Abstract
In this paper we analyze the performance of a novel genetic selection mechanism based on the classic tournament selection. This method tries to utilize the information present in the solution space of individuals, before mapping their solutions to a fitness measure. This allows to favour individuals dependent on what state the evolutionary search is in. If a population is caught up in several local optima, the correlation of the distance between the individuals and their performance tends to be lower than when the population converges to a single global optimum. We utilize this information by structuring the tournaments in a way favorable to each situation. The results of the experiments suggest that this new selection method is beneficial.
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The one with the lowest average distance to other individuals.
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A Appendix
A Appendix
Table 5 shows the quantiles and mean of the mean-squared errors for all the benchmark problems. The baseline results are dominated for nearly all problems except for \(f_{13}\) to \(f_{15}\).
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Oesch, C. (2017). Distance-Based Tournament Selection. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_45
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DOI: https://doi.org/10.1007/978-3-319-55849-3_45
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