On the mean convergence time of multi-parent genetic algorithms without selection

Authors:
Chuan-Kang Ting
Affiliations:
International Graduate School of Dynamic Intelligent Systems, University of Paderborn, Paderborn, Germany
Venue:
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Year:
2005

Citing 7
Cited 1

Finite Markov chain analysis of genetic algorithms

Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Multi-parent scanning crossover and genetic drift

Theoretical aspects of evolutionary computing
Finite Markov Chain Analysis of Genetic Algorithms with Niching

Proceedings of the 5th International Conference on Genetic Algorithms
Genetic algorithms with multi-parent recombination

PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
On the Mean Convergence Time of Evolutionary Algorithms without Selection and Mutation

PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Multiparent recombination in evolutionary computing

Advances in evolutionary computing
A markov chain framework for the simple genetic algorithm

Evolutionary Computation

A genetic algorithm with tabu search procedure for flexible job shop scheduling with transportation constraints and bounded processing times

Computers and Operations Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.