Abstract. Cartesian Genetic Programming (CGP) is applied to solving differential equations (DE). We illustrate that repeated elements in analytic solutions to DE can be exploited under GP. An analysis is carried out of the search space in tree and CGP frameworks, examining the complexity of different DE problems. Experimental results are provided against benchmark ordinary and partial differential equations. A system of ordinary differential equations (SODE) is solved using multiple outputs from a genome. We discuss best heuristics when generating DE solutions through evolutionary search.
Abstract. We investigate the occurrence of two pathological coevolutionary behaviours, specifically disengagement and cycling, in GP systems. An empirical analysis is carried out using constructed GP problems and a historical pursuit and evasion task, the Game of Tag. The effect of semantic bias on the likelihood of pathologies and performance in a coevolutionary context is examined. We present findings correlating semantic locality in the genotype to phenotype map to disengagement and cycling in a minimal competitive coevolutionary algorithm.
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