A self-crossover Genetic Algorithm for job shop scheduling problem

During the application of Genetic Algorithm (GA) for job shop scheduling problem (JSSP), chromosome representation and evolution strategy are the main consideration in order to guarantee the feasibility of solution. Crossover operation between two feasible solutions (parents) may result in infeasible solution (offspring).Inspired by the existence of self-reproducing in nature, this paper presents a self-crossover genetic algorithm for job shop scheduling problem (JSSP). The chromosome representation of the problem is based on work piece and the crossover operation is based on single individual. The approach was tested on a standard six-job six-machine (6×6) JSSP. The computational results validate the effectiveness of the proposed algorithm.

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