Abstract
Backward chaining evolutionary algorithms (BC-EA) offer the prospect of run-time efficiency savings by reducing the number of fitness evaluations without significantly changing the course of genetic algorithm or genetic programming runs. “Tournament selection, iterated coupon-collection problem, and backward-chaining evolutionary algorithm,” Poli, FOGA, 2005 describes how BC-EA does this by avoiding the generation and evaluation of individuals which never appear in selection tournaments. It suggests the largest savings occur in very large populations, short runs, small tournament sizes and shows actual savings in fixed-length binary GAS. Here, we provide a generational GP implementation, including mutation and two offspring crossover of BC-EA and empirically investigate its efficiency in terms of both fitness evaluations and effectiveness.
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Poli, R., Langdon, W.B. (2006). Running Genetic Programming Backwards. In: Yu, T., Riolo, R., Worzel, B. (eds) Genetic Programming Theory and Practice III. Genetic Programming, vol 9. Springer, Boston, MA. https://doi.org/10.1007/0-387-28111-8_9
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DOI: https://doi.org/10.1007/0-387-28111-8_9
Publisher Name: Springer, Boston, MA
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