Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/10339
Title: A pattern-based evolving mechanism for genetic algorithm to solve combinatorial optimization problems
Authors: Wang, Q
Yung, KL 
Ip, WH 
Keywords: Combinatorial mathematics
Genetic algorithms
Operations research
Pattern recognition
Polynomial approximation
Issue Date: 2003
Publisher: IEEE
Source: Proceedings of the 2003 IEEE International Workshop on Soft Computing in Industrial Applications, 2003 : SMCia/03, 23-25 June 2003, p. 97-101 How to cite?
Abstract: The combinatorial optimization problem always is ubiquitous in various applications and has been proved to be well known NP-hard problem that classical mathematical methods cannot solve within the polynomial time. To solve it, many approaches have been developed to find best or near best solutions. As one of such approaches, genetic algorithm is well known as being able to find satisfied solution within acceptable time, it is controlled by evolving mechanism to achieve optimization searching in the solutions space. In this paper, we propose a new evolving mechanism for GA to improve the solution quality and searching efficiency as well. This evolving mechanism can extract a generalized pattern from elite individuals in the whole population. The pattern is used to determine the selection probability to experience the genetic operations such as crossover, mutation, replication, etc. moreover, the evolving mechanism includes a replacement mechanism to substitute the worse individual for the potential excellent individual to expand searching space. The computation results show that the proposed evolving mechanism can work effectively and find satisfactory solutions better than traditional evolving mechanisms, even though the solution space increases with the problem size.
URI: http://hdl.handle.net/10397/10339
ISBN: 0-7803-7855-5
DOI: 10.1109/SMCIA.2003.1231351
Appears in Collections:Conference Paper

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