An Adaptive Hybrid Immune Genetic Algorithm for Maximum Cut Problem

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 abstracti...

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Bibliographic Details
Published inAdvances in Natural Computation pp. 863 - 866
Main Authors Song, Hong, Zhang, Dan, Liu, Ji
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
Immune Technique
Information Entropy
Information Entropy Theory
Maximum Span Tree
Weighted Graph Node
Immune algorithm
Adaptive algorithm
Genetic algorithm
Cardinal number
Abstraction
Non directed graph
Immune system
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ISBN9783540283256
3540283250
3540283234
9783540283232
ISSN0302-9743
1611-3349
DOI10.1007/11539117_121

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Summary: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.
Bibliography:Supported by the Nation Science Foundation of China (No.60173059).
ISBN:9783540283256
3540283250
3540283234
9783540283232
ISSN:0302-9743
1611-3349
DOI:10.1007/11539117_121