Fine-grained parallel genetic algorithm:a global convergence criterion

This paper presents a fine-grained parallel genetic algorithm with mutation rate as a control parameter. The function of the mutation rate is similar to the temperature parameter in the simulated annealing [3,8,10]. The motivation behind this research is to develop a global convergence theory for th...

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Veröffentlicht in:International journal of computer mathematics Jg. 73; H. 2; S. 139 - 155
Hauptverfasser: Muhammad, A., Bargiela, A., King, G.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Gordon and Breach Science Publishers 01.01.1999
Taylor and Francis
Schlagworte:
Biological and medical sciences
C1.2
Calculus of variations and optimal control
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
G.3
G1.6
General aspects
global convergence
I2.8
I6.4
Markov chain
Markov processes
Mathematical analysis
Mathematics
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
parallel model
Probability and statistics
Probability theory and stochastic processes
Sciences and techniques of general use
Stochastic optimisation
Markov chain
Transition rule
Genetic algorithm
Communicating sequential process
Discrete time
Parallel computation
Walsh polynomial
Stochastic method
Optimization
Convergence
Stationary distribution
ISSN:0020-7160, 1029-0265
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Beschreibung
Zusammenfassung:This paper presents a fine-grained parallel genetic algorithm with mutation rate as a control parameter. The function of the mutation rate is similar to the temperature parameter in the simulated annealing [3,8,10]. The motivation behind this research is to develop a global convergence theory for the fine-grained parallel genetic algorithms based on the simulated annealing model There is a mathematical difficulty associated with the genetic algorithms as they do not strictly come under die definition of an algorithm. Algorithms normally have a starting point and a defined point of termination which genetic algorithms lack. The parallel genetic algorithm presented here is a stochastic process based on Markov chain [2] model It has been proven that fine-grained parallel genetic algorithm is an ergodic Markov chain and that it converges to the stationary distribution. The theoretical result has been applied to in the context of optimisation of a deceptive function of 4-th order.
ISSN:0020-7160
1029-0265
DOI:10.1080/00207169908804885