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
The evolution of mental models
Advances in genetic programming
Machine Learning
Coevolutionary search among adversaries
Coevolutionary search among adversaries
Solution concepts in coevolutionary algorithms
Solution concepts in coevolutionary algorithms
The MaxSolve algorithm for coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
On identifying global optima in cooperative coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Ideal Evaluation from Coevolution
Evolutionary Computation
A game-theoretic memory mechanism for coevolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Objective reduction in evolutionary multiobjective optimization: Theory and applications
Evolutionary Computation
Formal analysis and algorithms for extracting coordinate systems of games
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
How many dimensions in co-optimization
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Improving coevolution by random sampling
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Coevolution has often been based on averaged outcomes, resulting in unstable evaluation. Several theoretical approaches have used archives to provide stable evaluation. However, the number of tests required by some of these approaches can be prohibitive of practical applications. Recent work has shown the existence of a set of underlying objectives which compress evaluation information into a potentially small set of dimensions. We consider whether these underlying objectives can be approximated online, and used for evaluation in a coevolution algorithm. The Dimension Extracting Coevolutionary Algorithm (DECA) is compared to several recent reliable coevolution algorithms on a Numbers game problem, and found to perform efficiently. Application to the more realistic Tartarus problem is shown to be feasible. Implications for current coevolution research are discussed.