Abstract
In this paper we propose a strategy to incorporate heuristic knowledge into the initial population of a Genetic Algorithm to solve Job Shop Scheduling problems. This is a generalization of strategy we proposed in a previous work. The experimental results reported confirm that the new strategy improves the former one. In particular, a higher diversity in achieved among the heuristic individuals, and at the same time the mean fitness is improved. Moreover, these improvements transate into a better convergence of the GA.
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Vela, C.R., Varela, R., Puente, J. (2001). Initialization in Genetic Algorithms for Constraint Satisfaction Problems. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_83
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DOI: https://doi.org/10.1007/3-540-45720-8_83
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