ALPS: the age-layered population structure for reducing the problem of premature convergence
Proceedings of the 8th annual conference on Genetic and evolutionary computation
The root causes of code growth in genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
A simple powerful constraint for genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
On Crossover Success Rate in Genetic Programming with Offspring Selection
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
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Hereditary Repulsion (HR) is a selection method coupled with a fitness constraint that substantially improves the performance and consistency of evolutionary algorithms. This also manifests as improved generalisation in the evolved GP expressions. We examine the behaviour of HR on the difficult Parity 5 problem using a population size of only 24 individuals. The negative effects of convergence are amplified under these circumstances and we progress through a series of insights and experiments which dramatically improve the consistency of the algorithm, resulting in a 70% success rate with the same small population. By contrast, a steady state GP system using a population of 5000 only had a success rate of 8%. We then confirm the effectiveness of these results in a number of arbitrary problem domains.