Co-Evolution in the Successful Learning of Backgammon Strategy
Machine Learning
Blondie24: playing at the edge of AI
Blondie24: playing at the edge of AI
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Solution concepts in coevolutionary algorithms
Solution concepts in coevolutionary algorithms
Ideal Evaluation from Coevolution
Evolutionary Computation
Combating Coevolutionary Disengagement by Reducing Parasite Virulence
Evolutionary Computation
A sequential niche technique for multimodal function optimization
Evolutionary Computation
New methods for competitive coevolution
Evolutionary Computation
A tunable model for multi-objective, epistatic, rugged, and neutral fitness landscapes
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A co-evolutionary approach for military operational analysis
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Ensemble of niching algorithms
Information Sciences: an International Journal
Iterated n-player games on small-world networks
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A framework for generating tunable test functions for multimodal optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary Optimization and Learning
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
Measuring Generalization Performance in Coevolutionary Learning
IEEE Transactions on Evolutionary Computation
Evolving neural networks to play checkers without relying on expert knowledge
IEEE Transactions on Neural Networks
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Coevolutionary algorithms are a special kind of evolutionary algorithm with advantages in solving certain specific kinds of problems. In particular, competitive coevolutionary algorithms can be used to study problems in which two sides compete against each other and must choose a suitable strategy. Often these problems are multimodal -- there is more than one strong strategy for each side. In this paper, we introduce a scalable multimodal test problem for competitive coevolution, and use it to investigate the effectiveness of some common coevolutionary algorithm enhancement techniques.