“Learning” Bayesian heuristics in flow-shop problem

Article type: Research Article

Authors: Mockus, Jonas | Kuryla, Henrikas

Affiliations: Vytautas Magnus University, Vileikos St. 8, 2042 Kaunas, Lithuania | Institute of Mathematics and Informatics, Akademijos St. 4, 2600 Vilnius, Lithuania

Abstract: We compare two alternative ways to use the Bayesian approach in heuristic optimization. The “no-learning” way means that we optimize the randomization parameters for each problem separately. The “learning” way means that we optimize the randomization parameters for some “learning” set of problems. We use those parameters later on for a family of related problems. We define the learning efficiency as a non-uniformity of optimal parameters while solving a set of randomly generated problems. We show that for flow-shop problems the non-uniformity of optimal parameters is significant. It means that the Bayesian learning is efficient in those problems.

Keywords: learning, optimization, discrete, global, Bayesian, flow-shop

DOI: 10.3233/INF-1995-6304

Journal: Informatica, vol. 6, no. 3, pp. 289-298, 1995