Analysis of Complete Convergence for Genetic Algorithm with Immune Memory

A new Immune Memory Genetic Algorithm (IMGA) based on the mechanism of immune memory and immune network is proposed in this article . Using Markov chains theory, we proven that NGA(Niche Genetic Algorithms) can’t not be complete convergence but IMGA can. The contrast simulation experiments between N...

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Bibliographic Details
Published inAdvances in Natural Computation pp. 978 - 982
Main Authors Zheng, Shiqin, Yang, Kongyu, Wang, Xiufeng
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Complete Convergence
Computer science; control theory; systems
Exact sciences and technology
Genetic Algorithm
Immune Network
Peak Individual
Stochastic Matrix
Markov chain
Availability
Immune algorithm
Genetic algorithm
Problem solving
Dating behavior
Mathematical programming
Immune system
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Summary:A new Immune Memory Genetic Algorithm (IMGA) based on the mechanism of immune memory and immune network is proposed in this article . Using Markov chains theory, we proven that NGA(Niche Genetic Algorithms) can’t not be complete convergence but IMGA can. The contrast simulation experiments between NGA and IMGA are performed. The experiments results validate the theoretical analysis and testify that IMGA has availability on solving multi-modal optimization problems, with quickly convergence ability and wonderful stability.
ISBN:9783540283256
3540283250
3540283234
9783540283232
ISSN:0302-9743
1611-3349
DOI:10.1007/11539117_136