An adaptive hybrid immune genetic algorithm for maximum cut problem

Authors:
Hong Song;Dan Zhang;Ji Liu
Affiliations:
School of Mechanical Engineering, Xi'an Shiyou University, Xi'an, Shaanxi, China;School of Electronics & Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China;School of Electronics & Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 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 in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
A novel genetic algorithm based on immunity

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

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Abstract

The goal of maximum cut problem is to partition the vertex set of an undirected graph into two parts in order to maximize the cardinality of the set of edges cut by the partition. This paper proposes an Adaptive Hybrid Immune Genetic Algorithm, which includes key techniques such as vaccine abstraction, vaccination and affinity-based selection. A large number of instances have been simulated, and the results show that proposed algorithm is superior to existing algorithms.