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A new approach to dynamics analysis of genetic algorithms without selection

Okabe, T, Jin, Y and Sendhoff, B (2005) A new approach to dynamics analysis of genetic algorithms without selection

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Abstract

Theoretical analysis of the dynamics of evolutionary algorithms is believed to be very important to understand the search behavior of evolutionary algorithms and to develop more efficient algorithms. We investigate the dynamics of a canonical genetic algorithm with one-point crossover and mutation theoretically. To this end, a new theoretical framework has been suggested in which the probability of each chromosome in the offspring population can be calculated from the probability distribution of the parent population after crossover and mutation. Empirical studies are conducted to verify the theoretical analysis. The finite population effect is also discussed. Compared to existing approaches to dynamics analysis, our theoretical framework is able to provide richer information on population dynamics and is computationally more efficient. © 2005 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions: Faculty of Engineering and Physical Sciences > Computing Science
Depositing User: Symplectic Elements
Date Deposited: 12 Jul 2012 13:07
Last Modified: 23 Sep 2013 19:27
URI: http://epubs.surrey.ac.uk/id/eprint/532841

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