Analysis of complete convergence for genetic algorithm with immune memory

Authors:
Shiqin Zheng;Kongyu Yang;Xiufeng Wang
Affiliations:
College of Information Technology and Science, Nankai University, Tianjin, China;College of Information & Electrical Engineering, Shandong Institute of Architecture & Engineering, Jinan, China;College of Information Technology and Science, Nankai University, Tianjin, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Genetic algorithms with sharing for multimodal function optimization

Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Artificial Immune Systems: A New Computational Intelligence Paradigm

Artificial Immune Systems: A New Computational Intelligence Paradigm

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Abstract

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.