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A genetic algorithm with multi-step crossover for job-shop scheduling problems

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2 Author(s)
Yamada, T. ; NTT Commun. Sci. Labs., Japan ; Nakano, R.

Genetic algorithms (GAs) have been designed as general purpose optimization methods. GAs can be uniquely characterized by their population-based search strategies and their operators: mutation, selection and crossover. In this paper, we propose a new crossover called multi-step crossover (MSX) which utilizes a neighborhood structure and a distance in the problem space. Given parents, MSX successively generates their descendents along the path connecting both of them. MSX was applied to the job-shop scheduling problem (JSSP) as a high-level crossover to work on the critical path. Preliminary experiments using JSSP benchmarks showed the promising performance of a GA with the proposed MSX

Published in:

Genetic Algorithms in Engineering Systems: Innovations and Applications, 1995. GALESIA. First International Conference on (Conf. Publ. No. 414)

Date of Conference:

12-14 Sep 1995