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An Improvement of Genetic Algorithms by Search Space Reductions in Solving Large-Scale Flowshop Problems (特集 メタヒューリスティクスの理論と応用)

While searching for suboptimal solutions for large-scale problems, it is critical to force search algorithms on promising regions. This paper presents genetic algorithms with search space reductions (RGAs) and their application to solving large-scale permutation flowshop problems. The reduced search spaces are defined by adding precedence constraints generated by heuristic rules. To balance between the size of reduced spaces and the risk of missing good solutions, a set of consecutively included search spaces is proposed. RGAs are implemented and their performance is tested on a large-scale flowshop problem. Primary experiments show that the RGAs outperform the standard genetic algorithms greatly. Moreover, we propose an improved uniform crossover operator which preserves the precedence constraints to focus genetic search on the specified search spaces. It is shown from computational experiments that the mechanism of search space reductions works well with GAs and RGAs outperform standard genetic algorithms significantly.