Fourier Analysis of Genetic Algorithms

Kosters, Walter A.; Kok, Joost N.; Floréen, Patrik

doi:10.1016/s0304-3975(99)00092-4

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Genetic Algorithms are direct, parallel, stochastic method for global search and optimization, which imitates the evolution of the living beings, described by Charles Darwin. GA is part of the group of Evolutionary Algorithms (EA) [15].…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort

Johri¹,

Nasar²,

Chanda³

2013

IJCA

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Allocation of limited testing efforts to a software development project is a complex task for software managers. The challenges become difficult when the nature of the development process is considered in the dynamic environment. Numerous software reliability growth models have been proposed in last one decade to minimize the whole testing effort expenditures, but generally under static assumption. The main purpose of this article is to distribute total testing resource optimally under dynamic condition. An elaborate optimization policy is proposed using genetic algorithm and numerical example is also demonstrated. Genetic Algorithms (GAs) works with a set of individuals, representing probable solutions of the task. The selection theory is applied by using a criterion, giving an evaluation for the individual with respect to the desired solution. This article also studies the optimal resource allocation problems for different conditions by investigative the activities of the model parameters. General Terms Evolutionary Algorithm KeywordsGenetic Algorithm, testing effort allocation, Software reliability, SRGM Nomenclature a is the initial fault content in the software. b is the fault detection rate.is the number of fault removed at time 't'. m(t) is the cumulative number of fault detected till time 't' due to the testing effort w1(t).T the planning period.C1(m(t),w2(t)) Cost per unit at time 't' for cumulative faults removed m(t) and debugging effort w2(t).C 2 is the cost of testing per unit testing efforts.W is the total resources utilized during the SDLC at any point of time 't'.W 1 (t) current effort expenditure due to testing at time 't' W 2 (t) current effort expenditure due to debugging the fault at time 't'

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Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort

Johri¹,

Nasar²,

Chanda³

2013

IJCA

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“…The termination criterion is fulfilled if the mean value of in the -members population is no longer improved (maximized or minimized). More detailed overviews of GAs can be found in [16]- [19].…”

Section: Gas In -D Polynomial Factorizationmentioning

confidence: 99%

A genetic algorithm approach to the problem of factorization of general multidimensional polynomials

Gonos

Mastorakis²,

Swamy³

2003

IEEE Trans. Circuits Syst. I

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In this paper, a solution to the problem of the multidimensional ( -D) polynomial factorization is attempted by using genetic algorithms (GAs). The proposed method is based on an appropriate minimization of the norm of the difference between the original polynomial and its desirable factorized form. Using GAs, we can obtain better results than with other methods of minimization (numerical techniques, neural networks, etc.). The present methodology, which can also be used for every type of -D factorization, is illustrated by means of a numerical example.Index Terms-Genetic algorithm (GA), multidimensional ( -D) polynomial factorization, multivariate polynomials.

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“…(2005)], and the convergence rate of GAs has been predicted by the second largest eigenvalue of the transition matrix ; Jiang, H. and Chang, C. K. and Zhu, D. and Cheng, S. (2007)]. Other methods, such as the analysis on Walsh transformation [Bethke, A. D. (1980); Forrest, S. and Mitchell, M. (1993)] or Fourier transformation of fitness functions of GAs [Kosters, W. A. and Kok, J. N. and Leiden, P. F. (1999)], have also been adopted by some researchers. Although many papers have proposed various approaches to analyzing the behaviors of GAs, most existing GA theories lack concern for practicality [ Schoenauer, M. et al (2007); Jiang, H. and Chang, C. K. (2008); Jiang, H. and Chang, C. K. and Zhu, D. and Cheng, S. (2007)].…”

Section: Problem Statement and Motivation Of This Workmentioning

confidence: 99%

A framework for estimating the applicability of GAs for real-world optimization problems

Jiang

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Fourier Analysis of Genetic Algorithms

Cited by 6 publications

References 17 publications

A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort

A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort

A genetic algorithm approach to the problem of factorization of general multidimensional polynomials

A framework for estimating the applicability of GAs for real-world optimization problems

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