On the Mean Convergence Time of Multi-parent Genetic Algorithms Without Selection | Zendy

Chuan-Kang Ting | Zendy

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On the Mean Convergence Time of Multi-parent Genetic Algorithms Without Selection

Author(s) -

Chuan-Kang Ting

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-28848-1

DOI - 10.1007/11553090_41

Subject(s) - computer science , pairwise comparison , convergence (economics) , markov chain , genetic algorithm , algorithm , selection (genetic algorithm) , acceleration , genetic drift , mathematical optimization , mathematics , artificial intelligence , machine learning , genetic variation , population , physics , classical mechanics , demography , sociology , economics , economic growth

This paper investigates genetic drift in multi-parent genetic algorithms (MPGAs). An exact model based on Markov chains is proposed to formulate the variation of gene frequency. This model identifies the correlation between the adopted number of parents and the mean convergence time. Moreover, it reveals the pairwise equivalence phenomenon in the number of parents and indicates the acceleration of genetic drift in MPGAs. The good fit between theoretical and experimental results further verifies the capability of this model.

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