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
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Coevolutionary search among adversaries
Coevolutionary search among adversaries
Co-Evolution in the Successful Learning of Backgammon Strategy
Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Tracking the Red Queen: Measurements of Adaptive Progress in Co-Evolutionary Simulations
Proceedings of the Third European Conference on Advances in Artificial Life
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Methods for statistical inference: extending the evolutionary computation paradigm
Methods for statistical inference: extending the evolutionary computation paradigm
Evolution of complexity in real-world domains
Evolution of complexity in real-world domains
Ideal Evaluation from Coevolution
Evolutionary Computation
Intransitivity revisited coevolutionary dynamics of numbers games
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Coevolution of neural networks using a layered pareto archive
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Environmental effects on the coevolution of pursuit and evasion strategies
Genetic Programming and Evolvable Machines
Representation development from pareto-coevolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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Designing an adequate fitness function requires substantial knowledge of a problem and of features that indicate progress towards a solution. Coevolution takes the human out of the loop by dynamically constructing the evaluation function based on interactions between evolving individuals. A question is to what extent such automatic evaluation can be adequate. We define the notion of an ideal evaluation function. It is shown that coevolution can in principle achieve ideal evaluation. Moreover, progress towards ideal evaluation can be measured. This observation leads to an algorithm for coevolution. The algorithm makes stable progress on several challenging abstract test problems.