Co-evolving parasites improve simulated evolution as an optimization procedure
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Machine Learning
Investigating the success of spatial coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Ideal Evaluation from Coevolution
Evolutionary Computation
Evolutionary consequences of coevolving targets
Evolutionary Computation
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The role of space is more and more accepted as a way to dramatically improve the success of coevolutionary function approximation. The process behind this success however is not yet fully understood. It is suggested that spatiality causes a persistence in the population diversity over generations and a better targeting of weak points in the host-population by means of the parasite. In this paper we will discuss the role of spatial pattern formation and speciation in coevolutonary function approximation and the influence on the success rate of coevolution. We observe specific patterns of speciation in the problems as well in the problem solving-population (LISP functions). These patterns depend on a combination of the functions and the fitness criteria. The success of the spatial coevolutionary process can be understood from the speciation patterns: only if the problems speciate such that 'easy ones' are first evaluated, the coevolutionary process is successful.