTRS)

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.

Karr, C. Lucas; Harper, Tony R.

1992-01-01

134

Genetic algorithms for minimal source reconstructions  

SciTech Connect

Under-determined linear inverse problems arise in applications in which signals must be estimated from insufficient data. In these problems the number of potentially active sources is greater than the number of observations. In many situations, it is desirable to find a minimal source solution. This can be accomplished by minimizing a cost function that accounts from both the compatibility of the solution with the observations and for its ``sparseness``. Minimizing functions of this form can be a difficult optimization problem. Genetic algorithms are a relatively new and robust approach to the solution of difficult optimization problems, providing a global framework that is not dependent on local continuity or on explicit starting values. In this paper, the authors describe the use of genetic algorithms to find minimal source solutions, using as an example a simulation inspired by the reconstruction of neural currents in the human brain from magnetoencephalographic (MEG) measurements.

Lewis, P.S.; Mosher, J.C.

1993-12-01

135

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

136

This is the documentation for the genetic algorithm program, as described in chapter Program MAP_GENETIC_ALGORITHM  

E-print Network

Appendix A This is the documentation for the genetic algorithm program, as described in chapter 9. Program MAP_GENETIC_ALGORITHM 1. Provenance of code. 2. Purpose of code. 3. Specification. 4. Description, Cambridge, U.K. Application added: September 2002 Purpose An application of the genetic algorithm (GA

Cambridge, University of

137

Selective Breeding in a Multiobjective Genetic Algorithm  

Microsoft Academic Search

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

Geoffrey T. Parks; I. Miller

1998-01-01

138

Forecasting Confined Spatiotemporal Chaos with Genetic Algorithms  

NASA Astrophysics Data System (ADS)

A technique to forecast spatiotemporal time series is presented. It uses a proper orthogonal or Karhunen-Love decomposition to encode large spatiotemporal data sets in a few time series, and genetic algorithms to efficiently extract dynamical rules from the data. The method works very well for confined systems displaying spatiotemporal chaos, as exemplified here by forecasting the evolution of the one-dimensional complex Ginzburg-Landau equation in a finite domain.

Lpez, Cristbal; lvarez, Alberto; Hernndez-Garca, Emilio

2000-09-01

139

Forecasting confined spatiotemporal chaos with genetic algorithms.  

PubMed

A technique to forecast spatiotemporal time series is presented. It uses a proper orthogonal or Karhunen-Love decomposition to encode large spatiotemporal data sets in a few time series, and genetic algorithms to efficiently extract dynamical rules from the data. The method works very well for confined systems displaying spatiotemporal chaos, as exemplified here by forecasting the evolution of the one-dimensional complex Ginzburg-Landau equation in a finite domain. PMID:10977996

Lpez, C; Alvarez, A; Hernndez-Garca, E

2000-09-11

140

Integrated environmental management process applying genetic algorithm  

Microsoft Academic Search

This study proposes a Total Environmental Management System using a Genetic Algorithm (TEMS-AGA), which takes into account the possible effects on the environment together with other management concerns such as quality, lead time, and cost. In TEMS-AGA, a problem is modeled by an ANDOR-type tree structure in its structural aspects and by a list of symbolic expressions in its quantitative

Toshihiko Takeuchi; Yusuke Yazu; Akiyuki Sakuma

1999-01-01

141

Dynamic Parameter Encoding for Genetic Algorithms  

Microsoft Academic Search

Thecommonuseofstaticbinaryplace-value codesforreal-valuedparametersofthephen- otype in Holland's genetic algorithm (GA) forceseitherthesacriflceofrepresentational precision for e-ciency of search or vice versa. Dynamic Parameter Encoding(DPE) is a mechanism that avoids this dilemma by using convergence statistics derived from the GA population to adaptively control the mapping from flxed-length binary genes to real values. DPE is shown to be empirically efiective and amenable to analysis; we

Nicol N. Schraudolph; Richard K. Belew

1992-01-01

142

PID Parameters Optimization by Using Genetic Algorithm  

E-print Network

Time delays are components that make time-lag in systems response. They arise in physical, chemical, biological and economic systems, as well as in the process of measurement and computation. In this work, we implement Genetic Algorithm (GA) in determining PID controller parameters to compensate the delay in First Order Lag plus Time Delay (FOLPD) and compare the results with Iterative Method and Ziegler-Nichols rule results.

Mirzal, Andri; Furukawa, Masashi

2012-01-01

143

Genetic algorithms for multiagent fusion system learning  

NASA Astrophysics Data System (ADS)

The development of efficient semi-automatic systems for heterogeneous information fusion is actually a great challenge. The efficiency can be presented as the system openness, the system evolution capabilities and the system performance. Multi- agent architecture can be designed in order to respect the first two efficiency constraints. As for the third constraint, which is the performance, the key point is the interaction between each information component of the system. The context of this study is the development of a semi-automatic information fusion system for cartographic features interpretation. Combining heterogeneous sources of information such as expert rules and strategies, domain models, image processing tools, interpolation techniques, etc. completes the system development task. The information modeling and fusion is performed within the evidential theory concepts. The purpose of this article is to propose a learning approach for interaction-oriented multi-agent systems. The optimization of the interaction weight is tackled with genetic algorithms technique because it provides solution for the whole set of weights at once. In this paper, the context of the multi-agent system development is presented first. The need for such system and its parameters is explained. A brief overview of learning techniques leads to genetic algorithms as a choice for the learning of the developed multi-agent system. Two approaches are designed to measure the system's fitness based on either binary or fuzzy decisions. The conclusion presents suggestions for further research in the area of multi-agent system-learning with genetic algorithms.

Pigeon, Luc; Inglada, Jordi; Solaiman, Basel

2001-03-01

144

Scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm in heterogeneous multiprocessors system  

Microsoft Academic Search

The scheduling problem for real-time tasks on multiprocessor is one of the NP-hard problems. This paper proposes a new scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm (mohGA) on heterogeneous multiprocessor environment. In solution algorithms, the genetic algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method, the convergence of GA is improved by introducing

Myungryun Yoo; Mitsuo Gen

2007-01-01

145

Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories  

NASA Technical Reports Server (NTRS)

The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.

Burchett, Bradley T.

2003-01-01

146

Genetic algorithm based reference bands selection in hyperspectral image compression  

Microsoft Academic Search

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

Yushi Chen; Aili Wang; Ye Zhang

2008-01-01

147

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

148

Analyzing synchronous and asynchronous parallel distributed genetic algorithms  

Microsoft Academic Search

Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial genetic algorithms (GAs), since they often can be tailored to provide a larger efficiency on complex search problems. In a PGA several sub-algorithms cooperate in parallel to solve the problem. This high-level definition has led to a considerable number of different implementations that preclude direct comparisons

Enrique Alba; Jos M. Troya

2001-01-01

149

A novel immune genetic algorithm for image segmentation  

NASA Astrophysics Data System (ADS)

Based on Immune Genetic Algorithm (IGA), a novel algorithm for image segmentation is presented in this paper. Utilizing functions of self-adaptive, antigen recognition and memory of immune mechanism, this algorithm combines genetic operator to segment image. The results of experiments indicate that it is an effective new method for image segmentation.

Wang, Chunbai; Zhao, Baojun; He, Peikun

2003-09-01

150

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

151

Genetic algorithm solution of economic dispatch with valve point loading  

Microsoft Academic Search

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

D. C. Walters; G. B. Sheble

1993-01-01

152

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

153

Some issues of designing genetic algorithms for traveling salesman problems  

Microsoft Academic Search

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

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

2004-01-01

154

Polychromator filter design with genetic algorithm  

NASA Astrophysics Data System (ADS)

In Thomson scattering (TS) diagnostics, polychromators are equipped with several optical band-pass filters which cover the spectral region where the radiation from the incident laser beam is expected to be Doppler shifted. The spectral location of the transmission band of individual filters has a strong influence on the measured electron temperature (Te) since the latter is derived from a previously computed lookup table including the spectral specifications of the filters. Here, we present the design of the set of polychromator filters through genetic algorithms (GAs). We examine the developed algorithm under two specific target conditions, and optimized filter sets covering the wavelength region longer than the wavelength of the incident laser seem to be more effective in improving the accuracy of the Te calculations provided by the diagnostic.

Oh, Seungtae; Park, Jiyoung

2015-02-01

155

Comparison of genetic algorithms with conjugate gradient methods  

NASA Technical Reports Server (NTRS)

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

Bosworth, J. L.; Foo, N. Y.; Zeigler, B. P.

1972-01-01

156

A genetic algorithm to minimize chromatic entropy  

E-print Network

We present an algorithmic approach to solving the problem of chromatic entropy, a combinatorial optimization problem related to graph coloring. This problem is a component in algorithms for optimizing data compression when ...

Durrett, Greg

157

Saving Resources with Plagues in Genetic Algorithms  

SciTech Connect

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

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

2004-06-15

158

Genetic algorithm and particle swarm optimization combined with Powell method  

NASA Astrophysics Data System (ADS)

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

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

2013-10-01

159

Stochastics and Statistics A hybrid hypercube Genetic algorithm approach for deploying many  

E-print Network

Stochastics and Statistics A hybrid hypercube ­ Genetic algorithm approach for deploying many Available online 9 September 2010 Keywords: Emergency response Hypercube Spatial queues Genetic algorithms), a location model and a metaheuristic optimization algorithm (genetic algorithm) for obtaining appropriate

Thévenaz, Jacques

160

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

161

Colloidal Crystal Prediction via a Genetic Algorithm  

NASA Astrophysics Data System (ADS)

We use a genetic algorithm search technique to find optimal colloidal (hard sphere) crystal structures. The method is verified with several known structures, and new ternary structures with various stoichiometries are presented. Structures with stoichiometries of ABC2, ABC3, ABC4, and AB2C2 have been found with packing fractions larger than close packed spheres. Layered structures with commensurate sphere sizes are preferred. The size ratio and range of stability of these structures are also presented suggesting a hierarchial structure formation where the smaller spheres stabilize normally unstable structures of larger spheres. Possible application to other shapes such as fused spheres and rods will be discussed.

Stucke, David; Crespi, Vincent

2002-03-01

162

Modeling of Nonlinear Systems using Genetic Algorithm  

NASA Astrophysics Data System (ADS)

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

Hayashi, Kayoko; Yamamoto, Toru; Kawada, Kazuo

163

Restoration of Halftone Image Using Genetic Algorithms  

NASA Astrophysics Data System (ADS)

In this study, we propose a new method for an inverse halftoning technique. Inverse halftoning technique is to restore a continuous tone (gray-scale) image from halftone (bi-level) image. Usually, it is not easy to derive gray-scale color (8bits/color) from only a bi-level data (black and white). We considered generating a gray-scale value from bi-level data as an optimum searching problems. Then, we propose a method of the inverse halftoning technique using the genetic algorithms without depending on the halftone kernel. As a result, a high quality image can be restored without depending on a halftone kernel.

Furuya, Tamotsu; Mori, Kunihiko

164

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

165

Dominant takeover regimes for genetic algorithms  

NASA Technical Reports Server (NTRS)

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

Noever, David; Baskaran, Subbiah

1995-01-01

166

A hybrid genetic algorithm for the channel routing problem  

Microsoft Academic Search

We present a Hybrid Genetic Algorithm (HGA) for the Channel Routing Problem (CRP). To do so we combine a Genetic Algorithm (GA) with domain specific knowledge, i.e. the genetic operators make use of the rip-up and reroute technique. Thereby the execution time of our method is faster than previously presented evolutionary based approaches. Furthermore, concerning space complexity we show by

Nicole Gckel; Gregor Pudelko; Rolf Drechsler; Bernd Becker

1996-01-01

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 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.

169

Serial and Parallel Genetic Algorithms as Function Optimizers  

Microsoft Academic Search

Parallel genetic algorithms are often very differentfrom the "traditional" genetic algorithmproposed by Holland, especially withregards to population structure and selectionmechanisms. In this paper we compare severalparallel genetic algorithms across a widerange of optimization functions in an attemptto determine whether these changes have positiveor negative impact on their problemsolvingcapabilities. The findings indicatethat the parallel structures perform as well asor ...

V. Scott Gordon; L. Darrell Whitley

1993-01-01

170

Application of genetic algorithm to steganalysis  

NASA Astrophysics Data System (ADS)

We present a novel application of genetic algorithm (GA) to optimal feature set selection in supervised learning using support vector machine (SVM) for steganalysis. Steganalysis attempts to determine whether a cover object (in our case an image file) contains hidden information. This is a bivariate classification problem: the image either does or does not contain hidden data. Our SVM classifier uses a training set of images with known classification to "learn" how to classify images with unknown classification. The SVM uses a feature set, essentially a set of statistical quantities extracted from the image. The performance of the SVM classifier is heavily dependent on the feature set used. Too many features not only increase computation time but decrease performance, and too few features do not provide enough information for accurate classification. Our steganalysis technique uses entropic features that yield up to 240 features per image. The selection of an optimum feature set is a problem that lends itself well to genetic algorithm optimization. We describe this technique in detail and present a "GA optimized" feature set of 48 features that, for our application, optimizes the tradeoff between computation time and classification accuracy.

Knapik, Timothy; Lo, Ephraim; Marsh, John A.

2006-05-01

171

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

172

JavaGenes and Condor: cycle-scavenging genetic algorithms  

Microsoft Academic Search

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, desk- side, and rack-mounted SGI workstations. Genetic algorithms mimic biological evolution by evolving solutions to problems using crossover and mutation. While most genetic algorithms evolve strings or trees,

Al Globus; Eric Langhirt; Miron Livny; Ravishankar Ramamurthy; Marvin H. Solomon; Steve Traugott

2000-01-01

173

An Anytime Algorithm for Scheduling of Aircraft Landing Times Using Genetic Algorithms \\Lambda  

E-print Network

between leaving wide safety margins between aircraft and maximizing the number of aircraft that take offAn Anytime Algorithm for Scheduling of Aircraft Landing Times Using Genetic Algorithms \\Lambda Vic of the computation time. We argue that for some kinds of problems, such as optimizing aircraft landing times, genetic

Ciesielski, Vic

174

Max Planck Molecular Genetics: Algorithmics Group http://algorithmics.molgen.mpg.de Ivan G. Costa Filho  

E-print Network

Max Planck Molecular Genetics: Algorithmics Group http://algorithmics.molgen.mpg.de Ivan G. Costa Filho Stefan Roepcke Alexander Schliep Computational Biology Department Max Planck Institute for Molecular Genetics, Berlin Analysis of Gene Expression Trees in Blood Cell Development #12;Max Planck

Spang, Rainer

175

A Comparative Analysis of Selection Schemes Used in Genetic Algorithms  

Microsoft Academic Search

This paper considers a number of selection schemes commonly used in modern genetic algorithms. Specifically, proportionate reproduction, rank- ing selection, tournament selection, and Genitor (or steady state\\

David E. Goldberg; Kalyanmoy Deb

1990-01-01

176

A Genetic Algorithm for the Point to Multipoint Routing Problem with Varying Number of Requests  

E-print Network

A Genetic Algorithm for the Point to Multipoint Routing Problem with Varying Number of Requests Problem that uses a genetic algorithm and a heuristic Steiner tree algorithm. Our genetic algorithm allows to Multipoint Routing Problem that uses a genetic algorithm and a heuristic Steiner tree algorithm. Using both

Wainwright, Roger L.

177

A New Challenge for Compression Algorithms: Genetic Sequences.  

ERIC Educational Resources Information Center

Analyzes the properties of genetic sequences that cause the failure of classical algorithms used for data compression. A lossless algorithm, which compresses the information contained in DNA and RNA sequences by detecting regularities such as palindromes, is presented. This algorithm combines substitutional and statistical methods and appears to

Grumbach, Stephane; Tahi, Fariza

1994-01-01

178

A new hybrid genetic algorithm for global optimization  

Microsoft Academic Search

In this paper a Hybrid Interval Genetic algorithm (HIG) is presented. The algorithm consists of two phases: In the first phase, interval arithmetic and especially an interval branch--and--bound algorithm is used to obtain small regions where candidate solutions lie. In this way, a population of potential solutions is initialized and initial bounds for the global minimum $f^*$ are obtained. In

D. G. Sotiropoulos; E. C. Stavropoulos; M. N. Vrahatis

1997-01-01

179

Morphological algorithm design for binary images using genetic programming  

Microsoft Academic Search

This paper presents a Genetic Programming (GP) approach to the design of Mathematical Morphology (MM) algorithms for binary images. The algorithms are constructed using logic operators and the basic MM operators, i.e. erosion and dilation, with a variety of structuring elements. GP is used to evolve MM algorithms that convert a binary image into another containing just a particular feature

Marcos I. Quintana; Riccardo Poli; Ela Claridge

2006-01-01

180

A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem  

PubMed Central

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

181

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

182

Multidisciplinary design optimization using genetic algorithms  

NASA Technical Reports Server (NTRS)

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

Unal, Resit

1994-01-01

183

Spacecraft Attitude Maneuver Planning Using Genetic Algorithms  

NASA Technical Reports Server (NTRS)

A key enabling technology that leads to greater spacecraft autonomy is the capability to autonomously and optimally slew the spacecraft from and to different attitudes while operating under a number of celestial and dynamic constraints. The task of finding an attitude trajectory that meets all the constraints is a formidable one, in particular for orbiting or fly-by spacecraft where the constraints and initial and final conditions are of time-varying nature. This approach for attitude path planning makes full use of a priori constraint knowledge and is computationally tractable enough to be executed onboard a spacecraft. The approach is based on incorporating the constraints into a cost function and using a Genetic Algorithm to iteratively search for and optimize the solution. This results in a directed random search that explores a large part of the solution space while maintaining the knowledge of good solutions from iteration to iteration. A solution obtained this way may be used as is or as an initial solution to initialize additional deterministic optimization algorithms. A number of representative case examples for time-fixed and time-varying conditions yielded search times that are typically on the order of minutes, thus demonstrating the viability of this method. This approach is applicable to all deep space and planet Earth missions requiring greater spacecraft autonomy, and greatly facilitates navigation and science observation planning.

Kornfeld, Richard P.

2004-01-01

184

A genetic algorithm for solving supply chain network design model  

NASA Astrophysics Data System (ADS)

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

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

2013-09-01

185

Genetic Algorithm Approaches to Prebiobiotic Chemistry Modeling  

NASA Technical Reports Server (NTRS)

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

Lohn, Jason; Colombano, Silvano

1997-01-01

186

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

187

A Genetic Algorithm Approach for Technology Characterization  

E-print Network

no efficient technique for modeling the parameterized Pareto frontier. The contribution of this thesis is a new algorithm for modeling the parameterized Pareto frontier to be used as a model of the characteristics of a technology. The novelty of the algorithm...

Galvan, Edgar

2012-10-19

188

Genetic Branch-and-Bound or Exact Genetic Algorithm?  

Microsoft Academic Search

Production resettings is a vital element of production flexibility and optimizing the setup tasks scheduling within a production\\u000a channel is required to improve production rate. This paper deals with a NP-Hard production resetting optimization problem\\u000a based on an industrial case. In this paper we present how to hybrid a Branch-and-Bound method for this problem with a genetic\\u000a algorithm. The idea

Cdric Pessan; Jean-louis Bouquard; Emmanuel Nron

2007-01-01

189

Transonic Wing Shape Optimization Using a Genetic Algorithm  

NASA Technical Reports Server (NTRS)

A method for aerodynamic shape optimization based on a genetic algorithm approach is demonstrated. The algorithm is coupled with a transonic full potential flow solver and is used to optimize the flow about transonic wings including multi-objective solutions that lead to the generation of pareto fronts. The results indicate that the genetic algorithm is easy to implement, flexible in application and extremely reliable.

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

2002-01-01

190

Anti-phishing detection of phishing attacks using genetic algorithm  

Microsoft Academic Search

An approach to detection of phishing hyperlinks using the rule based system formed by genetic algorithm is proposed, which can be utilized as a part of an enterprise solution to anti-phishing. A legitimate webpage owner can use this approach to search the web for suspicious hyperlinks. In this approach, genetic algorithm is used to evolve rules that are used to

V. Shreeram; M. Suban; P. Shanthi; K. Manjula

2010-01-01

191

A Lamarckian Evolution Strategy for Genetic Algorithms Brian J. Ross  

E-print Network

. 1. Introduction Prior to Charles Darwin's theory of evolution by natural selection, Jean BaptisteA Lamarckian Evolution Strategy for Genetic Algorithms Brian J. Ross Brock University Department implementation of a simple Lamarckian evolution module for genetic algorithms is discussed. Lamarckian evolution

Ross, Brian J.

192

A Test of Genetic Algorithms in Relevance Feedback.  

ERIC Educational Resources Information Center

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

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

2002-01-01

193

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

194

Adding Learning to Cellular Genetic Algorithms for Training Recurrent Neural  

E-print Network

1 Adding Learning to Cellular Genetic Algorithms for Training Recurrent Neural Networks Kim W. C search (in­ dividual learning) and cellular genetic algorithms (GAs) for training recurrent neural of the numbers forms a chromosome. Reproduction takes place locally in a square grid with each grid point

Mak, Man-Wai

195

Delta Coding: An Iterative Search Strategy for Genetic Algorithms  

Microsoft Academic Search

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

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

1991-01-01

196

Fuzzy multiple objective optimal system design by hybrid genetic algorithm  

Microsoft Academic Search

In this paper, we propose a method for solving fuzzy multiple objective optimal system design problems with GUB structure by hybridized genetic algorithms (HGA). This approach enables the flexible optimal system design by applying fuzzy goals and fuzzy constraints. In this genetic algorithm (GA), we propose the new chromosomes representation that represents the GUB structure simply and effectively at the

Masato Sasaki; Mitsuo Gen

2003-01-01

197

Application of improved genetic algorithm in camera calibration  

NASA Astrophysics Data System (ADS)

With the camera internal parameters known, to calculate the external parameters is to solve a set of highly nonlinear over-determined equations. In this paper, an improved hybrid genetic algorithm is adopted to obtain external parameters. It combines the advantages of genetic algorithm and Newton method, making it possible to obtain results with high accuracy and a faster convergence.

Li, Weimin; Liu, Hui; Zhu, Lichun; Zhao, Yu

2014-09-01

198

A hybrid genetic algorithm for the job shop scheduling problem  

Microsoft Academic Search

This paper presents a hybrid genetic algorithm for the Job Shop Scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local

Jos Fernando Gonalves; Jorge Jos De Magalhes Mendes; Maur??cio G. C. Resende

2005-01-01

199

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

200

Genetic algorithm optimization for aerospace electromagnetic design and analysis  

Microsoft Academic Search

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

J. Michael Johnson; Yahya Rahmat-Samii

1996-01-01

201

Improving flexibility and efficiency by adding parallelism to genetic algorithms  

Microsoft Academic Search

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

Enrique Alba; Jos M. Troya

2002-01-01

202

Transaction clustering of web log data files using genetic algorithm  

Microsoft Academic Search

Increasingly web applications found to impact on numerous environments. The web log data offer more promises and particularly application of the genetic algorithms is significant as it represents the relations between different data components. We have used simple genetic algorithms to log files and we found that the preliminary results are more promising there by open more avenues for future

Daisy Jacobs; S. Sarasvady; Pit. Pichappan

2007-01-01

203

Application of a genetic algorithm to doping profile identification  

Microsoft Academic Search

This work describes a new approach to CV dopant profiling by means of nonlinear least squares inverse modeling. It is shown that a genetic algorithm can replace standard nonlinear minimization procedure in identification of doping profile parameters. The most important advantages of the genetic algorithm are in its ability to avoid local minima and often in faster convergence in \\

W. Kuzmicz

1996-01-01

204

Adaptive probabilities of crossover and mutation in genetic algorithms  

Microsoft Academic Search

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

M. Srinivas; Lalit M. Patnaik

1994-01-01

205

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 algorithm as the core optimization method is described and the resulting advanced coil designs presented. 1

206

A genetic algorithm solution to the unit commitment problem  

Microsoft Academic Search

This paper presents a genetic algorithm (GA) solution to the unit commitment problem. GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple GA algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but

S. A. Kazarlis; A. G. Bakirtzis; V. Petridis

1996-01-01

207

Model-based image interpretation using genetic algorithms  

Microsoft Academic Search

We describe the application of genetic algorithms in model-based image inter- pretation. The delineation of left ventricular boundaries in apical 4-chamber echocardiograms is used as an illustrative exemplar. The suitability of genetic algorithms for the model\\/objective-function\\/search procedure is presented.

Andrew Hill; Christopher J. Taylor

1992-01-01

208

Genetic Algorithms for Optimal Scheduling of Chlorine Dosing in Water  

E-print Network

oz343 Genetic Algorithms for Optimal Scheduling of Chlorine Dosing in Water Distribution Systems for determining the optimal schedule of chlorine dosing within a water distribution system considering multiple. The model is also capable of handling improved nonlinear chlorine decay algorithms by separating the genetic

Coello, Carlos A. Coello

209

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 viscousinviscid 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

210

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

211

Assessing the performance of two immune inspired algorithms and a hybrid genetic algorithm for function optimisation  

Microsoft Academic Search

Do artificial immune systems (AIS) have something to offer the world of optimisation? Indeed do they have any new to offer at all? This paper reports the initial findings of a comparison between two immune inspired algorithms and a hybrid genetic algorithm for function optimisation. This work is part of ongoing research which forms part of a larger project to

Jon Timmis; Camilla Edmonds; Johnny Kelsey

2004-01-01

212

Optimizing Melodic Extraction Algorithm for Jazz Guitar Recordings Using Genetic Algorithms  

E-print Network

Optimizing Melodic Extraction Algorithm for Jazz Guitar Recordings Using Genetic Algorithms Sergio of jazz standards, and we collected commercial audio recordings extracted from jazz guitar CDs. Based on the MIDI record- ings as ground truth, two different instrument settings are compared (Jazz trio

213

Identification of Keys and Cryptographic Algorithms Using Genetic Algorithm and Graph Theory  

Microsoft Academic Search

This paper describes genetic algorithms that use the Calisnki-Harabasz index as its evaluation function and graphs techniques that are both used to identify patterns in cryptograms generated by cryptographics algorithms certified by NIST (National Institute Standard Technology), namely AES, RC6, MARS, Twofish and Serpent. Evidence of patterns or \\

Jose Xexeo; William Souza; Renato Torres; Glaucio Oliveira; Ricardo Linden

2011-01-01

214

Performance Analysis of Estimation of Distribution Algorithm and Genetic Algorithm in Zone Routing Protocol  

Microsoft Academic Search

In this paper, Estimation of Distribution Algorithm (EDA) is used for Zone Routing Protocol (ZRP) in Mobile Ad-hoc Network (MANET) instead of Genetic Algorithm (GA). It is an evolutionary approach, and used when the network size grows and the search space increases. When the destination is outside the zone, EDA is applied to find the route with minimum cost and

Mst. Farhana Rahman; S. M. Masud Karim; Kazi Shah Nawaz Ripon

2010-01-01

215

SOLVING THE REDUNDANCY ALLOCATION PROBLEM USING A COMBINED NEURAL NETWORK / GENETIC ALGORITHM APPROACH  

E-print Network

SOLVING THE REDUNDANCY ALLOCATION PROBLEM USING A COMBINED NEURAL NETWORK / GENETIC ALGORITHM reliability constraint. The genetic algorithm searches among candidate designs of the system configuration;2 SOLVING THE REDUNDANCY ALLOCATION PROBLEM USING A COMBINED NEURAL NETWORK / GENETIC ALGORITHM APPROACH

Smith, Alice E.

216

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

NASA Astrophysics Data System (ADS)

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

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

217

Multiobjective Genetic Algorithm applied to dengue control.  

PubMed

Dengue fever is an infectious disease caused by a virus of the Flaviridae family and transmitted to the person by a mosquito of the genus Aedes aegypti. This disease has been a global public health problem because a single mosquito can infect up to 300 people and between 50 and 100 million people are infected annually on all continents. Thus, dengue fever is currently a subject of research, whether in the search for vaccines and treatments for the disease or efficient and economical forms of mosquito control. The current study aims to study techniques of multiobjective optimization to assist in solving problems involving the control of the mosquito that transmits dengue fever. The population dynamics of the mosquito is studied in order to understand the epidemic phenomenon and suggest strategies of multiobjective programming for mosquito control. A Multiobjective Genetic Algorithm (MGA_DENGUE) is proposed to solve the optimization model treated here and we discuss the computational results obtained from the application of this technique. PMID:25230238

Florentino, Helenice O; Cantane, Daniela R; Santos, Fernando L P; Bannwart, Bettina F

2014-12-01

218

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

219

Random Volumetric MRI Trajectories via Genetic Algorithms  

PubMed Central

A pseudorandom, velocity-insensitive, volumetric k-space sampling trajectory is designed for use with balanced steady-state magnetic resonance imaging. Individual arcs are designed independently and do not fit together in the way that multishot spiral, radial or echo-planar trajectories do. Previously, it was shown that second-order cone optimization problems can be defined for each arc independent of the others, that nulling of zeroth and higher moments can be encoded as constraints, and that individual arcs can be optimized in seconds. For use in steady-state imaging, sampling duty cycles are predicted to exceed 95 percent. Using such pseudorandom trajectories, aliasing caused by under-sampling manifests itself as incoherent noise. In this paper, a genetic algorithm (GA) is formulated and numerically evaluated. A large set of arcs is designed using previous methods, and the GA choses particular fit subsets of a given size, corresponding to a desired acquisition time. Numerical simulations of 1 second acquisitions show good detail and acceptable noise for large-volume imaging with 32 coils. PMID:18604305

Curtis, Andrew Thomas; Anand, Christopher Kumar

2008-01-01

220

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

221

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

222

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

223

Time-Delay System Identification Using Genetic Algorithm -Part Two  

E-print Network

-site issues, such as the excitation condition, measured data quality, selected identification algorithmTime-Delay System Identification Using Genetic Algorithm - Part Two: FOPDT/SOPDT Model Approximation Zhenyu Yang Glen T. Seested Department of Energy Technology, Aalborg University, Esbjerg Campus

Yang, Zhenyu

224

The exploration\\/exploitation tradeoff in dynamic cellular genetic algorithms  

Microsoft Academic Search

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

Enrique Alba; Bernab Dorronsoro

2005-01-01

225

Combining Robot Control Strategies using Genetic Algorithms with Memory.  

E-print Network

[10]. We cast low­level robot control strategy design as a search problem in a search spaceCombining Robot Control Strategies using Genetic Algorithms with Memory. Sushil J. Louis and Gan Li algorithm augmented with a long term memory to design control strategies for a simulated robot, a mobile ve

Louis, Sushil J.

226

Combining Robot Control Strategies using Genetic Algorithms with Memory.  

E-print Network

to choose between these low-level behaviors for performing more complex tasks 10]. We cast low-level robotCombining Robot Control Strategies using Genetic Algorithms with Memory. Sushil J. Louis and Gan Li algorithm augmented with a long term memory to design control strategies for a simulated robot, a mobile ve

Louis, Sushil J.

227

Power economic dispatch using a hybrid genetic algorithm  

Microsoft Academic Search

This letter outlines a hybrid genetic algorithm (HGA) for solving the economic dispatch problem. The algorithm incorporates the solution produced by an improved Hopfield neural network (NN) as a part of its initial population. Elitism, arithmetic crossover and mutation are used in the GAs to generate successive sets of possible operating policies. The technique improves the quality of the solution

T. Yalcinoz; H. Altun

2001-01-01

228

A Genetic Algorithm for the Generation of Jazz Melodies  

Microsoft Academic Search

This paper describes a system for the generation of jazz melodies over an input chord progression. A genetic algorithm was used to search through the space of possible solutions. A symbolic, as opposed to binary, approach with domain-specificreproduction operators was chosen because it allowed knowledge based constraints to be imposed on the search space. The objective, algorithmic fitnessfunction as well

George Papadopoulos; Geraint Wiggins

1998-01-01

229

Identification of pneumatic cylinder friction parameters using genetic algorithms  

Microsoft Academic Search

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

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

2004-01-01

230

Transonic airfoil design and optimisation by using vibrational genetic algorithm  

Microsoft Academic Search

In this study, the power of vibrational genetic algorithm (VGA) for transonic airfoil design and optimisation problems, which are generally characterized by multi-model topology in the design parameter space, has been introduced. This type of problem is characterized by search and computational time to achieve satisfying solutions. In order to obtain more robust and faster algorithm, vibration concept, our earlier

2003-01-01

231

An Overview of Genetic Algorithms : Part 1, Fundamentals  

E-print Network

stated by Charles Darwin in The Origin of Species. By mimicking this process, genetic algorithms are able to evolution. Exactly which biological processes are essential for evolution, and which processes have little

Martin, Ralph R.

232

The Use of Genetic Algorithms in Multilayer Mirror Optimization  

E-print Network

Department of Physics and Astronomy Brigham Young University, Provo, UT 84602 #12;June 10, 1999, GA.doc, page 2 Abstract We have applied the genetic algorithm to extreme ultraviolet (XUV) multilayer mirror

Hart, Gus

233

A multi-population genetic algorithm for a constrained two ...  

E-print Network

dure with a genetic algorithm based on random keys. We propose also a ..... usually applied to some individuals, to guarantee population diversity. 3.2.1 Chromosome ...... cutting'. European Journal of Operational Research 156, 601 627. 27...

2008-12-10

234

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

E-print Network

Jun 30, 2005 ... priorities of the activities are defined by the genetic algorithm. The heuristic generates ..... to some individuals, to guarantee population diversity. ..... European Journal of Operational Research 49, pp. 3-13. Bouleimen, K. and...

x

2005-07-01

235

A Pareto Frontier for Full Stern Submarines via Genetic Algorithm  

E-print Network

by : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : Justin E. Kerwin Professor of Naval Architecture Thesis Supervisor Accepted: Professor of Naval Architecture #12; #12; A Pareto Frontier for Full Stern Submarines via Genetic Algorithm Institute of Technology, 1996 Naval Engineer, Massachusetts Institute of Technology, 1996 Submitted

Coello, Carlos A. Coello

236

Generation of Fuzzy Classification Systems using Genetic Algorithms.  

E-print Network

??In this thesis, we propose an improved fuzzy GBMLgenetic-based machine learningalgorithm to construct a FRBCSfuzzy rule-based classification systemfor pattern classification problem. Existing hybrid fuzzy GBML (more)

Lee, Cheng-Tsung

2006-01-01

237

Genetic Algorithms for Real Parameter Optimization  

Microsoft Academic Search

This paper is concerned with the application of gen etic algorithms to optimization problems over several real parameters. It is shown that k-point crossover (for k small relative to the number of parameters) can be viewed as a crossover operation on the vector of parameters plus perturbations of some of the parameters. Mutation can also be co nsidered as a

Alden H. Wright

1991-01-01

238

Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms  

E-print Network

HORIZONTAL WELL PLACEMENT OPTIMIZATION IN GAS RESERVOIRS USING GENETIC ALGORITHMS A Thesis by TREVOR HOWARD GIBBS Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements... for the degree of MASTER OF SCIENCE May 2010 Major Subject: Petroleum Engineering HORIZONTAL WELL PLACEMENT OPTIMIZATION IN GAS RESERVOIRS USING GENETIC ALGORITHMS A Thesis by TREVOR HOWARD GIBBS Submitted to the Office of Graduate...

Gibbs, Trevor Howard

2011-08-08

239

A hybrid of the genetic algorithm and concurrent simplex  

E-print Network

A HYBRID OF THE GENETIC ALGORITHM AND CONCURRENT SIMPLEX A Thesis by DAVID ETHAN RANDOLPH Submitted to the Office of Graduate Studies of Texas ARM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May... 1995 Major Subject: Computer Science A HYBRID OF THE GENETIC ALGORITHM AND CONCURRENT SIMPLEX A Thesis DAVID ETHAN RANDOLPH Submitted to Texas AkM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE...

Randolph, David Ethan

1995-01-01

240

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

241

Optimizing Hydropower Reservoir Operation Using Hybrid Genetic Algorithm and Chaos  

Microsoft Academic Search

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

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

2008-01-01

242

Hybrid genetic algorithm with adaptive local search scheme  

Microsoft Academic Search

This paper proposes a hybrid genetic algorithm (a-hGA) with adaptive local search scheme. For designing the a-hGA, a local search technique is incorporated in the loop of genetic algorithm (GA), and whether or not the local search technique is used in the GA is automatically determined by the adaptive local search scheme. Two modes of adaptive local search schemes are

YoungSu Yun

2006-01-01

243

A Hybrid Genetic Algorithm for the Capacitated Vehicle Routing Problem  

Microsoft Academic Search

Recently proved successful for variants of the vehicle routing problem (VRP) involving time windows, genetic algorithms have\\u000a not yet shown to compete or challenge current best search techniques in solving the classical capacitated VRP. In this paper,\\u000a a hybrid genetic algorithm to address the capacitated vehicle routing problem is proposed. The basic scheme consists in concurrently\\u000a evolving two populations of

Jean Berger; Mohamed Barkaoui

2003-01-01

244

Superscattering of light optimized by a genetic algorithm  

SciTech Connect

We analyse scattering of light from multi-layer plasmonic nanowires and employ a genetic algorithm for optimizing the scattering cross section. We apply the mode-expansion method using experimental data for material parameters to demonstrate that our genetic algorithm allows designing realistic core-shell nanostructures with the superscattering effect achieved at any desired wavelength. This approach can be employed for optimizing both superscattering and cloaking at different wavelengths in the visible spectral range.

Mirzaei, Ali, E-mail: ali.mirzaei@anu.edu.au; Miroshnichenko, Andrey E.; Shadrivov, Ilya V.; Kivshar, Yuri S. [Nonlinear Physics Center, Research School of Physics and Engineering, Australian National University, Canberra ACT 0200 (Australia)

2014-07-07

245

A New Neuro-Fuzzy Adaptive Genetic Algorithm  

Microsoft Academic Search

Novel neuro-fuzzy techniques are used to dynamically control parameter settings of genetic algorithms (GAs). The benchmark routine is an adaptive genetic algorithm (AGA) that uses a fuzzy knowledge-based system to control GA parameters. The self-learning ability of the cerebellar model ariculation controller(CMAC) neural network makes it possible for on-line learning the knowledge on GAs throughout the run. Automatically designing and

ZHU Lili ZHANG

2003-01-01

246

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

247

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

248

Hybrid algorithm for NARX network parameters' determination using differential evolution and genetic algorithm  

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

249

An Agent Inspired Reconfigurable Computing Implementation of a Genetic Algorithm  

NASA Technical Reports Server (NTRS)

Many software systems have been successfully implemented using an agent paradigm which employs a number of independent entities that communicate with one another to achieve a common goal. The distributed nature of such a paradigm makes it an excellent candidate for use in high speed reconfigurable computing hardware environments such as those present in modem FPGA's. In this paper, a distributed genetic algorithm that can be applied to the agent based reconfigurable hardware model is introduced. The effectiveness of this new algorithm is evaluated by comparing the quality of the solutions found by the new algorithm with those found by traditional genetic algorithms. The performance of a reconfigurable hardware implementation of the new algorithm on an FPGA is compared to traditional single processor implementations.

Weir, John M.; Wells, B. Earl

2003-01-01

250

The search for black hole binaries using a genetic algorithm  

E-print Network

In this work we use genetic algorithm to search for the gravitational wave signal from the inspiralling massive Black Hole binaries in the simulated LISA data. We consider a single signal in the Gaussian instrumental noise. This is a first step in preparation for analysis of the third round of the mock LISA data challenge. We have extended a genetic algorithm utilizing the properties of the signal and the detector response function. The performance of this method is comparable, if not better, to already existing algorithms.

Antoine Petiteau; Yu Shang; Stanislav Babak

2009-08-25

251

Forecasting non-stationary financial time series through genetic algorithm  

E-print Network

We utilize a recently developed genetic algorithm, in conjunction with discrete wavelets, for carrying out successful forecasts of the trend in financial time series, that includes the NASDAQ composite index. Discrete wavelets isolate the local, small scale variations in these non-stationary time series, after which the genetic algorithm's predictions are found to be quite accurate. The power law behavior in Fourier domain reveals an underlying self-affine dynamical behavior, well captured by the algorithm, in the form of an analytic equation. Remarkably, the same equation captures the trend of the Bombay stock exchange composite index quite well.

M. B. Porecha; P. K. Panigrahi; J. C. Parikh; C. M. Kishtawal; Sujit Basu

2005-07-18

252

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

253

A simple genetic algorithm for multiple sequence alignment.  

PubMed

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 MSA-GA. Genetic algorithms, a class of evolutionary algorithms, are well suited for problems of this nature since residues and gaps are discrete units. An evolutionary algorithm cannot compete in terms of speed with progressive alignment methods but it has the advantage of being able to correct for initially misaligned sequences; which is not possible with the progressive method. This was shown using the BaliBase benchmark, where Clustal-W alignments were used to seed the initial population in MSA-GA, improving outcome. Alignment scoring functions still constitute an open field of research, and it is important to develop methods that simplify the testing of new functions. A general evolutionary framework for testing and implementing different scoring functions was developed. The results show that a simple genetic algorithm is capable of optimizing an alignment without the need of the excessively complex operators used in prior study. The clear distinction between objective function and genetic algorithms used in MSA-GA makes extending and/or replacing objective functions a trivial task. PMID:18058716

Gondro, C; Kinghorn, B P

2007-01-01

254

Performance Analysis of Estimation of Distribution Algorithm and Genetic Algorithm in Zone Routing Protocol  

Microsoft Academic Search

In this paper, Estimation of Distribution Algorithm (EDA) is used for Zone\\u000aRouting Protocol (ZRP) in Mobile Ad-hoc Network (MANET) instead of Genetic\\u000aAlgorithm (GA). It is an evolutionary approach, and used when the network size\\u000agrows and the search space increases. When the destination is outside the zone,\\u000aEDA is applied to find the route with minimum cost and

Mst. Farhana Rahman; S. M. Masud Karim; Kazi Shah Nawaz Ripon

2010-01-01

255

An Indirect Genetic Algorithm for a Nurse Scheduling Problem  

E-print Network

This paper describes a Genetic Algorithms approach to a manpower-scheduling problem arising at a major UK hospital. Although Genetic Algorithms have been successfully used for similar problems in the past, they always had to overcome the limitations of the classical Genetic Algorithms paradigm in handling the conflict between objectives and constraints. The approach taken here is to use an indirect coding based on permutations of the nurses, and a heuristic decoder that builds schedules from these permutations. Computational experiments based on 52 weeks of live data are used to evaluate three different decoders with varying levels of intelligence, and four well-known crossover operators. Results are further enhanced by introducing a hybrid crossover operator and by making use of simple bounds to reduce the size of the solution space. The results reveal that the proposed algorithm is able to find high quality solutions and is both faster and more flexible than a recently published Tabu Search approach.

Aickelin, Uwe

2008-01-01

256

Novel hybrid genetic algorithm for progressive multiple sequence alignment.  

PubMed

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

Afridi, Muhammad Ishaq

2013-01-01

257

Forecasting the solar cycle with genetic algorithms  

NASA Astrophysics Data System (ADS)

In the past, it has been postulated that the irregular dynamics of the solar cycle may embed a low order chaotic process (Weiss 1988, 1994; Spiegel 1994) which, if true, implies that the future behaviour of solar activity should be predictable. Here, starting from the historical record of Zrich sunspot numbers, we build a dynamical model of the solar cycle which allows us to make a long-term forecast of its behaviour. Firstly, the deterministic part of the time series has been reconstructed using the Singular Spectrum Analysis and then an evolutionary algorithm (Alvarez et al. 2001), based on Darwinian theories of natural selection and survival and ideally suited for non-linear time series, has been applied. Then, the predictive capability of the algorithm has been tested by comparing the behaviour of solar cycles 19-22 with forecasts made with the algorithm, obtaining results which show reasonable agreement with the known behaviour of those cycles. Next, the forecast of the future behaviour of solar cycle 23 has been performed and the results point out that the level of activity during this cycle will be somewhat smaller than in the two previous ones.

Orfila, A.; Ballester, J. L.; Oliver, R.; Alvarez, A.; Tintor, J.

2002-04-01

258

The Genetic Algorithm is Useful to Fitting Input Probability Distributions for Simulation Models  

E-print Network

The Genetic Algorithm is Useful to Fitting Input Probability Distributions for Simulation Models, Genetic Algorithm ABSTRACT The genetic algorithm can be applied to selecting theoretical probability the genetic algorithm, one can decide which one of some different families of prob- ability distributions

Strelen, Christoph

259

Genetic algorithms with permutation coding for multiple sequence alignment.  

PubMed

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

Ben Othman, Mohamed Tahar; Abdel-Azim, Gamil

2013-08-01

260

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

261

Fuzzy Information Retrieval Using Genetic Algorithms and Relevance Feedback.  

ERIC Educational Resources Information Center

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

Petry, Frederick E.; And Others

1993-01-01

262

Multiple Query Evaluation Based on an Enhanced Genetic Algorithm.  

ERIC Educational Resources Information Center

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

Tamine, Lynda; Chrisment, Claude; Boughanem, Mohand

2003-01-01

263

A hybrid genetic algorithm for the container loading problem  

Microsoft Academic Search

This paper presents a hybrid genetic algorithm (GA) for the container loading problem with boxes of different sizes and a single container for loading. Generated stowage plans include several vertical layers each containing several boxes. Within the procedure, stowage plans are represented by complex data structures closely related to the problem. To generate offspring, specific genetic operators are used that

Andreas Bortfeldt; Hermann Gehring

2001-01-01

264

Genetic Algorithm and Neural Network Hybrids for Controlling Mobile Robots  

Microsoft Academic Search

As the hardware capabilities of unmanned battlefield robots, such as Micro Aerial Vehicles (MAVs) and Unmanned Ground Vehicles (UGVs), increases, so to must the intelligence of the software controlling them. Genetic Algorithms (GAs) and Genetic Programming (GP) have proven effective in preliminary MAV and UGV simulations for evolving simple tracking and surveillance behav iors. However, the reactive approach that most

Jimmy Secretan; Guy A. Schiavone

2004-01-01

265

Empirical studies of the genetic algorithm with noncoding segments  

E-print Network

performance. Understanding when and why this improvement occurs will help us to use the GA to its full: genetic algorithms, non­coding segments, non­coding DNA, introns, Royal Road function. 1 #12; 1, specifically, non­coding DNA. In natural systems, deoxyribonucleic acid (DNA) is the genetic material

Wu, Annie S.

266

Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization  

Microsoft Academic Search

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

Carlos M. Fonseca; Peter J. Fleming

1993-01-01

267

Solving Classification Problems Using Genetic Programming Algorithms on GPUs  

NASA Astrophysics Data System (ADS)

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

Cano, Alberto; Zafra, Amelia; Ventura, Sebastin

268

A hybrid genetic algorithm for a class of global optimization problems with box constraints  

Microsoft Academic Search

In this paper, a new hybrid genetic algorithm is proposed, which combines the genetic algorithm with hill-climbing search steps differently from some former algorithms. The new algorithm can be widely applied to a class of global optimization problems for continuous functions with box constraints. Finally, numerical examples show that this algorithm can yield the global optimum with high efficiency.

Quan Yuan; Zhiqing He; Huinan Leng

2008-01-01

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

A pipelined FPGA implementation of an encryption algorithm based on genetic algorithm  

NASA Astrophysics Data System (ADS)

With the evolution of digital data storage and exchange, it is essential to protect the confidential information from every unauthorized access. High performance encryption algorithms were developed and implemented by software and hardware. Also many methods to attack the cipher text were developed. In the last years, the genetic algorithm has gained much interest in cryptanalysis of cipher texts and also in encryption ciphers. This paper analyses the possibility to use the genetic algorithm as a multiple key sequence generator for an AES (Advanced Encryption Standard) cryptographic system, and also to use a three stages pipeline (with four main blocks: Input data, AES Core, Key generator, Output data) to provide a fast encryption and storage/transmission of a large amount of data.

Thirer, Nonel

2013-05-01

271

Constrained minimization of smooth functions using a genetic algorithm  

NASA Technical Reports Server (NTRS)

The use of genetic algorithms for minimization of differentiable functions that are subject to differentiable constraints is considered. A technique is demonstrated for converting the solution of the necessary conditions for a constrained minimum into an unconstrained function minimization. This technique is extended as a global constrained optimization algorithm. The theory is applied to calculating minimum-fuel ascent control settings for an energy state model of an aerospace plane.

Moerder, Daniel D.; Pamadi, Bandu N.

1994-01-01

272

Genetic algorithm for multi-objective experimental optimization.  

PubMed

A new software tool making use of a genetic algorithm for multi-objective experimental optimization (GAME.opt) was developed based on a strength Pareto evolutionary algorithm. The software deals with high dimensional variable spaces and unknown interactions of design variables. This approach was evaluated by means of multi-objective test problems replacing the experimental results. A default parameter setting is proposed enabling users without expert knowledge to minimize the experimental effort (small population sizes and few generations). PMID:17048033

Link, Hannes; Weuster-Botz, Dirk

2006-12-01

273

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

274

Hierarchical Genetic Algorithm Approach to Determine Pulse Sequences in NMR  

E-print Network

We develop a new class of genetic algorithm that computationally determines efficient pulse sequences to implement a quantum gate U in a three-qubit system. The method is shown to be quite general, and the same algorithm can be used to derive efficient sequences for a variety of target matrices. We demonstrate this by implementing the inversion-on-equality gate efficiently when the spin-spin coupling constants $J_{12}=J_{23}=J$ and $J_{13}=0$. We also propose new pulse sequences to implement the Parity gate and Fanout gate, which are about 50% more efficient than the previous best efforts. Moreover, these sequences are shown to require significantly less RF power for their implementation. The proposed algorithm introduces several new features in the conventional genetic algorithm framework. We use matrices instead of linear chains, and the columns of these matrices have a well defined hierarchy. The algorithm is a genetic algorithm coupled to a fast local optimizer, and is hence a hybrid GA. It shows fast con...

Ajoy, Ashok

2009-01-01

275

Hierarchical Genetic Algorithm Approach to Determine Pulse Sequences in NMR  

E-print Network

We develop a new class of genetic algorithm that computationally determines efficient pulse sequences to implement a quantum gate U in a three-qubit system. The method is shown to be quite general, and the same algorithm can be used to derive efficient sequences for a variety of target matrices. We demonstrate this by implementing the inversion-on-equality gate efficiently when the spin-spin coupling constants $J_{12}=J_{23}=J$ and $J_{13}=0$. We also propose new pulse sequences to implement the Parity gate and Fanout gate, which are about 50% more efficient than the previous best efforts. Moreover, these sequences are shown to require significantly less RF power for their implementation. The proposed algorithm introduces several new features in the conventional genetic algorithm framework. We use matrices instead of linear chains, and the columns of these matrices have a well defined hierarchy. The algorithm is a genetic algorithm coupled to a fast local optimizer, and is hence a hybrid GA. It shows fast convergence, and running on a MATLAB platform takes about 20 minutes on a standard personal computer to derive efficient pulse sequences for any target 8X8 matrix $U$.

Ashok Ajoy; Anil Kumar

2009-12-04

276

Genetic algorithm and the application for job shop group scheduling  

NASA Astrophysics Data System (ADS)

Genetic algorithm (GA) is a heuristic and random search technique mimicking nature. This paper first presents the basic principle of GA, the definition and the function of the genetic operators, and the principal character of GA. On the basis of these, the paper proposes using GA as a new solution method of the job-shop group scheduling problem, discusses the coded representation method of the feasible solution, and the particular limitation to the genetic operators.

Mao, Jianzhong; Wu, Zhiming

1995-08-01

277

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

278

A Hybrid Genetic Algorithm for the Point to Multipoint Routing Problem with  

E-print Network

A Hybrid Genetic Algorithm for the Point to Multipoint Routing Problem with Single Split Paths Words: Genetic Algorithm, Steiner Trees, Point to Multipoint Routing, Telecommunications Network to Multipoint Routing Problem with Single Split Paths. Our hybrid algorithm uses a genetic algorithm

Wainwright, Roger L.

279

Optimization of experimental design in fMRI: a general framework using a genetic algorithm  

E-print Network

Optimization of experimental design in fMRI: a general framework using a genetic algorithm Tor D uses a genetic algorithm (GA), a class of flexible search algorithms that optimize designs with respect genetic algorithms may be applied to experimental design for fMRI, and we use the framework to explore

280

A genetic algorithm approach in interface and surface structure optimization  

SciTech Connect

The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the material structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.

Zhang, Jian

2010-05-16

281

Rules extraction in short memory time series using genetic algorithms  

NASA Astrophysics Data System (ADS)

Data mining is performed using genetic algorithm on artificially generated time series data with short memory. The extraction of rules from a training set and the subsequent testing of these rules provide a basis for the predictions on the test set. The artificial time series are generated using the inverse whitening transformation, and the correlation function has an exponential form with given time constant indicative of short memory. A vector quantization technique is employed to classify the daily rate of return of this artificial time series into four categories. A simple genetic algorithm based on a fixed format of rules is introduced to do the forecasting. Comparing to the benchmark tests with random walk and random guess, genetic algorithms yield substantially better prediction rates, between 50% to 60%. This is an improvement compared with the 47% for random walk prediction and 25% for random guessing method.

Fong, L. Y.; Szeto, K. Y.

2001-04-01

282

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

283

Acoustic design of rotor blades using a genetic algorithm  

NASA Technical Reports Server (NTRS)

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

Wells, V. L.; Han, A. Y.; Crossley, W. A.

1995-01-01

284

Control of Complex Systems Using Bayesian Networks and Genetic Algorithm  

E-print Network

A method based on Bayesian neural networks and genetic algorithm is proposed to control the fermentation process. The relationship between input and output variables is modelled using Bayesian neural network that is trained using hybrid Monte Carlo method. A feedback loop based on genetic algorithm is used to change input variables so that the output variables are as close to the desired target as possible without the loss of confidence level on the prediction that the neural network gives. The proposed procedure is found to reduce the distance between the desired target and measured outputs significantly.

Marwala, Tshilidzi

2007-01-01

285

Genetic algorithm dose minimization for an operational layout.  

SciTech Connect

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

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

2002-01-01

286

Genetic Algorithm Modeling with GPU Parallel Computing Technology  

E-print Network

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from a multi-core CPU serial implementation, named GAME, already scientifically successfully tested and validated on astrophysical massive data classification problems, through a web application resource (DAMEWARE), specialized in data mining based on Machine Learning paradigms. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm has provided an exploit of the internal training features of the model, permitting a strong optimization in terms of processing performances and scalability.

Cavuoti, Stefano; Brescia, Massimo; Pescap, Antonio; Longo, Giuseppe; Ventre, Giorgio

2012-01-01

287

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

2014-06-16

288

Application of genetic algorithm to hexagon-based motion estimation.  

PubMed

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

Kung, Chih-Ming; Cheng, Wan-Shu; Jeng, Jyh-Horng

2014-01-01

289

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

290

The ordered clustered travelling salesman problem: a hybrid genetic algorithm.  

PubMed

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

291

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

292

Concurrent genetic algorithms for optimization of large structures  

SciTech Connect

In a recent article, the writers presented an augmented Lagrangian genetic algorithm for optimization of structures. The optimization of large structures such as high-rise building structures and space stations with several hundred members by the hybrid genetic algorithm requires the creation of thousands of strings in the population and the corresponding large number of structural analyses. In this paper, the writers extend their previous work by presenting two concurrent augmented Lagrangian genetic algorithms for optimization of large structures utilizing the multiprocessing capabilities of high-performance computers such as the Cray Y-MP 8/864 supercomputer. Efficiency of the algorithms has been investigated by applying them to four space structures including two high-rise building structures. It is observed that the performance of both algorithms improves with the size of the structure, making them particularly suitable for optimization of large structures. A maximum parallel processing speed of 7.7 is achieved for a 35-story tower (with 1,262 elements and 936 degrees of freedom), using eight processors. 9 refs.

Adeli, H.; Cheng, N. (Ohio State Univ., Columbus, OH (United States))

1994-07-01

293

Economic Dispatch Using Genetic Algorithm Based Hybrid Approach  

SciTech Connect

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

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

2006-07-01

294

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

295

Asynchronous parallelism in steady-state genetic algorithms  

NASA Astrophysics Data System (ADS)

Genetic algorithms (GAs) are becoming increasingly popular for signal detection, often in conjunction with neural networks. The time-intensive nature of these techniques has fostered an interest in parallel implementations. Genitor is a widely used algorithm belonging to the class of steady-state GAs which are generally believed to contain little exploitable parallelism. Parallel versions have involved fundamental changes to the algorithm by introducing islands. This paper describes how Genitor can be parallelized virtually as is, with nearly linear speedup, by rearranging the order of some of the genetic operations. An analytical method is derived which can be used for determining the amount of parallelism that can be achieved. An implementation for a shared-memory machine is described, and the resulting execution is shown to support the analysis.

Gordon, Vahl S.

1994-06-01

296

A biased random-key genetic algorithm for data clustering.  

PubMed

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

Festa, P

2013-09-01

297

Genetic algorithm with age structure and its application to self-organizing manufacturing system  

Microsoft Academic Search

The genetic algorithm has recently been demonstrated its effectiveness in optimization issues, but it has two major problems: a premature local convergence and a bias by the genetic drift. In order to solve these problems, we propose a new genetic algorithm with an age structure of a continuous generation model. The new genetic algorithm is applied to a self-organizing manufacturing

Naoyuki KUBOTA; Toshio FUKUDA; Fumihito ARAI; Koji SHIMOJIMA

1994-01-01

298

in silico protein recombination: a genetic algorithm applied to template and alignment selection in  

E-print Network

in silico protein recombination: a genetic algorithm applied to template and alignment selection error estimates on final model +FR #12;chromosome evolution & computational analogy: genetic algorithms genetic algorithm applied to Comparative Modelling ·how are solutions coded? ·genetic operators

Moreira, Bruno Contreras

299

Combining Case-Based Memory with Genetic Algorithm Search for Competent Game AI  

E-print Network

Combining Case-Based Memory with Genetic Algorithm Search for Competent Game AI Sushil J Louis-injected genetic algorithms for learning how to competently play computer strategy games. Case-injected genetic algo- rithms combine genetic algorithm search with a case-based memory of past problem solving attempts

Louis, Sushil J.

300

Lecture 15 Simulated Annealing and Genetic Algorithm Weinan E1,2  

E-print Network

Lecture 15 Simulated Annealing and Genetic Algorithm Weinan E1,2 and Tiejun Li2 1 Department, tieli@pku.edu.cn No.1 Science Building, 1575 #12;Introduction Simulated Annealing Genetic Algorithm Outline Introduction Simulated Annealing Genetic Algorithm #12;Introduction Simulated Annealing Genetic

Li, Tiejun

301

Combining CaseBased Memory with Genetic Algorithm Search for Competent Game AI  

E-print Network

Combining Case­Based Memory with Genetic Algorithm Search for Competent Game AI Sushil J Louis­injected genetic algorithms for learning how to competently play computer strategy games. Case­injected genetic algo­ rithms combine genetic algorithm search with a case­based memory of past problem solving attempts

Louis, Sushil J.

302

Optimization by hybridization of a genetic algorithm with constraint satisfaction techniques  

E-print Network

Optimization by hybridization of a genetic algorithm with constraint satisfaction techniques techniques and whose constraints are too complex for conventional genetic algorithm. The main idea is the han- dling of sub-domains of the CSP variables by the genetic algorithm. The population of the genetic

Paris-Sud XI, Université de

303

Stochastic search in structural optimization - Genetic algorithms and simulated annealing  

NASA Technical Reports Server (NTRS)

An account is given of illustrative applications of genetic algorithms and simulated annealing methods in structural optimization. The advantages of such stochastic search methods over traditional mathematical programming strategies are emphasized; it is noted that these methods offer a significantly higher probability of locating the global optimum in a multimodal design space. Both genetic-search and simulated annealing can be effectively used in problems with a mix of continuous, discrete, and integer design variables.

Hajela, Prabhat

1993-01-01

304

Multiobjective optimal design of high frequency transformers using genetic algorithm  

Microsoft Academic Search

This paper deals with the multiobjective optimization (MO) design of high frequency (HF) transformers using genetic algorithms (GAs). In its most general form, the design problem requires minimizing the mass or overall dimensions of the core and windings as well as the loss of the transformer while ensuring the satisfaction of a number of constraints. In this contribution, the area

C. Versele; O. Deblecker; J. Lobry

2009-01-01

305

In search of optimal clusters using genetic algorithms  

Microsoft Academic Search

Genetic Algorithms (GAs) are generally portrayed as search procedures which can optimize functions based on a limited sample of function values. In this paper, GAs have been used in an attempt to optimize a specified objective function related to a clustering problem. Several experiments on synthetic and real life data sets show the utility of the proposed method. K-Means is

C. A. Murthy; Nirmalya Chowdhury

1996-01-01

306

A genetic algorithm for optimizing off-farm irrigation scheduling  

Microsoft Academic Search

This paper examines the use of genetic algorithm (GA) optimization to identify water delivery schedules for an open-channel irrigation system. Significant objectives and important constraints are identified for this system, and suitable representations of these within the GA framework are developed. Objectives include maximizing the number of orders that are scheduled to be delivered at the requested time and minimizing

J. B. Nixon; G. C. Dandy; A. R. Simpson

2001-01-01

307

The Evolution of Solid Object Designs using Genetic Algorithms  

Microsoft Academic Search

This paper describes an attempt to enable computers to generate truly novel conceptual designs of three dimensional solid objects by using genetic algorithms (GAs). These designs are represented using spatial partitions of 'stretched cubes' with optional intersecting planes (1). Each individual three-dimensional solid object has its functionality specified by an explicit objective function, which is utilised by GAs to evolve

PETER J BENTLEY; JONATHAN P WAKEFIELD

1996-01-01

308

Statistical Dynamics of the Royal Road Genetic Algorithm  

Microsoft Academic Search

Metastability is a common phenomenon. Many evolutionary processes, both natural and ar- ticial, alternate between periods of stasis and brief periods of rapid change in their behavior. In this paper an analytical model for the dynamics of a mutation-only genetic algorithm (GA) is introduced that identies a new and general mechanism causing metastability in evolutionary dynamics. The GA's population dynamics

Erik van Nimwegen; James P. Crutchelda; Melanie Mitchell

309

Statistical Dynamics of the Royal Road Genetic Algorithm  

Microsoft Academic Search

Metastability is a common phenomenon. Many evolutionary processes, both natural and artificial, alternate between periods of stasis and brief periods of rapid change in their behavior. In this paper 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 is

Erik Van Nimwegen; James P. Crutchfield; Melanie Mitchell

1999-01-01

310

Crossover Improvement for the Genetic Algorithm in Information Retrieval.  

ERIC Educational Resources Information Center

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

Vrajitoru, Dana

1998-01-01

311

A Parallel Genetic Algorithm for Automated Electronic Circuit Design  

NASA Technical Reports Server (NTRS)

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

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

2000-01-01

312

USING GENETIC ALGORITHMS TO DESIGN ENVIRONMENTALLY FRIENDLY PROCESSES  

EPA Science Inventory

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

313

Applying Genetic Algorithms To Query Optimization in Document Retrieval.  

ERIC Educational Resources Information Center

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

Horng, Jorng-Tzong; Yeh, Ching-Chang

2000-01-01

314

Calibration of VISSIM for shanghai expressway using genetic algorithm  

Microsoft Academic Search

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

Wu Zhizhou; Sun Jian; Yang Xiaoguang

2005-01-01

315

Calibration of VISSIM for Shanghai expressway using genetic algorithm  

Microsoft Academic Search

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

Wu Zhizhou; Sun Jian; Yang Xiaoguang

2005-01-01

316

A genetic algorithm approach to piping route path planning  

Microsoft Academic Search

A genetic algorithm (GA) approach to support interactive planning of a piping route path in plant layout design is presented. To present this approach, the paper mainly describes the basic ideas used in the methodology, which include the definition of genes to deal with pipe routes, the concept of spatial potential energy, the method of generating initial individuals for GA

TERUAKI ITO

1999-01-01

317

Simulating Gender Separation and Mating Constraints for Genetic Algorithms  

E-print Network

of the sexual reproduction and of the mating schemes has been an interest of research in GAs and evolutionary and evolved into more intelligent ones. In this context, sexual reproduction is the most important mechanism various reproduction modes and types restrictions from nature with the genetic algorithms. We con- sider

Vrajitoru, Dana

318

A parallel hybrid genetic algorithm for multiple protein sequence alignment  

Microsoft Academic Search

This paper presents a parallel hybrid genetic algorithm (GA) for solving sum-of-pairs multiple protein sequence alignment. The method is based on a multiple population GENITOR-type GA and involves local search heuristics. It is then extended to parallel to exploit the benefit of a multiprocessor system. Benchmarks from the BAliBASE library are used to validate the method

Hung Dinh Nguyen; Ikuo YOSHIHARA; Kunihito YAMAMORI; Moritoshi YASUNAGA

2002-01-01

319

Hybrid genetic algorithm based detection schemes for synchronous CDMA systems  

Microsoft Academic Search

We applied a hybrid genetic algorithm (GA) scheme as a suboptimal multiuser detection technique in bit-synchronous code division multiple access (CDMA) systems over a Gaussian channel as well as over a single-path Rayleigh fading channel. The proposed hybrid GA scheme attempts to search for the users' transmitted bit sequence that optimizes the correlation metric employed. Simulation results showed that the

K. Yen; L. Hanzo

2000-01-01

320

A hybrid genetic algorithm for component sequencing and feeder arrangement  

Microsoft Academic Search

This paper presents a hybrid genetic algorithm to optimize the sequence of component placements on a printed circuit board and the arrangement of component types to feeders simultaneously for a pick-and-place machine with multiple stationary feeders, a fixed board table and a movable placement head. The objective of the problem is to minimize the total traveling distance, or the traveling

William Ho; Ping Ji

2004-01-01

321

Hybrid genetic algorithm for transmitter location in wireless networks  

Microsoft Academic Search

Site selection for transmitters in wireless networks is a complex, time-consuming process. Most often, transmitters are located in one of two ways: manually, or through the use of simple geometric models. Unfortunately, each of these methods disregards the most important geographic information affecting the performance of transmitters. This paper introduces a hybrid genetic algorithm designed to automate the site selection

R. M. Krzanowski; J. Raper

1999-01-01

322

An improved hybrid genetic algorithm for the generalized assignment problem  

Microsoft Academic Search

We consider the generalized assignment problem in which the objective is to find a minimum cost assignment of a set of jobs to a set of agents subject to resource constraints. The presented new approach is based on a previously published, successful hybrid genetic algorithm and includes as new features two alternative initialization heuristics, a modified selection and replacement scheme

Harald Feltl; Gnther R. Raidl

2004-01-01

323

A HYBRID GENETIC ALGORITHM APPROACH FOR OSPF WEIGHT SETTING PROBLEM  

Microsoft Academic Search

In this paper, a method based on hybrid genetic algorithm (GA), is presented to optimize administrative weights for OSPF routing. This method can be seen as an alternative to the local- search method in (1) or another GA-based method in (8,10). However, the GA as well as the objective function we use are different. Instead of minimizing a convex cost

Eueung Mulyana; Ulrich Killat

2002-01-01

324

Craniofacial Superimposition in Forensic Identification using Genetic Algorithms  

E-print Network

at the University of Granada. 1 Introduction Forensic anthropology is best conceptualized more broadly as a field the skeleton. This way, the most important application of forensic anthropology is the identification of humanCraniofacial Superimposition in Forensic Identification using Genetic Algorithms Lucia Ballerini1

Granada, Universidad de

325

Genetic Algorithm Based Damage Control For Shipboard Power Systems  

E-print Network

-population genetic algorithm ........................................ 14 3.1 Notional NGIPS DC zonal IFTP system .................................................... 17 3.2 Bus transfer switch... zonal system ......................................................................................... 29 4.4 Chromosome structure for one zone: incoming switches .......................... 29 4.5 Chromosome structure for one zone: non-vital DC...

Amba, Tushar

2010-07-14

326

Feature Subset Selection, Class Separability, and Genetic Algorithms  

Microsoft Academic Search

The performance of classiflcation algorithms in machine learn- ing is afiected by the features used to describe the labeled examples pre- sented to the inducers. Therefore, the problem of feature subset selection has received considerable attention. Genetic approaches to this problem usually follow the wrapper approach: treat the inducer as a black box that is used to evaluate candidate feature

Erick Cant-paz

2004-01-01

327

Nonlinearity, Hyperplane Ranking and the Simple Genetic Algorithm  

E-print Network

to their useful­ ness. Whitley et al. [4] developed a metric OE which can be used to measure the consistency et al. [4] show that during the first few generations of a genetic algorithm the dynamic ranking of using randomly generated functions (as Whitley et al. [4]), we look at functions with differing degrees

Whitley, Darrell

328

Representation, Search and Genetic Algorithms Darrell Whitley Soraya B. Rana  

E-print Network

Representation, Search and Genetic Algorithms Darrell Whitley Soraya B. Rana Computer Science a Gray coded representation is better than Binary in the sense that on average it induces fewer minima as it relates to representations of optimization and search problems that are coded as bit strings of length L

Whitley, Darrell

329

Inductive character learning and classification with genetic algorithms  

Microsoft Academic Search

Adaptive-image learning and discrimination techniques using classifier systems are presented. The genetic algorithm (GA) is used for a learning strategy in the system. The proposed system learns arbitrary image objects without any prior knowledge of given images and recognizes them. The system also makes up for some general weak points that are present in most learning systems including conventional classifier

Alastair D. McAulay; Jae Chan Oh

1991-01-01

330

Using Genetic Algorithms for Solving Hard Problems in GIS  

E-print Network

Using Genetic Algorithms for Solving Hard Problems in GIS Steven van Dijk Dirk Thierens Mark de in Geographical Information Systems (GIS's). The framework is especially suited for geographical problems since as well. 1 Introduction Geographic Information Systems (GIS's for short) combine a geographical database

Utrecht, Universiteit

331

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.

332

An Adaptive Penalty Approach for Constrained GeneticAlgorithm Optimization  

E-print Network

). These include: 1. Rejection of infeasible solutions (the death penalty). 2. Using a mapping function so that allAn Adaptive Penalty Approach for Constrained Genetic­Algorithm Optimization Khaled Rasheed shehata@cs.rutgers.edu ABSTRACT In this paper we describe a new adaptive penalty approach for handling

Rasheed, Khaled

333

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.

334

OPTIMUM ACTUATOR SELECTION WITH A GENETIC ALGORITHM FOR AIRCRAFT CONTROL  

E-print Network

OPTIMUM ACTUATOR SELECTION WITH A GENETIC ALGORITHM FOR AIRCRAFT CONTROL JAMES L. ROGERS NASA Langley Research Center ABSTRACT: The placement of actuators on a wing determines the control axes, while minimizing the coupling. For example, the desired actuators produce a pure roll moment

Coello, Carlos A. Coello

335

SEXUAL SELECTION WITH COMPETITIVE/COOPERATIVE OPERATORS FOR GENETIC ALGORITHMS  

E-print Network

,jxbg@cs.bham.ac.uk ABSTRACT In a standard genetic algorithm (GA), individuals repro­ duce asexually: any two organisms may reproductive success depends on interaction with a partner of the oppo­ site sex to produce offspring [23­ ponents, namely the Direct fitness resulting from personal reproduction, and which we shall adapt to our

Bullinaria, John

336

Experiences with the PGAPack Parallel Genetic Algorithm library  

SciTech Connect

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

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

1997-07-01

337

Optimal design of the magnetic microactuator using the genetic algorithm  

Microsoft Academic Search

This paper presents the optimal design of the magnetic microactuator using the genetic algorithm. The magnetic microactuator is composed of an enclosed core and a permalloy plate to form a closed magnetic circuit. The present design allows the area of the magnetic poles to be optimally enlarged and achieve a maximum force generation. To obtain the optimal geometry and maximum

C. H. Ko; J. C. Chiou

2003-01-01

338

Optimization of classification tasks by using genetic algorithms  

Microsoft Academic Search

We present an attempt to separate between two kinds of events, using Genetic Algorithms. Events were produced by a Monte Carlo generator and characterized by the most discriminant variables. For the separation between events, two approaches are investigated. First, discriminant function parameters and neural network connection weights are optimized. In a multidimensional search approach, hyper-planes and hyper-surfaces are computed. In

Mostafa Mjahed

2010-01-01

339

Optimum detailed design of reinforced concrete frames using genetic algorithms  

Microsoft Academic Search

This article presents the application of the genetic algorithm to the optimum detailed design of reinforced concrete frames based on Indian Standard specifications. The objective function is the total cost of the frame which includes the cost of concrete, formwork and reinforcing steel for individual members of the frame. In order for the optimum design to be directly constructible without

V. Govindaraj; J. V. Ramasamy

2007-01-01

340

Flexural Design of Reinforced Concrete Frames Using a Genetic Algorithm  

Microsoft Academic Search

A design procedure implementing a genetic algorithm is developed for discrete optimization of reinforced concrete frames ~RC-GA!. The design procedure conforms to the American Concrete Institute ~ACI! Building Code and Commentary. The objective of the RC-GA procedure is to minimize the material and construction costs of reinforced concrete structural elements subjected to serviceability and strength requirements described by the ACI

Charles V. Camp; Shahram Pezeshk; Ha?kan Hansson

2003-01-01

341

An efficient constraint handling method for genetic algorithms  

Microsoft Academic Search

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

Kalyanmoy Deb

2000-01-01

342

An Efficient Constraint Handling Method for Genetic Algorithms  

Microsoft Academic Search

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

Kalyanmoy Deb

1998-01-01

343

Comparison between Genetic Algorithms and Particle Swarm Optimization  

Microsoft Academic Search

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

Russell C. Eberhart; Yuhui Shi

1998-01-01

344

A Niched Pareto Genetic Algorithm for Multiobjective Optimization  

Microsoft Academic Search

Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple objectives by incorporating the concept of Pareto domination

Jeffrey Horn; Nicholas Nafpliotis; David E. Goldberg

1994-01-01

345

Evolving Networks: Using the Genetic Algorithm with Connectionist Learning  

Microsoft Academic Search

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

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

1990-01-01

346

Genetic algorithms for nondestructive testing in crack identification  

Microsoft Academic Search

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

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

1994-01-01

347

Using Genetic Algorithms to Converge on Molecules with Specific Properties  

Microsoft Academic Search

Although it can be a straightforward matter to determine the properties of a molecule from its structure, the inverse problem is much more difficult. We have chosen to generate molecules by using a genetic algorithm, a computer simulation that models biological evolution and natural selection. By creating a population of randomly generated molecules, we can apply a process of selection,

Stephen Foster; Nathan Lindzey; Jon Rogers; Carl West; Walt Potter; Sean Smith; Steven Alexander

2007-01-01

348

Application of a genetic algorithm to wind turbine design  

Microsoft Academic Search

This paper presents an optimization method for stall-regulated horizontal-axis wind turbines. A hybrid approach is used that combines the advantages of a genetic algorithm with an inverse design method. This method is used to determine the optimum blade pitch and blade chord and twist distributions that maximize the annual energy production. To illustrate the method, a family of 25 wind

M. S. Selig; V. L. Coverstone-Carroll

1996-01-01

349

Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling  

Microsoft Academic Search

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

R. J. Abrahart

2004-01-01

350

The Proportional Genetic Algorithm Representation Annie S. Wu  

E-print Network

The Proportional Genetic Algorithm Representation Annie S. Wu School of EECS University of Central into expressed values. In both cases, Expressed value = V i;min + pct(V i ) \\Theta (V i;max \\Gamma V i;min ): Vmin and Vmax are predefined constants. Value V Vmin Vmax # positive char(V ) # negative char(V ) pct(V

Wu, Annie S.

351

Improving Digital Video Commercial Detectors With Genetic Algorithms  

Microsoft Academic Search

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

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

2002-01-01

352

LSGA: combining level-sets and genetic algorithms for segmentation  

Microsoft Academic Search

A novel technique is presented to combine genetic algorithms (GAs) with level-set functions to segment objects with known shapes and variabilities on images. The individuals of the GA, also known as chromosomes consist of a sequence of parameters of a level-set function. Each chromosome represents a unique segmenting contour. An initial population of segmenting contours is generated based on the

Melanie Mitchell; Judith Gold

2010-01-01

353

Segmentation of thermographic images of hands using a genetic algorithm  

Microsoft Academic Search

This paper presents a new technique for segmenting thermographic images using a genetic algorithm (GA). The individuals of the GA also known as chromosomes consist of a sequence of parameters of a level set function. Each chromosome represents a unique segmenting contour. An initial population of segmenting contours is generated based on the learned variation of the level set parameters

Payel Ghosh; Melanie Mitchell; Judith Gold

2010-01-01

354

Vibrational genetic algorithm as a new concept in airfoil design  

Microsoft Academic Search

We introduce the Vibration concept for real coded Genetic Algorithm and its implementation to inverse airfoil design, which decreases the number of CFD calculations. This concept assures efficient diversity in the population and consequently gives faster solution. We used the Vibration concept as vibrational mutation and vibrational crossover. For the mutational manner, a sinusoidal wave with random amplitude is introduced

Abdurrahman Hacio?lu; ?brahim zkol

2002-01-01

355

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

356

Genetic Algorithms Can Improve the Construction of D-Optimal Experimental Designs  

E-print Network

algorithms for constructing D- optimal designs are Monte Carlo algorithms, heuristics, that base on the idea that are D-optimal. To this purpose, we use standard Monte Carlo algorithms such as DETMAX and k better results. Key-Words: - Genetic Algorithm, Memetic Algorithm, Design of Experiments, DOE, D

Zell, Andreas

357

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

358

Application of Genetic Algorithm in the Optimization of Water Pollution Control Scheme  

Microsoft Academic Search

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

Rui-Ming Zhao; Dong-Ping Qian

2007-01-01

359

Performance Analysis of Estimation of Distribution Algorithm and Genetic Algorithm in Zone Routing Protocol  

E-print Network

In this paper, Estimation of Distribution Algorithm (EDA) is used for Zone Routing Protocol (ZRP) in Mobile Ad-hoc Network (MANET) instead of Genetic Algorithm (GA). It is an evolutionary approach, and used when the network size grows and the search space increases. When the destination is outside the zone, EDA is applied to find the route with minimum cost and time. The implementation of proposed method is compared with Genetic ZRP, i.e., GZRP and the result demonstrates better performance for the proposed method. Since the method provides a set of paths to the destination, it results in load balance to the network. As both EDA and GA use random search method to reach the optimal point, the searching cost reduced significantly, especially when the number of data is large.

Rahman, Mst Farhana; Ripon, Kazi Shah Nawaz; Suvo, Md Iqbal Hossain

2010-01-01

360

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

361

A Genetic Algorithm Approach to Multiple-Response Optimization  

SciTech Connect

Many designed experiments require the simultaneous optimization of multiple responses. A common approach is to use a desirability function combined with an optimization algorithm to find the most desirable settings of the controllable factors. However, as the problem grows even moderately in either the number of factors or the number of responses, conventional optimization algorithms can fail to find the global optimum. An alternative approach is to use a heuristic search procedure such as a genetic algorithm (GA). This paper proposes and develops a multiple-response solution technique using a GA in conjunction with an unconstrained desirability function. The GA requires that several parameters be determined in order for the algorithm to operate effectively. We perform a robust designed experiment in order to tune the genetic algorithm to perform well regardless of the complexity of the multiple-response optimization problem. The performance of the proposed GA method is evaluated and compared with the performance of the method that combines the desirability with the generalized reduced gradient (GRG) optimization. The evaluation shows that only the proposed GA approach consistently and effectively solves multiple-response problems of varying complexity.

Ortiz, Francisco; Simpson, James R.; Pignatiello, Joseph J.; Heredia-Langner, Alejandro

2004-10-01

362

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

363

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

364

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

NASA Astrophysics Data System (ADS)

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

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

2011-12-01

365

Using genetic algorithms to construct a network for financial prediction  

NASA Astrophysics Data System (ADS)

Traditional forecasting models such as the Box-Jenkins ARIMA model are almost all based on models that assume a linear relationship amongst variables and cannot approximate the non- linear relationship that exists amongst variables in real-world data such as stock-price data. Artificial neural networks, on the other hand, consist of two or more levels of nonlinearity that have been successfully used to approximate the underlying relationships of time series data. Neural networks however, pose a design problem: their optimum topology and training rule parameters including learning rate and momentum, for the problem at hand need to be determined. In this paper, we use genetic algorithms to determine these design parameters. In general genetic algorithms are an optimization method that find solutions to a problem by an evolutionary process based on natural selection. The genetic algorithm searches through the network parameter space and the neural network learning algorithm evaluates the selected parameters. We then use the optimally configured network to predict the stock market price of a blue-chip company on the UK market.

Patel, Devesh

1996-03-01

366

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

367

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

368

A QoS multicast routing algorithm based on tabu-hierarchy genetic algorithm in IP/DWDM optical Internet  

NASA Astrophysics Data System (ADS)

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

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

2005-11-01

369

Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization  

NASA Technical Reports Server (NTRS)

We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these properties, setting up the additional population is trivial making implementation no more difficult than using a standard GA. Empirical results using a suite of two-objective test functions indicate that this CGA performs well at finding solutions on convex, nonconvex, discrete, and deceptive Pareto-optimal fronts, while giving respectable results on a nonuniform optimization. On a multimodal Pareto front, the CGA finds a solution that dominates solutions produced by eight other algorithms, yet the CGA has poor coverage across the Pareto front.

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

2002-01-01

370

An Airborne Conflict Resolution Approach Using a Genetic Algorithm  

NASA Technical Reports Server (NTRS)

An airborne conflict resolution approach is presented that is capable of providing flight plans forecast to be conflict-free with both area and traffic hazards. This approach is capable of meeting constraints on the flight plan such as required times of arrival (RTA) at a fix. The conflict resolution algorithm is based upon a genetic algorithm, and can thus seek conflict-free flight plans meeting broader flight planning objectives such as minimum time, fuel or total cost. The method has been applied to conflicts occurring 6 to 25 minutes in the future in climb, cruise and descent phases of flight. The conflict resolution approach separates the detection, trajectory generation and flight rules function from the resolution algorithm. The method is capable of supporting pilot-constructed resolutions, cooperative and non-cooperative maneuvers, and also providing conflict resolution on trajectories forecast by an onboard FMC.

Mondoloni, Stephane; Conway, Sheila

2001-01-01

371

A Genetic Algorithm to Optimize a Tweet for Retweetability  

E-print Network

Twitter is a popular microblogging platform. When users send out messages, other users have the ability to forward these messages to their own subgraph. Most research focuses on increasing retweetability from a node's perspective. Here, we center on improving message style to increase the chance of a message being forwarded. To this end, we simulate an artificial Twitter-like network with nodes deciding deterministically on retweeting a message or not. A genetic algorithm is used to optimize message composition, so that the reach of a message is increased. When analyzing the algorithm's runtime behavior across a set of different node types, we find that the algorithm consistently succeeds in significantly improving the retweetability of a message.

Hochreiter, Ronald

2014-01-01

372

A modified genetic algorithm for precise determination the geometrical orbital elements of binary stars  

Microsoft Academic Search

The paper presents a modified genetic algorithm called adapted genetic algorithm with adjusting population size (AGA-POP) for precise determination the orbital elements of binary stars. The proposed approach is a simple, robust way that can be considered to be a new member in the class of self organizing genetic algorithms. The proposed AGA-POP is applied on the star ? Bootis

Abdel-Fattah Attia; Eman Mahmoud; H. I. Shahin; A. M. Osman

2009-01-01

373

The self-organization genetic algorithm based on the mutation with cycle probabilities  

Microsoft Academic Search

First, a cycle mutation genetic algorithm (CMGA) is designed by simulating the evolutionary rule of the earth creature found by paleontologists in the paper. Then, according to some phenomena of the population genetics, an improved cycle mutation genetic algorithm (ICMGA) is schemed by mended the selection operator of CMGA. Last, 22 functions are tested by ICMGA and other evolution algorithms

Baojuan Huang; Jian Zhuang; Dehong Yu

2008-01-01

374

Statistical maritime radar duct estimation using hybrid genetic algorithmMarkov  

E-print Network

Statistical maritime radar duct estimation using hybrid genetic algorithm­Markov chain Monte Carlo estimation using hybrid genetic algorithm­Markov chain Monte Carlo method, Radio Sci., 42, RS3014, doi:10 work, genetic algorithms (GA) and Markov chain Monte Carlo (MCMC) samplers were used to calculate

Buckingham, Michael

375

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; Jos L. Verdegay

1998-01-01

376

The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance  

E-print Network

and study artificial life. Genetic algorithms (GAs) [13, 9] are an idealized computational modelThe Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance Melanie Mitchell AI Dept. of Psychology University of Michigan Ann Arbor, MI 48109 Abstract Genetic algorithms (GAs) play

Mitchell, Melanie

377

Enhanced Direct and Indirect Genetic Algorithm Approaches for a Mall Layout and Tenant Selection Problem  

E-print Network

Enhanced Direct and Indirect Genetic Algorithm Approaches for a Mall Layout and Tenant Selection, it was recognised that in order to be successful with an indirect genetic algorithm approach using a decoder the genetic algorithm itself, simultaneously to solving the problem, sets weights to balance the components

Aickelin, Uwe

378

Enhancement of the Shifting Balance Genetic Algorithm for Highly Multimodal Problems  

E-print Network

Enhancement of the Shifting Balance Genetic Algorithm for Highly Multimodal Problems Jun Chen Email: wineberg@cis.uoguelph.ca Abstract- The Shifting Balance Genetic Algorithm (SBGA) is an extension of the Genetic Algorithm (GA) that was created to promote guided diversity to improve performance in highly

Wineberg, Mark

379

A Distributed Genetic Algorithm for Parameters Optimization to Detect Microcalcifications in  

E-print Network

A Distributed Genetic Algorithm for Parameters Optimization to Detect Microcalcifications obtained by applying a distributed genetic algorithm to a problem of parameter op- timization in medical the performance of our system. A distributed genetic algorithm supervising this process allowed to improve of some

Lanconelli, Nico

380

Non-Uniform search domain based Genetic algorithm for the optimization of real time FFT Processor  

E-print Network

Non-Uniform search domain based Genetic algorithm for the optimization of real time FFT Processor case scenario. This paper presents a Genetic Algorithm for the optimization of word length for both using Genetic Algorithm (GA). The solutions found by the GA are then evaluated for power evaluation. GA

Arslan, Tughrul

381

Polygonal Approximation of Digital Curves Using a Multi-objective Genetic Algorithm  

E-print Network

Polygonal Approximation of Digital Curves Using a Multi-objective Genetic Algorithm Herve Locteau, a polygonal approximation approach based on a multi- objective genetic algorithm is proposed. In this method][17][18]. It consists in using Genetic Algorithms in order to find a near- optimal polygonal approximation

Paris-Sud XI, Université de

382

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

E-print Network

Local 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 Corresponding author. #12;1 Local Search Genetic Algorithm for Optimal Design of Reliable Networks Abstract

Smith, Alice E.

383

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 is motivated by the fact that, while evolution programs (EPs) in general and genetic algorithms in particular

Kimbrough, Steven Orla

384

Process Synthesis of Laser Forming by Genetic Algorithm Jin Cheng and Y. Lawrence Yao  

E-print Network

Process Synthesis of Laser Forming by Genetic Algorithm Jin Cheng and Y. Lawrence Yao Department reports an effort towards this end using genetic algorithms (GAs). The problem is formulated for a class such as genetic algorithms (GAs) because the complexity involved in these processes does not lend themselves

Yao, Y. Lawrence

385

A Hardware Genetic Algorithm for the Traveling Salesman Problem on Splash2  

E-print Network

A Hardware Genetic Algorithm for the Traveling Salesman Problem on Splash2 Paul Graham and Brent in hardware. In this paper, we describe the Splash 2 Parallel Genetic Algorithm (SPGA), which is a parallel quickly than single processor and software­based implementations of the genetic algorithm. The four

Nelson, Brent E.

386

New applications of the genetic algorithm for the interpretation of high-resolution spectra1  

E-print Network

804 New applications of the genetic algorithm for the interpretation of high-resolution spectra1 W. An alternative approach is unassigned fits of the spectra using genetic algorithms (GAs) with special cost, genetic algorithm, biomolecules, structure, van der Waals clusters. Résumé : La spectroscopie électronique

Nijmegen, University of

387

Sorting Permutations by Reversals through a Hybrid Genetic Algorithm based on Breakpoint Elimination  

E-print Network

Sorting Permutations by Reversals through a Hybrid Genetic Algorithm based on Breakpoint belongs to P. In this paper, a standard genetic algorithm for solving the problem of sorting by reversals, an improved genetic algorithm is proposed, that in the initial generations applies reversals

Ayala-Rincón, Mauricio

388

Graph Classification Using Genetic Algorithm and Graph Probing Application to Symbol Recognition  

E-print Network

Graph Classification Using Genetic Algorithm and Graph Probing Application to Symbol Recognition classification approach using genetic algorithm and a fast dissimilarity measure between graphs called graph this learning set, a graph based Genetic Algorithm (GA) is applied. Its aim is to generate a set of K graph

Paris-Sud XI, Université de

389

Local Search Genetic Algorithm for Optimization of Highly Reliable Communications Networks  

E-print Network

Local Search Genetic Algorithm for Optimization of Highly Reliable Communications Networks Berna Turkey berna@rorqual.cc.metu.edu.tr Abstract This paper presents a genetic algorithm (GA. Genetic algorithms (GA) have recently found their way in combinatorial optimization approaches to reliable

Smith, Alice E.

390

Genetic algorithms in astronomy and astrophysics Vinesh Rajpaul1,2  

E-print Network

Genetic algorithms in astronomy and astrophysics Vinesh Rajpaul1,2 1 Astrophysics, Cosmology-mail: vinesh.rajpaul@uct.ac.za Abstract. Genetic algorithms (GAs) emulate the process of biological evolution-called genetic algorithms form one of the most successful subsets, and certainly the most popular subset

Masci, Frank

391

Application of genetic algorithm to the calculation of bound states and local density approximations  

E-print Network

Application of genetic algorithm to the calculation of bound states and local density; accepted 16 November 1994 A novel method, based on genetic algorithms, has been developed and applied. © 1995 American Institute of Physics. I. INTRODUCTION Genetic algorithms GA are global optimization meth

Zeiri, Yehuda

392

Automated Design of Steel Open Web Joist Floor Framing Systems Using a Genetic Algorithm  

E-print Network

Automated Design of Steel Open Web Joist Floor Framing Systems Using a Genetic Algorithm systems utilizing a genetic algorithm (GA), which utilizes a search strategy that is modeled on the same design options are perfectly suited to the genetic algorithm. The GA has been used successfully

Foley, Christopher M.

393

INDUCING PARAMETERS OF A DECISION TREE FOR EXPERT SYSTEM SHELL MCESE BY GENETIC ALGORITHM  

E-print Network

INDUCING PARAMETERS OF A DECISION TREE FOR EXPERT SYSTEM SHELL MCESE BY GENETIC ALGORITHM I. Bruha-mail: {bruha | franya}@mcmaster.ca KEYWORDS Expert system shell, genetic algorithms, rule-based sys- tems for the whole knowledge base. Genetic algorithms comprise a long process of evolution of a large population

Franek, Frantisek

394

A Study and Improvement of the Genetic Algorithm in the CAMBrain Machine  

E-print Network

A Study and Improvement of the Genetic Algorithm in the CAM­Brain Machine Yvan Saeys and Herwig Van directly in hardware under the control of a built-in genetic algorithm that guides the evolution. The goal was to analyse this existing genetic algorithm in the CBM, discover some of its weaknesses and present a better

Gent, Universiteit

395

Beyond the Rotamer Library: Genetic Algorithm Combined with the Disturbing Mutation Process for Upbuilding  

E-print Network

Beyond the Rotamer Library: Genetic Algorithm Combined with the Disturbing Mutation Process, China ABSTRACT The disturbing genetic algorithm, incorporating the disturbing mutation process into the genetic algorithm flow, has been developed to extend the searching space of side-chain conforma- tions

Luhua, Lai

396

Simulated annealing, weighted simulated an nealing and genetic algorithm at work  

E-print Network

Simulated annealing, weighted simulated an­ nealing and genetic algorithm at work Fran and genetic algorithm are compared when using a sample to minimize an objective function which efficient than the genetic algorithm. With regard to the bias problem, the randomly weighted version

Besse, Philippe

397

Novel Use of a Genetic Algorithm for Protein Structure Prediction: Searching Template and Sequence  

E-print Network

Novel Use of a Genetic Algorithm for Protein Structure Prediction: Searching Template and Sequence Laboratories, London, United Kingdom ABSTRACT A novel genetic algorithm was ap- plied to all CASP5 targets recognition; comparative model- ing; genetic algorithms; template selec- tion; alignment errors INTRODUCTION

Moreira, Bruno Contreras

398

A Genetic Algorithm-Based Controller for Decentralized Multi-Agent Robotic Systems  

E-print Network

A Genetic Algorithm-Based Controller for Decentralized Multi-Agent Robotic Systems Proceedings, using genetic algorithms, producing a generation of robots with superior task performance, compared that perform specific tasks in an environment is evolved by using genetic algorithms, hence producing a better

Kansas, University of

399

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 Genetic algorithms are commonly used to perform searches on complex search spaces for optimum solutions spaces subject to the use of a genetic algorithm as an optimization technique for field programmable gate

Wu, Annie S.

400

Multi-Agent Learning with a Distributed Genetic Algorithm Exploring Innovation Diffusion on Networks  

E-print Network

Multi-Agent Learning with a Distributed Genetic Algorithm Exploring Innovation Diffusion in a genetic algorithm (GA) solving a shared problem. We examine two questions: (1) How does the network by showing how this model can be useful not only for multi-agent learning, but also for genetic algorithms

Wilensky, Uri

401

Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model  

E-print Network

Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model) and the inversion of a canopy radiative transfer (RT) model. In recent years, applications of the genetic algorithms area index; Genetic algorithms; Radiative transfer; Inversion; Landsat-7; ETM+ 1. Introduction Land

Liang, Shunlin

402

Optimizing core-shell nanoparticle catalysts with a genetic algorithm Nathan S. Froemming and Graeme Henkelmana  

E-print Network

Optimizing core-shell nanoparticle catalysts with a genetic algorithm Nathan S. Froemming 2009 A genetic algorithm is used with density functional theory to investigate the catalytic properties to be effective for oxygen reduction. The genetic algorithm starts by creating an initial population of random

Henkelman, Graeme

403

Scope of Gradient and Genetic Algorithms in Multivariable Function Optimization  

NASA Technical Reports Server (NTRS)

Global optimization of a multivariable function - constrained by bounds specified on each variable and also unconstrained - is an important problem with several real world applications. Deterministic methods such as the gradient algorithms as well as the randomized methods such as the genetic algorithms may be employed to solve these problems. In fact, there are optimization problems where a genetic algorithm/an evolutionary approach is preferable at least from the quality (accuracy) of the results point of view. From cost (complexity) point of view, both gradient and genetic approaches are usually polynomial-time; there are no serious differences in this regard, i.e., the computational complexity point of view. However, for certain types of problems, such as those with unacceptably erroneous numerical partial derivatives and those with physically amplified analytical partial derivatives whose numerical evaluation involves undesirable errors and/or is messy, a genetic (stochastic) approach should be a better choice. We have presented here the pros and cons of both the approaches so that the concerned reader/user can decide which approach is most suited for the problem at hand. Also for the function which is known in a tabular form, instead of an analytical form, as is often the case in an experimental environment, we attempt to provide an insight into the approaches focusing our attention toward accuracy. Such an insight will help one to decide which method, out of several available methods, should be employed to obtain the best (least error) output. *

Shaykhian, Gholam Ali; Sen, S. K.

2007-01-01

404

Genetic-algorithm-based path optimization methodology for spatial decision  

NASA Astrophysics Data System (ADS)

In this paper, we proposed a method based on GA to solve the path-optimization problem. Unlike the traditional methods, it considers many other factors besides the road length including the task assignment and its balance, which are beyond the capability of path analysis and make this problem a Combinatorial Optimization problem. It can't be solved by a traditional graph-based algorithm. This paper proposes a new algorithm that integrates the Graph Algorithm and Genetic Algorithm together to solve this problem. The traditional Graph-Algorithm is responsible for preprocessing data and GA is responsible for the global optimization. The goal is to find the best combination of paths to meet the requirement of time, cost and the reasonable task assignment. The prototype of this problem is named the TSP (Traveling Salesman Problem) problem and known as NP-Hard Problem. However, we demonstrate how these problems are resolved by the GA without complicated programming, the result proves it's effective. The technique presented in this paper is helpful to those GIS developer working on an intelligent system to provide more effective decision-making.

Yu, Liang; Bian, Fuling

2006-10-01

405

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

406

A sustainable genetic algorithm for satellite resource allocation  

NASA Technical Reports Server (NTRS)

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

Abbott, R. J.; Campbell, M. L.; Krenz, W. C.

1995-01-01

407

Optimum Actuator Selection with a Genetic Algorithm for Aircraft Control  

NASA Technical Reports Server (NTRS)

The placement of actuators on a wing determines the control effectiveness of the airplane. One approach to placement maximizes the moments about the pitch, roll, and yaw axes, while minimizing the coupling. For example, the desired actuators produce a pure roll moment without at the same time causing much pitch or yaw. For a typical wing, there is a large set of candidate locations for placing actuators, resulting in a substantially larger number of combinations to examine in order to find an optimum placement satisfying the mission requirements and mission constraints. A genetic algorithm has been developed for finding the best placement for four actuators to produce an uncoupled pitch moment. The genetic algorithm has been extended to find the minimum number of actuators required to provide uncoupled pitch, roll, and yaw control. A simplified, untapered, unswept wing is the model for each application.

Rogers, James L.

2004-01-01

408

Design of PID-type controllers using multiobjective genetic algorithms.  

PubMed

The design of a PID controller is a multiobjective problem. A plant and a set of specifications to be satisfied are given. The designer has to adjust the parameters of the PID controller such that the feedback interconnection of the plant and the controller satisfies the specifications. These specifications are usually competitive and any acceptable solution requires a tradeoff among them. An approach for adjusting the parameters of a PID controller based on multiobjective optimization and genetic algorithms is presented in this paper. The MRCD (multiobjective robust control design) genetic algorithm has been employed. The approach can be easily generalized to design multivariable coupled and decentralized PID loops and has been successfully validated for a large number of experimental cases. PMID:12398277

Herreros, Alberto; Baeyens, Enrique; Pern, Jos R

2002-10-01

409

Forecasting Smoothed Non-Stationary Time Series Using Genetic Algorithms  

NASA Astrophysics Data System (ADS)

We introduce kernel smoothing method to extract the global trend of a time series and remove short time scales variations and fluctuations from it. A multifractal detrended fluctuation analysis (MF-DFA) shows that the multifractality nature of TEPIX returns time series is due to both fatness of the probability density function of returns and long range correlations between them. MF-DFA results help us to understand how genetic algorithm and kernel smoothing methods act. Then we utilize a recently developed genetic algorithm for carrying out successful forecasts of the trend in financial time series and deriving a functional form of Tehran price index (TEPIX) that best approximates the time variability of it. The final model is mainly dominated by a linear relationship with the most recent past value, while contributions from nonlinear terms to the total forecasting performance are rather small.

Norouzzadeh, P.; Rahmani, B.; Norouzzadeh, M. S.

410

Designing A Hybrid Genetic Algorithm for the Linear Ordering Problem  

Microsoft Academic Search

The Linear Ordering Problem(LOP), which is a well-known \\u000a \\u000a \\u000a \\u000a \\u000a \\u000a NP\\u000a\\\\mathcal{N}\\\\mathcal{P}\\u000a\\u000a \\u000a \\u000a \\u000a \\u000a \\u000a -hard problem, has numerous applications in various fields. Using this problem as an example, we illustrate a general procedure\\u000a of designing a hybrid genetic algorithm, which includes the selection of crossover\\/mutation operators, accelerating the local\\u000a search module and tuning the parameters. Experimental results show that our hybrid genetic algorithm outperforms

Gaofeng Huang; Andrew Lim

2003-01-01

411

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

412

GAz: A Genetic Algorithm for Photometric Redshift Estimation  

E-print Network

We present a new approach to the problem of estimating the redshift of galaxies from photometric data. The approach uses a genetic algorithm combined with non-linear regression to model the 2SLAQ LRG data set with SDSS DR7 photometry. The genetic algorithm explores the very large space of high order polynomials while only requiring optimisation of a small number of terms. We find a $\\sigma_{\\text{rms}}=0.0408\\pm 0.0006$ for redshifts in the range $0.4

Hogan, Robert; Seeburn, Navin

2014-01-01

413

Genetic algorithms and the analysis of SnIa data  

E-print Network

The Genetic Algorithm is a heuristic that can be used to produce model independent solutions to an optimization problem, thus making it ideal for use in cosmology and more specifically in the analysis of type Ia supernovae data. In this work we use the Genetic Algorithms (GA) in order to derive a null test on the spatially flat cosmological constant model $\\Lambda$CDM. This is done in two steps: first, we apply the GA to the Constitution SNIa data in order to acquire a model independent reconstruction of the expansion history of the Universe $H(z)$ and second, we use the reconstructed $H(z)$ in conjunction with the Om statistic, which is constant only for the $\\Lambda$CDM model, to derive our constraints. We find that while $\\Lambda$CDM is consistent with the data at the $2\\sigma$ level, some deviations from $\\Lambda$CDM model at low redshifts can be accommodated.

Savvas Nesseris

2010-11-08

414

Structural pattern recognition using genetic algorithms with specialized operators  

Microsoft Academic Search

This paper presents a genetic algorithm (GA)-based optimization procedure for structural pattern recognition in a model-based recognition system using attributed relational graph (ARG) matching technique. The objective of our work is to improve the GA-based ARG matching procedures leading to a faster convergence rate and better quality mapping between a scene ARG and a set of given model ARGs. In

K. G. Khoo; Ponnuthurai N. Suganthan

2003-01-01

415

Unit commitment by genetic algorithm with specialized search operators  

Microsoft Academic Search

An approach for solving the unit commitment problem based on genetic algorithm with new search operators is presented. These operators, specific to the problem, are mutation with a probability of bit change depending on load demand, production and start-up costs of the generating units and transposition. The method incorporates time-dependent start-up costs, demand and reserve constraints, minimum up and down

Grzegorz Dudek

2004-01-01

416

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

417

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

418

A hybrid genetic algorithm for production and distribution  

Microsoft Academic Search

This paper develops a hybrid genetic algorithm for production and distribution problems in multi-factory supply chain models. Supply chain problems usually may involve multi-criterion decision-making, for example operating cost, service level, resources utilization, etc. These criteria are numerous and interrelated. To organize them, analytic hierarchy process (AHP) will be utilized. It provides a systematic approach for decision makers to assign

Felix T. S. Chan; S. H. Chung; Subhash Wadhwa

2005-01-01

419

GenJam: A Genetic Algorithm for Generating Jazz Solos  

Microsoft Academic Search

This paper describes GenJam, a genetic algorithm-based model of a novice jazz musician learning to improvise. GenJam maintains hierarchically related populations of melodic ideas that are mapped to specific notes through scales suggested by the chord progression being played. As GenJam plays its solos over the accompaniment of a standard rhythm section, a human mentor gives real-time feedback, which is

John A. Biles

1994-01-01

420

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

421

A genetic algorithm for ground-based telescope observation scheduling  

NASA Astrophysics Data System (ADS)

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

Mahoney, William; Veillet, Christian; Thanjavur, Karun

2012-09-01

422

A quantum genetic algorithm with quantum crossover and mutation operations  

E-print Network

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 which 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.

Akira SaiToh; Robabeh Rahimi; Mikio Nakahara

2013-11-22

423

Nash Genetic Algorithms : examples and applications M. Sefrioui  

E-print Network

Nash Genetic Algorithms : examples and applications M. Sefrioui LIP6, University Paris 6 4, Place. A strategy pair (x; y) 2 E#2;F is said to be a Nash equilibrium iff: fE (x; y) = inf x2E fE (x; y) fF (x; y) = inf y2F fF (x; y) It may also be defined by: u = (u 1 ; : : : ; uG ) is a Nash equilibrium iff: 8i; 8v

Coello, Carlos A. Coello

424

DARWIN: CMOS opamp synthesis by means of a genetic algorithm  

Microsoft Academic Search

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

Wim Kruiskamp; Domine Leenaerts

1995-01-01

425

Designing neuroclassifier fusion system by immune genetic algorithm  

NASA Astrophysics Data System (ADS)

A multiple neural network classifier fusion system design method using immune genetic algorithm (IGA) is proposed. The IGA is modeled after the mechanics of human immunity. By using vaccination and immune selection in the evolution procedures, the IGA outperforms the traditional genetic algorithms in restraining the degenerate phenomenon and increasing the converging speed. The fusion system consists of N neural network classifiers that work independently and in parallel to classify a given input pattern. The classifiers' outputs are aggregated by a fusion scheme to decide the collective classification results. The goal of the system design is to obtain a fusion system with both good generalization and efficiency in space and time. Two kinds of measures, the accuracy of classification and the size of the neural networks, are used by IGA to evaluate the fusion system. The vaccines are abstracted by a self-adaptive scheme during the evolutionary process. A numerical experiment on the 'alternate labels' problem is implemented and the comparisons of IGA with traditional genetic algorithm are presented.

Liang, Jimin; Zhao, Heng; Yang, Wanhai

2001-09-01

426

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

427

Design of lightweight mirror based on genetic algorithm  

NASA Astrophysics Data System (ADS)

Based on genetic algorithm the design of lightweight mirror in a space optical system is presented. At present some novel lightweight techniques for more quick, more exact implementation of the lightweight mirror design are considered. The design of lightweight mirror is a multi-variable and multi-range of value complex discrete variable optimization issue which belongs to combination optimizations issue. Genetic algorithm (GA) has global astringency and parallelism, so employing it for the design of lightweight mirror can provide a global optimization solution. The theory of the method is genetic algorithm is used to be optimization subprogram, links with the finite element code by interface program, and the deformation of mirror surface is computed by emulation analysis while the weight reduction and the influence of dead weight to the deformation have both been taken into account. The lightweight mirror design is exact if the deformation is controlled under a certain tolerance, Pareto optimal solution gather of multi-variable indicating mirror parameters. An example is demonstrated for the lightweight primary mirror of an off-axis three-mirror system. The design objective is the root-mean-square optical surface error under the influence of dead weight satisfying the tolerance, which is controlled under one fortieth of wavelength. The approving deformation of mirror surface is gained by combining finite element analysis and GA, at the same time optimal solution gather about mirror design parameters is received.

Zhang, Wei; Yang, Yi

2006-02-01

428

Genetic Algorithm Optimizes Q-LAW Control Parameters  

NASA Technical Reports Server (NTRS)

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

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

2008-01-01

429

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

430

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

NASA Astrophysics Data System (ADS)

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

Nepomuceno, Juan A.; Troncoso, Alicia; AguilarRuiz, Jess S.

431

Applying genetic algorithm for the bandwidth allocation of ATM nets  

NASA Astrophysics Data System (ADS)

In this paper, we develop an improved optimization algorithm based on genetic algorithm (GA) approach for the bandwidth allocation of ATM networks. The ATM switches can be connected with multiples of DS3 trunks via digital cross connect systems (DCS). One of the advantages of DCS is its ability to reconfigure a customer network dynamically. We utilize this advantage in the design and dynamic reconfiguration of ATM networks. The problem is formulated as a network optimization problem where a congestion measure based on the average packet delay is minimized, subject to capacity constraints posed by the underlying facility trunks. We choose the traffic routing on the express pipes and the allocation of the bandwidth on these pipes as the variables in this problem. The previous GA algorithm is not practical because (1) the number of the traffic distribution patterns is huge, and (2) the values of offered traffic are continuous. A new representation of the chromosome, Net- Chro, and the reproduction operator are presented. We show that the previous algorithm cannot guarantee full usage of trunk capacities in the solutions it generates. We also discuss open-loop control to overcome the congestion caused by a trunk failure.

Park, Fransis Y.; Wong, Edward K.

1996-11-01

432

Optimal design of link systems using successive zooming genetic algorithm  

NASA Astrophysics Data System (ADS)

Link-systems have been around for a long time and are still used to control motion in diverse applications such as automobiles, robots and industrial machinery. This study presents a procedure involving the use of a genetic algorithm for the optimal design of single four-bar link systems and a double four-bar link system used in diesel engine. We adopted the Successive Zooming Genetic Algorithm (SZGA), which has one of the most rapid convergence rates among global search algorithms. The results are verified by experiment and the Recurdyn dynamic motion analysis package. During the optimal design of single four-bar link systems, we found in the case of identical input/output (IO) angles that the initial and final configurations show certain symmetry. For the double link system, we introduced weighting factors for the multi-objective functions, which minimize the difference between output angles, providing balanced engine performance, as well as the difference between final output angle and the desired magnitudes of final output angle. We adopted a graphical method to select a proper ratio between the weighting factors.

Kwon, Young-Doo; Sohn, Chang-hyun; Kwon, Soon-Bum; Lim, Jae-gyoo

2009-07-01

433

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

ERIC Educational Resources Information Center

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

Jones, Gareth; And Others

1994-01-01

434

Parameters optimization of Support Vector Machine based on Simulated Annealing and Genetic Algorithm  

Microsoft Academic Search

The generalization error of support vector machine usually depends on its kernel parameters, but there is no analytic method to choose kernel parameters for SVM. In order to choose the kernel parameters for SVM, the simulated annealing algorithm and genetic algorithm are combined, which is called simulated annealing genetic algorithm (SA-GA), to choose the SVM kernel parameters. SA-GA makes use

Qilong Zhang; Ganlin Shan; Xiusheng Duan; Zining Zhang

2009-01-01

435

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

436

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

437

A genetic algorithm based method for docking flexible molecules  

SciTech Connect

The authors describe a computational method for docking flexible molecules into protein binding sites. The method uses a genetic algorithm (GA) to search the combined conformation/orientation space of the molecule to find low energy conformation. Several techniques are described that increase the efficiency of the basic search method. These include the use of several interacting GA subpopulations or niches; the use of a growing algorithm that initially docks only a small part of the molecule; and the use of gradient minimization during the search. To illustrate the method, they dock Cbz-GlyP-Leu-Leu (ZGLL) into thermolysin. This system was chosen because a well refined crystal structure is available and because another docking method had previously been tested on this system. Their method is able to find conformations that lie physically close to and in some cases lower in energy than the crystal conformation in reasonable periods of time on readily available hardware.

Judson, R.S. [Sandia National Labs., Livermore, CA (United States); Jaeger, E.P.; Treasurywala, A.M. [Sterling-Winthrop Inc., Collegeville, PA (United States)

1993-11-01

438

A Genetic Algorithm for Solving the Generalized Vehicle Routing Problem  

NASA Astrophysics Data System (ADS)

The generalized vehicle routing problem is a variant of the well-known vehicle routing problem in which the nodes of a graph are partitioned into a given number of node sets (clusters) and the objective is to find the minimum-cost delivery or collection of routes, subject to capacity restrictions, from a given depot to the number of predefined clusters passing through one node from each clusters. We present an effective metaheuristic algorithm for the problem based on genetic algorithms. The proposed metaheuristic is competitive with other heuristics published to date in both solution quality and computation time. Computational results for benchmarks problems are reported and the results point out that GA is an appropriate method to explore the search space of this complex problem and leads to good solutions in a short amount of time.

Pop, P. C.; Matei, O.; Sitar, C. Pop; Chira, C.

439

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.

Kosmas, O T; Vlachos, D S; Simos, T E

2008-01-01

440

An Island Grouping Genetic Algorithm for Fuzzy Partitioning Problems  

PubMed Central

This paper presents a novel fuzzy clustering technique based on grouping genetic algorithms (GGAs), which are a class of evolutionary algorithms especially modified to tackle grouping problems. Our approach hinges on a GGA devised for fuzzy clustering by means of a novel encoding of individuals (containing elements and clusters sections), a new fitness function (a superior modification of the Davies Bouldin index), specially tailored crossover and mutation operators, and the use of a scheme based on a local search and a parallelization process, inspired from an island-based model of evolution. The overall performance of our approach has been assessed over a number of synthetic and real fuzzy clustering problems with different objective functions and distance measures, from which it is concluded that the proposed approach shows excellent performance in all cases. PMID:24977235

Salcedo-Sanz, S.; Del Ser, J.; Geem, Z. W.

2014-01-01

441

Application of genetic algorithms to tuning fuzzy control systems  

NASA Technical Reports Server (NTRS)

Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.

Espy, Todd; Vombrack, Endre; Aldridge, Jack

1993-01-01

442

Hyperspectral image reconstruction based on an improved genetic algorithm  

NASA Astrophysics Data System (ADS)

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

Wang, Lang; Guo, Shuxu; Ren, Ruizhi

2009-08-01

443

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

2009-05-04

444

Optimization of catalysts using specific, description-based genetic algorithms.  

PubMed

This paper deals with the key optimization task that has to be solved when improving the performance of many chemical processes--optimization of the catalysts used in the reaction via the optimization of its composition and preparation. A novel approach is presented that allows for the preservation of the advantages of genetic algorithms developed specifically for the optimization of catalytic materials but avoids the disadvantageous necessity to reimplement the algorithm when the scope of the optimized materials changes. Its main idea is to automatically generate problem-tailored implementations from requirements concerning the materials with a program generator. For the specification of such requirements, a formal description language, called catalyst description language, has been developed. PMID:18254615

Holena, Martin; Cukic, Tatjana; Rodemerck, Uwe; Linke, David

2008-02-01

445

Genetic Algorithm for Multiple Bus Line Coordination on Urban Arterial  

PubMed Central

Bus travel time on road section is defined and analyzed with the effect of multiple bus lines. An analytical model is formulated to calculate the total red time a bus encounters when travelling along the arterial. Genetic algorithm is used to optimize the offset scheme of traffic signals to minimize the total red time that all bus lines encounter in two directions of the arterial. The model and algorithm are applied to the major part of Zhongshan North Street in the city of Nanjing. The results show that the methods in this paper can reduce total red time of all the bus lines by 31.9% on the object arterial and thus improve the traffic efficiency of the whole arterial and promote public transport priority.

Yang, Zhen; Wang, Wei; Chen, Shuyan; Ding, Haoyang; Li, Xiaowei

2015-01-01

446

Reliability Optimization of Series-Parallel Systems Using a Genetic Algorithm David W. Coit, IEEE Student Member  

E-print Network

Reliability Optimization of Series-Parallel Systems Using a Genetic Algorithm David W. Coit, IEEE Optimization of Series-Parallel Systems Using a Genetic Algorithm Key Words - Genetic algorithm, Combinatorial genetic algorithm (GA) is developed and demonstrated to analyze series-parallel systems and to determine

Smith, Alice E.

447

Dynamic and fault tolerant three-dimensional cellular genetic algorithms  

E-print Network

In the area of artificial intelligence, the development of Evolutionary Algorithms (EAs) has been very active, especially in the last decade. These algorithms started to evolve when scientists from various regions of the ...

Al Naqi, Asmaa

2012-11-29

448

Empirical study of self-configuring genetic programming algorithm performance and behaviour  

NASA Astrophysics Data System (ADS)

The behaviour of the self-configuring genetic programming algorithm with a modified uniform crossover operator that implements a selective pressure on the recombination stage, is studied over symbolic programming problems. The operator's probabilistic rates interplay is studied and the role of operator variants on algorithm performance is investigated. Algorithm modifications based on the results of investigations are suggested. The performance improvement of the algorithm is demonstrated by the comparative analysis of suggested algorithms on the benchmark and real world problems.

Semenkin, E.; Semenkina, M.

2015-01-01

449

Design of Genetically Evolved Artificial Neural Network Using Enhanced Genetic Algorithm  

Microsoft Academic Search

This paper deals with designing an Artificial Neural Network (ANN) whose weights are genetically evolved using the proposed Enhanced Genetic Algorithm (EGA), thereby obtaining optimal weight set. The perform- ance is analysed by fitness function based ranking. The ability of learning may depend on many factors like the number of neurons in the hidden layer, number of training input patterns

M. NirmalaDevi; N. Mohankumar; M. Karthick; Nikhil Jayan; R. Nithya; S. Shobana; M. Shyam Sundar; S. Arumugam

2009-01-01

450

Vertical decomposition with Genetic Algorithm for Multiple Sequence Alignment  

PubMed Central

Background Many Bioinformatics studies begin with a multiple sequence alignment as the foundation for their research. This is because multiple sequence alignment can be a useful technique for studying molecular evolution and analyzing sequence structure relationships. Results In this paper, we have proposed a Vertical Decomposition with Genetic Algorithm (VDGA) for Multiple Sequence Alignment (MSA). In VDGA, we divide the sequences vertically into two or more subsequences, and then solve them individually using a guide tree approach. Finally, we combine all the subsequences to generate a new multiple sequence alignment. This technique is applied on the solutions of the initial generation and of each child generation within VDGA. We have used two mechanisms to generate an initial population in this research: the first mechanism is to generate guide trees with randomly selected sequences and the second is shuffling the sequences inside such trees. Two different genetic operators have been implemented with VDGA. To test the performance of our algorithm, we have compared it with existing well-known methods, namely PRRP, CLUSTALX, DIALIGN, HMMT, SB_PIMA, ML_PIMA, MULTALIGN, and PILEUP8, and also other methods, based on Genetic Algorithms (GA), such as SAGA, MSA-GA and RBT-GA, by solving a number of benchmark datasets from BAliBase 2.0. Conclusions The experimental results showed that the VDGA with three vertical divisions was the most successful variant for most of the test cases in comparison to other divisions considered with VDGA. The experimental results also confirmed that VDGA outperformed the other methods considered in this research. PMID:21867510

2011-01-01

451

Multidisciplinary Optimization of Airborne Radome Using Genetic Algorithm  

NASA Astrophysics Data System (ADS)

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

Tang, Xinggang; Zhang, Weihong; Zhu, Jihong

452

Properties of nucleon resonances by means of a genetic algorithm  

SciTech Connect

We present an optimization scheme that employs a genetic algorithm (GA) to determine the properties of low-lying nucleon excitations within a realistic photo-pion production model based upon an effective Lagrangian. We show that with this modern optimization technique it is possible to reliably assess the parameters of the resonances and the associated error bars as well as to identify weaknesses in the models. To illustrate the problems the optimization process may encounter, we provide results obtained for the nucleon resonances {delta}(1230) and {delta}(1700). The former can be easily isolated and thus has been studied in depth, while the latter is not as well known experimentally.

Fernandez-Ramirez, C. [Center for Theoretical Physics, Laboratory for Nuclear Science and Department of Physics, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139 (United States); Moya de Guerra, E. [Grupo de Fisica Nuclear, Departamento de Fisica Atomica, Molecular y Nuclear, Facultad de Ciencias Fisicas, Universidad Complutense de Madrid, Avda. Complutense s/n, E-28040 Madrid (Spain); Instituto de Estructura de la Materia, CSIC, Serrano 123, E-28006 Madrid (Spain); Udias, A. [Departamento de Estadistica e Investigacion Operativa, Escuela Superior de Ciencias Experimentales y Tecnologia, Universidad Rey Juan Carlos, Camino del Molino s/n, E-28943 Fuenlabrada (Spain); Udias, J. M. [Grupo de Fisica Nuclear, Departamento de Fisica Atomica, Molecular y Nuclear, Facultad de Ciencias Fisicas, Universidad Complutense de Madrid, Avda. Complutense s/n, E-28040 Madrid (Spain)

2008-06-15

453

Simulating and Synthesizing Substructures Using Neural Network and Genetic Algorithms  

NASA Technical Reports Server (NTRS)

The feasibility of simulating and synthesizing substructures by computational neural network models is illustrated by investigating a statically indeterminate beam, using both a 1-D and a 2-D plane stress modelling. The beam can be decomposed into two cantilevers with free-end loads. By training neural networks to simulate the cantilever responses to different loads, the original beam problem can be solved as a match-up between two subsystems under compatible interface conditions. The genetic algorithms are successfully used to solve the match-up problem. Simulated results are found in good agreement with the analytical or FEM solutions.

Liu, Youhua; Kapania, Rakesh K.; VanLandingham, Hugh F.

1997-01-01

454

Application of Genetic Algorithms in Nonlinear Heat Conduction Problems  

PubMed Central

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

Khan, Waqar A.

2014-01-01

455

A new chromatic dispersion compensation method based on genetic algorithm  

NASA Astrophysics Data System (ADS)

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

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

2013-08-01

456

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

457

Selecting image retrieval parameters with a genetic algorithm  

NASA Astrophysics Data System (ADS)

Image retrieval (IR) means taking a probe image and finding the most appropriate match in a (possibly very large) image database. Unlike keyword-indexing, our approach is to compute a feature vector (FV) for each image, and to compute the distance from the probe to each image in the database. As a starting point, we studied the system of Jacobs et al., developed at the University of Washington, which used the Haar wavelet transform to produce feature vectors from images. A genetic algorithm developed weighting parameters which yielded significantly improved image retrieval performance.

Soroka, Barry I.; Kerrick, Steven P.

2003-01-01

458

Optimal brushless DC motor design using genetic algorithms  

NASA Astrophysics Data System (ADS)

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

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

2010-11-01

459

Properties of Nucleon Resonances by means of a Genetic Algorithm  

E-print Network

We present an optimization scheme that employs a Genetic Algorithm (GA) to determine the properties of low-lying nucleon excitations within a realistic photo-pion production model based upon an effective Lagrangian. We show that with this modern optimization technique it is possible to reliably assess the parameters of the resonances and the associated error bars as well as to identify weaknesses in the models. To illustrate the problems the optimization process may encounter, we provide results obtained for the nucleon resonances $\\Delta$(1230) and $\\Delta$(1700). The former can be easily isolated and thus has been studied in depth, while the latter is not as well known experimentally.

C. Fernandez-Ramirez; E. Moya de Guerra; A. Udias; J. M. Udias

2008-06-24

460

Full design of fuzzy controllers using genetic algorithms  

NASA Technical Reports Server (NTRS)

This paper examines the applicability of genetic algorithms (GA) in the complete design of fuzzy logic controllers. While GA has been used before in the development of rule sets or high performance membership functions, the interdependence between these two components dictates that they should be designed together simultaneously. GA is fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. We show the application of this new method to the development of a cart controller.

Homaifar, Abdollah; Mccormick, ED

1992-01-01

461

Experience with a Genetic Algorithm Implemented on a Multiprocessor Computer  

NASA Technical Reports Server (NTRS)

Numerical experiments were conducted to find out the extent to which a Genetic Algorithm (GA) may benefit from a multiprocessor implementation, considering, on one hand, that analyses of individual designs in a population are independent of each other so that they may be executed concurrently on separate processors, and, on the other hand, that there are some operations in a GA that cannot be so distributed. The algorithm experimented with was based on a gaussian distribution rather than bit exchange in the GA reproductive mechanism, and the test case was a hub frame structure of up to 1080 design variables. The experimentation engaging up to 128 processors confirmed expectations of radical elapsed time reductions comparing to a conventional single processor implementation. It also demonstrated that the time spent in the non-distributable parts of the algorithm and the attendant cross-processor communication may have a very detrimental effect on the efficient utilization of the multiprocessor machine and on the number of processors that can be used effectively in a concurrent manner. Three techniques were devised and tested to mitigate that effect, resulting in efficiency increasing to exceed 99 percent.

Plassman, Gerald E.; Sobieszczanski-Sobieski, Jaroslaw

2000-01-01

462

Prostate segmentation on pelvic CT images using a genetic algorithm  

NASA Astrophysics Data System (ADS)

A genetic algorithm (GA) for automating the segmentation of the prostate on pelvic computed tomography (CT) images is presented here. The images consist of slices from three-dimensional CT scans. Segmentation is typically performed manually on these images for treatment planning by an expert physician, who uses the "learned" knowledge of organ shapes, textures and locations to draw a contour around the prostate. Using a GA brings the flexibility to incorporate new "learned" information into the segmentation process without modifying the fitness function that is used to train the GA. Currently the GA uses prior knowledge in the form of texture and shape of the prostate for segmentation. We compare and contrast our algorithm with a level-set based segmentation algorithm, thereby providing justification for using a GA. Each individual of the GA population represents a segmenting contour. Shape variability of the prostate derived from manually segmented images is used to form a shape representation from which an individual of the GA population is randomly generated. The fitness of each individual is evaluated based on the texture of the region it encloses. The segmenting contour that encloses the prostate region is considered more fit than others and is more likely to be selected to produce an offspring over successive generations of the GA run. This process of selection, crossover and mutation is iterated until the desired region is segmented. Results of 2D and 3D segmentation are presented and future work is also discussed here.

Ghosh, Payel; Mitchell, Melanie

2008-03-01

463

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. PMID:25405225

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

2014-01-01

464

Parallel genetic algorithms for large-scale fixed charge networks  

SciTech Connect

We present parallel genetic algorithms (GA`s) for several classes of fixed-charge multicommodity flow problems arising from applications in parallel database design, domain decomposition, and telecommunications. These algorithms utilize a high-level approach based upon representing individual (in the GA sense) in terms of selections from a library of pre-computed {open_quotes}building blocks{close_quotes} of sets of variables rather than as values of individual binary variables corresponding to single links. The fitness function for this form of representation is then evaluated by applying heuristics to the starting point represented by an individual, thereby allowing for modifications in the original {open_quotes}blueprint{close_quotes} represented by the individual. These heuristics lead to objective function improvements and are also used to force feasibility. With this type of fitness function, the amount of time spent on the other operations of the GA (selection, mutation, etc.) is relatively small, so that high efficiency may be achieved in parallel implementations of the algorithm. We present computational results on the CM-5 supercomputer, demonstrating the ability to solve to optimality certain fixed-charge problems with more than one million binary variables.

Meyer, R.R.

1994-12-31

465

F?NANSTA EVR?MSEL ALGOR?TM?K YAKLA?IMLAR: GENET?K ALGOR?TMA UYGULAMALARI EVOLUTIONARY ALGORITHMIC APPROACHES IN FINANCE: APPLICATIONS OF GENETIC ALGORITHMS  

Microsoft Academic Search

The objective of this study is to review genetic algorithms, which are evolution and natural genetic process based stochastic search and optimization techniques, and their financial applications. First, the concepts of evolutionary algorithms and genetic algortihms are explained. Then, literature on the applications of genetic algorithms on finance is reviewed. Financial applications of genetic algoritms are analyzed under two main

Hakan ER; M. Koray

466

Nonlinear predictive control of a drying process using genetic algorithms.  

PubMed

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

Yuzgec, Ugur; Becerikli, Yasar; Turker, Mustafa

2006-10-01

467

Applying niching genetic algorithms for multiple cluster discovery in spatial analysis  

Microsoft Academic Search

Traditional genetic algorithms with elitist selection are unable to locate more than one solution in a multimodal fitness landscape in a single run. This genetic drift is illustrated in Mapex, a smart spatial analysis technique, employing a genetic algorithm for spatial cluster discovery. However, for detecting multiple clusters Mapex provides a non-ideal approach. In this paper, we use a fitness

Ritvik Sahajpal; G. V. Ramaraju; V. Bhatt

2004-01-01

468

A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem  

Microsoft Academic Search

The capacitated multipoint network design problem (CMNDP) is NP-complete. In this paper, a hybrid genetic algo- rithm for CMNDP is proposed. The multiobjective hybrid genetic algorithm (MOHGA) differs from other genetic algorithms (GA's) mainly in its selection procedure. The concept of subpopulation is used in MOHGA. Four subpopulations are generated according to the elitism reservation strategy, the shifting Prfer vector,

Chi-chun Lo; Wei-hsin Chang

2000-01-01

469

A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows  

Microsoft Academic Search

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

Jean Berger; Martin Salois; Regent Begin

1998-01-01

470

Realization of microcontroller-based polarization control system with genetic algorithm  

Microsoft Academic Search

Realization of a PIC32 microcontroller-based polarization control system is described. Genetic algorithm is used for control purposes. The controller measures the signal intensity to estimate the genetic value. To reach optimum performance, the code is optimized by using the best genetic parameter to achieve the fastest execution time. This algorithm consumes low size of memory besides providing fast speed. The

Ghazaleh Mamdoohi; Aida Esmailian; Ahmad Fauzi Abas; Khairulmizam Samsudin; Ariya Hidayat; Noor Hisham Ibrahim; Mohd Adzir Mahdi

2009-01-01

471

Predictive Models for the Breeder Genetic Algorithm, I: Continuous Parameter Optimization  

Microsoft Academic Search

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

Heinz Mhlenbein; Dirk Schlierkamp-voosen

1993-01-01

472

Scheduling algorithms  

Microsoft Academic Search

This paper discusses automated scheduling as it applies to complex domains such as factories, transportation, and communications systems. The window-constrained-packing problem is introduced as an ideal model of the scheduling trade offs. Specific algorithms are compared in terms of simplicity, speed, and accuracy. In particular, dispatch, look-ahead, and genetic algorithms are statistically compared on randomly generated job sets. The conclusion

William J. Wolfe; David Wood; Steve Sorensen

1996-01-01

473

Genetic algorithm for unsupervised classification of remote sensing imagery  

NASA Astrophysics Data System (ADS)

Traditionally, an unsupervised classification divides all pixels within an image into a corresponding class pixel by pixel. The number of classes must be selected, but seldom is ascertainable with little information in advance. Moreover, spectral properties of specific informational classes change seasonally for satellite imagery. The relationships between informational classes and spectral classes are not always constant, and relationships defined for one image cannot be extended to others. Thus, the analyst has very limited or no control over the menu of classes and their specific identities. In this study, a Genetic Algorithm is adopted to interpret the cluster centers of an image and to reveal a suitable number of classes to overcome the disadvantage of unsupervised classification. A Genetic Algorithm is capable of dealing with a set of numerous data such as satellite imagery pixels. An optimization consequence of the image classification is introduced and carried out. Through an image process program developed in Mathlab, the GA unsupervised classifier was processed on several test images for validity and on SPOT satellite imagery. The classified SPOT image was compared with finer aerial photographs as a ground truth for the estimation of classification accuracy.

Yang, Ming-Der; Yang, Yeh-Fen

2004-05-01

474

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

475

Actuator Placement Via Genetic Algorithm for Aircraft Morphing  

NASA Technical Reports Server (NTRS)

This research continued work that began under the support of NASA Grant NAG1-2119. The focus of this effort was to continue investigations of Genetic Algorithm (GA) approaches that could be used to solve an actuator placement problem by treating this as a discrete optimization problem. In these efforts, the actuators are assumed to be "smart" devices that change the aerodynamic shape of an aircraft wing to alter the flow past the wing, and, as a result, provide aerodynamic moments that could provide flight control. The earlier work investigated issued for the problem statement, developed the appropriate actuator modeling, recognized the importance of symmetry for this problem, modified the aerodynamic analysis routine for more efficient use with the genetic algorithm, and began a problem size study to measure the impact of increasing problem complexity. The research discussed in this final summary further investigated the problem statement to provide a "combined moment" problem statement to simultaneously address roll, pitch and yaw. Investigations of problem size using this new problem statement provided insight into performance of the GA as the number of possible actuator locations increased. Where previous investigations utilized a simple wing model to develop the GA approach for actuator placement, this research culminated with application of the GA approach to a high-altitude unmanned aerial vehicle concept to demonstrate that the approach is valid for an aircraft configuration.

Crossley, William A.; Cook, Andrea M.

2001-01-01

476

Road Detection by Neural and Genetic Algorithm in Urban Environment  

NASA Astrophysics Data System (ADS)

In the urban object detection challenge organized by the ISPRS WG III/4 high geometric and radiometric resolution aerial images about Vaihingen/Stuttgart, Germany are distributed. The acquired data set contains optical false color, near infrared images and airborne laserscanning data. The presented research focused exclusively on the optical image, so the elevation information was ignored. The road detection procedure has been built up of two main phases: a segmentation done by neural networks and a compilation made by genetic algorithms. The applied neural networks were support vector machines with radial basis kernel function and self-organizing maps with hexagonal network topology and Euclidean distance function for neighborhood management. The neural techniques have been compared by hyperbox classifier, known from the statistical image classification practice. The compilation of the segmentation is realized by a novel application of the common genetic algorithm and by differential evolution technique. The genes were implemented to detect the road elements by evaluating a special binary fitness function. The results have proven that the evolutional technique can automatically find major road segments.

Barsi, A.

2012-07-01

477

Feature Subset Selection, Class Separability, and Genetic Algorithms  

SciTech Connect

The performance of classification algorithms in machine learning is affected by the features used to describe the labeled examples presented to the inducers. Therefore, the problem of feature subset selection has received considerable attention. Genetic approaches to this problem usually follow the wrapper approach: treat the inducer as a black box that is used to evaluate candidate feature subsets. The evaluations might take a considerable time and the traditional approach might be unpractical for large data sets. This paper describes a hybrid of a simple genetic algorithm and a method based on class separability applied to the selection of feature subsets for classification problems. The proposed hybrid was compared against each of its components and two other feature selection wrappers that are used widely. The objective of this paper is to determine if the proposed hybrid presents advantages over the other methods in terms of accuracy or speed in this problem. The experiments used a Naive Bayes classifier and public-domain and artificial data sets. The experiments suggest that the hybrid usually finds compact feature subsets that give the most accurate results, while beating the execution time of the other wrappers.

Cantu-Paz, E

2004-01-21

478

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

479

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

480

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 algorithmdifferential evolution. PMID:24987749

Sve?ko, Rajko

2014-01-01

481

Solving NP-Complete Problems in Real-Time System Design by Multichromosome Genetic Algorithms  

Microsoft Academic Search

Most problems in the design of real-time applications like task allocation or schedulingbelong to the class of NP-complete problems and can be solved efficiently only by heuristics.Genetic Algorithms are a relatively new method to attack these problems. ConventionalGenetic Algorithms, however, have a number of drawbacks that reduce their applicability todesign problems of real-time systems.The Genetic Algorithm presented in this paper

Roman Nossal Thomas M. Galla

1997-01-01

482

Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation  

Microsoft Academic Search

Quantum computing is applied to genetic algorithm (GA) to develop a class of quantum-inspired genetic algorithm (QGA) characterized by certain principles of quantum mechanisms for numerical optimization. Furthermore, a framework of hybrid QGA, named RQGA, is proposed by reasonably combining the Q-bit search of quantum algorithm in micro-space and classic genetic search of real-coded GA (RGA) in macro-space to achieve

Ling Wang; Fang Tang; Hao Wu

2005-01-01

483

Balancing Exploration and Exploitation in an Adaptive Three-Dimensional Cellular Genetic Algorithm via a Probabilistic Selection Operator  

E-print Network

Balancing Exploration and Exploitation in an Adaptive Three-Dimensional Cellular Genetic Algorithm presents a new adaptive gradual algorithm which is based on three-dimensional cellular genetic algorithms on diversity measure, the proposed algorithm gradually tunes the selection pressure by modifying the genetic

Arslan, Tughrul

484

An Indirect Genetic Algorithm for Set Covering Problems Journal of the Operational Research Society, 53 (10): 1118-1126, 2002.  

E-print Network

1 An Indirect Genetic Algorithm for Set Covering Problems Journal of the Operational Research 1BB UK, uxa@cs.nott.ac.uk Abstract This paper presents a new type of genetic algorithm for the set algorithm, i.e. the actual solutions are found by an external decoder function. The genetic algorithm itself

Aickelin, Uwe

485

Quantized phase optimization of two-dimensional Fourier kinoforms by a genetic algorithm  

NASA Astrophysics Data System (ADS)

We have developed a phase optimization method of a quantized kinoform by a genetic algorithm. Because the genetic algorithm inherently deals with discrete values, the quantized phase of the kinoform can be easily estimated. The two-dimensional Fourier kinoform can utilize effectively the periodicity of the discrete Fourier transform in the genetic algorithm. This condition enables us to perform the crossover process that is one of the processes in genetic algorithm without a spatial bandwidth of the kinoform. The optimization has been performed successfully in computer simulation. The optically reconstructed image agrees well with the theoretical one.

Yoshikawa, N.; Itoh, M.; Yatagai, T.

1995-04-01

486

Combining neural networks and genetic algorithms for hydrological flow forecasting  

NASA Astrophysics Data System (ADS)

We present a neural network approach to rainfall-runoff modeling for small size river basins based on several time series of hourly measured data. Different neural networks are considered for short time runoff predictions (from one to six hours lead time) based on runoff and rainfall data observed in previous time steps. Correlation analysis shows that runoff data, short time rainfall history, and aggregated API values are the most significant data for the prediction. Neural models of multilayer perceptron and radial basis function networks with different numbers of units are used and compared with more traditional linear time series predictors. Out of possible 48 hours of relevant history of all the input variables, the most important ones are selected by means of input filters created by a genetic algorithm. The genetic algorithm works with population of binary encoded vectors defining input selection patterns. Standard genetic operators of two-point crossover, random bit-flipping mutation, and tournament selection were used. The evaluation of objective function of each individual consists of several rounds of building and testing a particular neural network model. The whole procedure is rather computational exacting (taking hours to days on a desktop PC), thus a high-performance mainframe computer has been used for our experiments. Results based on two years worth data from the Ploucnice river in Northern Bohemia suggest that main problems connected with this approach to modeling are ovetraining that can lead to poor generalization, and relatively small number of extreme events which makes it difficult for a model to predict the amplitude of the event. Thus, experiments with both absolute and relative runoff predictions were carried out. In general it can be concluded that the neural models show about 5 per cent improvement in terms of efficiency coefficient over liner models. Multilayer perceptrons with one hidden layer trained by back propagation algorithm and predicting relative runoff show the best behavior so far. Utilizing the genetically evolved input filter improves the performance of yet another 5 per cent. In the future we would like to continue with experiments in on-line prediction using real-time data from Smeda River with 6 hours lead time forecast. Following the operational reality we will focus on classification of the runoffs into flood alert levels, and reformulation of the time series prediction task as a classification problem. The main goal of all this work is to improve flood warning system operated by the Czech Hydrometeorological Institute.

Neruda, Roman; Srejber, Jan; Neruda, Martin; Pascenko, Petr

2010-05-01

487

Thermodynamic Pareto optimization of turbojet engines using multi-objective genetic algorithms  

Microsoft Academic Search

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

K. Atashkari; N. Nariman-Zadeh; A. Pilechi; A. Jamali; X. Yao

2005-01-01

488

A hybrid genetic algorithm and bacterial foraging approach for global optimization  

Microsoft Academic Search

The social foraging behavior of Escherichia coli bacteria has been used to solve optimization problems. This paper pro- poses a hybrid approach involving genetic algorithms (GA) and bacterial foraging (BF) algorithms for function optimiza- tion problems. We first illustrate the proposed method using four test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover,

Dong Hwa Kim; Ajith Abraham; Jae Hoon Cho

2007-01-01

489

Optimization of reinforced concrete frame bridges by parallel genetic and memetic algorithms  

Microsoft Academic Search

This paper deals with the automated design and economic optimization of reinforced concrete frame bridges typically used in road construction. The optimization algorithms applied are (i) parallel genetic algorithms (PGA) and (ii) parallel memetic algorithms (PMA). The evaluation of solutions follows the Spanish Code for structural concrete. Stress resultants and envelopes of framed structures are computed by an internal matrix

C. Perea; M. Baitsch; D. Hartmann

490

Thermodynamic Pareto optimization of turbojet engines using multi-objective genetic algorithms  

Microsoft Academic Search

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

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

491

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

492

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

493

1-m lightweight mirror design using genetic algorithm  

NASA Astrophysics Data System (ADS)

We present our design procedure for a 1-m lightweight mirror in a space optical system. The glass mirror has three monolithic bosses at the rim and is assembled with metallic bipod flexures. Their dimensional parameters cannot be optimized independently with each other in a classical design process, where optical performance is greatly affected by the flexure mount configuration. With our method, the design problem is separated into two independent problems; mirror design and flexure design. Resources required to achieve design goals are reduced by almost one order of magnitude in time. Also the mirror and flexure mount designs can be parallel-processed without interfering each other. In this paper, we present the mirror design process and its results optimized with multi-objective genetic algorithm (GA).

Kihm, Hagyong; Moon, Il Kweon; Yang, Ho-Soon; Lee, Yun-Woo

494

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

495

Genetic algorithms and solid state NMR pulse sequences  

E-print Network

The use of genetic algorithms for the optimisation of magic angle spinning NMR pulse sequences is discussed. The discussion uses as an example the optimisation of the C7 dipolar recoupling pulse sequence, aiming to achieve improved efficiency for spin systems characterised by large chemical shielding anisotropies and/or small dipolar coupling interactions. The optimised pulse sequence is found to be robust over a wide range of parameters, requires only minimal a priori knowledge of the spin system for experimental implementations with buildup rates being solely determined by the magnitude of the dipolar coupling interaction, but is found to be less broadbanded than the original C7 pulse sequence. The optimised pulse sequence breaks the synchronicity between r.f. pulses and sample spinning.

Bechmann, Matthias; Sebald, Angelika

2013-01-01

496

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

497

Hierarchical Stochastic Simulation Algorithm for SBML Models of Genetic Circuits  

PubMed Central

This paper describes a hierarchical stochastic simulation algorithm, which has been implemented within iBioSim, a tool used to model, analyze, and visualize genetic circuits. Many biological analysis tools flatten out hierarchy before simulation, but there are many disadvantages associated with this approach. First, the memory required to represent the model can quickly expand in the process. Second, the flattening process is computationally expensive. Finally, when modeling a dynamic cellular population within iBioSim, inlining the hierarchy of the model is inefficient since models must grow dynamically over time. This paper discusses a new approach to handle hierarchy on the fly to make the tool faster and more memory-efficient. This approach yields significant performance improvements as compared to the former flat analysis method. PMID:25506588

Watanabe, Leandro H.; Myers, Chris J.

2014-01-01

498

A Comparative Study between Genetic Algorithm and Genetic Programming Based Gait Generation Methods for Quadruped Robots  

Microsoft Academic Search

\\u000a Planning gaits for legged robots is a challenging task that requires optimizing parameters in a highly irregular and multidimensional\\u000a space. Two gait generation methods using GA (Genetic Algorithm), GP (genetic programming) are compared to develop fast locomotion\\u000a for a quadruped robot. GA-based approaches seek to optimize a pre-selected set of parameters which include locus of paw and\\u000a stance parameters of

Kisung Seo; Soohwan Hyun

2010-01-01

499

Cloud identification using genetic algorithms and massively parallel computation  

NASA Technical Reports Server (NTRS)

As a Guest Computational Investigator under the NASA administered component of the High Performance Computing and Communication Program, we implemented a massively parallel genetic algorithm on the MasPar SIMD computer. Experiments were conducted using Earth Science data in the domains of meteorology and oceanography. Results obtained in these domains are competitive with, and in most cases better than, similar problems solved using other methods. In the meteorological domain, we chose to identify clouds using AVHRR spectral data. Four cloud speciations were used although most researchers settle for three. Results were remarkedly consistent across all tests (91% accuracy). Refinements of this method may lead to more timely and complete information for Global Circulation Models (GCMS) that are prevalent in weather forecasting and global environment studies. In the oceanographic domain, we chose to identify ocean currents from a spectrometer having similar characteristics to AVHRR. Here the results were mixed (60% to 80% accuracy). Given that one is willing to run the experiment several times (say 10), then it is acceptable to claim the higher accuracy rating. This problem has never been successfully automated. Therefore, these results are encouraging even though less impressive than the cloud experiment. Successful conclusion of an automated ocean current detection system would impact coastal fishing, naval tactics, and the study of micro-climates. Finally we contributed to the basic knowledge of GA (genetic algorithm) behavior in parallel environments. We developed better knowledge of the use of subpopulations in the context of shared breeding pools and the migration of individuals. Rigorous experiments were conducted based on quantifiable performance criteria. While much of the work confirmed current wisdom, for the first time we were able to submit conclusive evidence. The software developed under this grant was placed in the public domain. An extensive user's manual was written and distributed nationwide to scientists whose work might benefit from its availability. Several papers, including two journal articles, were produced.

Buckles, Bill P.; Petry, Frederick E.

1996-01-01

500

A new perspective on dark energy modeling via genetic algorithms  

SciTech Connect

We use Genetic Algorithms to extract information from several cosmological probes, such as the type Ia supernovae (SnIa), the Baryon Acoustic Oscillations (BAO) and the growth rate of matter perturbations. This is done by implementing a model independent and bias-free reconstruction of the various scales and distances that characterize the data, like the luminosity d{sub L}(z) and the angular diameter distance d{sub A}(z) in the SnIa and BAO data, respectively, or the dependence with redshift of the matter density ?{sub m}(a) in the growth rate data, f?{sub 8}(z). These quantities can then be used to reconstruct the expansion history of the Universe, and the resulting Dark Energy (DE) equation of state w(z) in the context of FRW models, or the mass radial function ?{sub M}(r) in LTB models. In this way, the reconstruction is completely independent of our prior bias. Furthermore, we use this method to test the Etherington relation, ie the well-known relation between the luminosity and the angular diameter distance, ??d{sub L}(z)/(1+z){sup 2}d{sub A}(z), which is equal to 1 in metric theories of gravity. We find that the present data seem to suggest a 3-? deviation from one at redshifts z ? 0.5. Finally, we present a novel way, within the Genetic Algorithm paradigm, to analytically estimate the errors on the reconstructed quantities by calculating a Path Integral over all possible functions that may contribute to the likelihood. We show that this can be done regardless of the data being correlated or uncorrelated with each other and we also explicitly demonstrate that our approach is in good agreement with other error estimation techniques like the Fisher Matrix approach and the Bootstrap Monte Carlo.

Nesseris, Savvas; Garca-Bellido, Juan, E-mail: savvas.nesseris@uam.es, E-mail: juan.garciabellido@uam.es [Instituto de Fsica Terica UAM-CSIC, Universidad Autonma de Madrid, Cantoblanco, 28049 Madrid (Spain)

2012-11-01