This paper proposes the association of two approaches of GP which improve efficiency and reduce bloat. The first approach is to use a multi-population version of GP and the second one is to employ populations that can change size dynamically and adaptively. The latter approach consists in deleting or adding individuals in the population as a function of the current fitness and two other parameters. We test this approach on three well-known problems in GP, artificial ant, even parity 5 and one instance of the symbolic regression. We find that the combination of these two methods improves the quality of the individuals in the populations while keeping their size as small as possible and decreases the amount of resources required
Rochat, D., Vanneschi, L., Tomassini, M. (2005). Dynamic size populations in distributed genetic programming. In Genetic Programming, 8th European Conference, EuroGP 2005, Lausanne, Switzerland, March 30 - April 1, 2005. Proceedings (pp.50-61). Springer [10.1007/978-3-540-31989-4_5].
Dynamic size populations in distributed genetic programming
Vanneschi, L;
2005
Abstract
This paper proposes the association of two approaches of GP which improve efficiency and reduce bloat. The first approach is to use a multi-population version of GP and the second one is to employ populations that can change size dynamically and adaptively. The latter approach consists in deleting or adding individuals in the population as a function of the current fitness and two other parameters. We test this approach on three well-known problems in GP, artificial ant, even parity 5 and one instance of the symbolic regression. We find that the combination of these two methods improves the quality of the individuals in the populations while keeping their size as small as possible and decreases the amount of resources required
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