Abstract
The runtime performance of many evolutionary algorithms depends heavily on their parameter values, many of which are problem specific. Previous work has shown that the modification of parameter values at runtime can lead to significant improvements in performance. In this paper we discuss both the ‘when’ and ‘how’ aspects of implementing self-adaptation in a Genetic Programming system, focusing on the crossover operator. We perform experiments on Tartarus Problem instances and find that the runtime modification of crossover parameters at the individual level, rather than population level, generate solutions with superior performance, compared to traditional crossover methods.
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Notes
- 1.
The descriptive terms ‘Adaptive’ and ‘Self-Adaptive’ are used in the broad general context of Evolutionary Computation. These terms have distinct meanings in fields such as Artificial Life; based on strict Ecological and Psychological definitions.
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Griffiths, T.D., Ekárt, A. (2018). Self-adaptive Crossover in Genetic Programming: The Case of the Tartarus Problem. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11101. Springer, Cham. https://doi.org/10.1007/978-3-319-99253-2_19
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