Varying fitness functions in genetic algorithm constrainedoptimization: the cutting stock and unit commitment problems

Authors:
V. Petridis;S. Kazarlis;A. Bakirtzis
Affiliations:
Dept. of Electr. & Comput. Eng., Aristotelian Univ. of Thessaloniki;-;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
1998

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Abstract

We present a specific varying fitness function technique in genetic algorithm (GA) constrained optimization. This technique incorporates the problem's constraints into the fitness function in a dynamic way. It consists of forming a fitness function with varying penalty terms. The resulting varying fitness function facilitates the GA search. The performance of the technique is tested on two optimization problems: the cutting stock, and the unit commitment problems. Also, new domain-specific operators are introduced. Solutions obtained by means of the varying and the conventional (nonvarying) fitness function techniques are compared. The results show the superiority of the proposed technique