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

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3 Author(s)
V. Petridis ; Dept. of Electr. & Comput. Eng., Aristotelian Univ. of Thessaloniki, Greece ; S. Kazarlis ; A. Bakirtzis

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

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IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)  (Volume:28 ,  Issue: 5 )