A Genetic Algorithm with Multi-Step Crossover for Job-Shop Scheduling Problems

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 the 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.