Coevolutionary search among adversaries
Coevolutionary search among adversaries
The design and analysis of a computational model of cooperative coevolution
The design and analysis of a computational model of cooperative coevolution
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Host-Parasite Genetic Algorithm for Asymmetric Tasks
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Methods for Competitive Co-Evolution: Finding Opponents Worth Beating
Proceedings of the 6th International Conference on Genetic Algorithms
Evaluation of a Simple Host-Parasite Genetic Algorithm
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
New methods for competitive coevolution
Evolutionary Computation
Pareto Optimality in Coevolutionary Learning
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
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Previous research on host-parasite algorithms has shown that the co-evolutionaxy arms race is difficult to sustain when the tasks faced by hosts and parasites are heavily asymmetric. We have therefore proposed an asymmetry-handling algorithm, AHPA, with a capacity for self-adapting the allocation of generations to hosts and parasites, so that the problem asymmetry is counteracted. In this paper we discuss the need for systematic evaluation of this algorithm, so that its behaviour under varying levels of asymmetry can be studied in detail. We propose the use of Kaufmann's NK landscape model for this purpose, and show how the model can be adapted for the testing of host-parasite algorithms. Using the adapted model, we present simulation results which confirm AHPA's ability to sustain a stable arms-race under varying levels of asymmetry.