Transactions of the Society of Instrument and Control Engineers
Online ISSN : 1883-8189
Print ISSN : 0453-4654
ISSN-L : 0453-4654
Genetic Symbiosis Algorithm for Multiobjective Optimization Problems
Jiangming MAOKotaro HIRASAWAJinglu HUJunichi MURATA
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2001 Volume 37 Issue 9 Pages 893-901


Evolutionary Algorithms are often well-suited for optimization problems. Since mid 1980's, the interest in multi-objective problems has been expanding rapidly. Various evolutionary algorithms for multiobjective problems have been developed which are capable of searching for multiple solutions concurrently in a single run. In this paper, we propose a new genetic symbiosis algorithm (GSA) for multiobjective optimization problems (MOP) based on the symbiotic concept found widely in ecosystems. In the proposed GSA for MOP, a set of symbiotic parameters are introduced to modify the fitness of individuals used for reproduction so as to obtain a variety of Pareto solutions corresponding to user's demands. The symbiotic parameters are trained by minimizing a user defined criterion function. Several numerical simulations are carried out to demonstrate the effectiveness of the proposed GSA.

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