Abstract
We propose an extended parameter-free genetic algorithm. The first step of this study is that each individual includes additional gene whose phenotype indicates a mutation rate. The second step is an extension of the selection rule of the parameter-free genetic algorithm, in which each individual has a characteristic neighborhood radius and the individuals generated near the parents are not selected to avoid trapping a local minimum. The characteristic neighborhood radius of an individual is given by the distance between before mutation and after mutation. As a result of the experiment for function minimization problems, effect of the population size appears and the success rate is improved.
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Adachi, S. (2010). Effect of Population Size in Extended Parameter-Free Genetic Algorithm. In: Peper, F., Umeo, H., Matsui, N., Isokawa, T. (eds) Natural Computing. Proceedings in Information and Communications Technology, vol 2. Springer, Tokyo. https://doi.org/10.1007/978-4-431-53868-4_13
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DOI: https://doi.org/10.1007/978-4-431-53868-4_13
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-53867-7
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