title: Varying fitness functions in genetic algorithm constrained optimization: the cutting stock and unit commitment problems.
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.
authors.affiliation: Dept. of Electr. & Comput. Eng., Aristotelian Univ. of Thessaloniki.
authors.forename: V
authors.initials: V
authors.lastname: Petridis
authors.forename: S
authors.initials: S
authors.lastname: Kazarlis
authors.forename: A
authors.initials: A
authors.lastname: Bakirtzis