Most scheduling problems are NP-hard, the time required to solve the problem optimally increases exponentially with the size of the problem. Scheduling problems have important applications, and a number of heuristic algorithms have been proposed to determine relatively good solutions in polynomial time. Recently, genetic algorithms (GA) are successfully used to solve scheduling problems, as shown by the growing numbers of papers. GA are known as one of the most efficient algorithms for solving scheduling problems. But, when a GA is applied to scheduling problems various crossovers and mutations operators can be applicable. This paper presents and examines a new concept of genetic operators for scheduling problems. A software tool called hybrid and flexible genetic algorithm (HybFlexGA) was developed to examine the performance of various crossover and mutation operators by computing simulations of job scheduling problems.
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Ferrolho, A., Crisóstomo, M. (2009). Scheduling Jobs with Genetic Algorithms. In: Machado, J.A.T., Pátkai, B., Rudas, I.J. (eds) Intelligent Engineering Systems and Computational Cybernetics. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8678-6_17
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