Machine Learning
Coevolutionary search among adversaries
Coevolutionary search among adversaries
ALIFE Proceedings of the sixth international conference on Artificial life
Tracking the Red Queen: Measurements of Adaptive Progress in Co-Evolutionary Simulations
Proceedings of the Third European Conference on Advances in Artificial Life
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
Ideal Evaluation from Coevolution
Evolutionary Computation
A no-free-lunch framework for coevolution
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evaluation and Diversity in Co-evolution
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
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This paper introduces the Objective Fitness Correlation, a new tool to analyze the evaluation accuracy of coevolutionary algorithms. Accurate evaluation is an essential ingredient in creating adequate coevolutionary dynamics. Based on the notion of a solution concept, a new definition for objective fitness in coevolution is provided. The correlation between the objective fitness and the subjective fitness used in a coevolutionary algorithm yields the Objective Fitness Correlation. The OFC measure is applied to three coevolutionary evaluation methods. It is found that the Objective Fitness Correlation varies substantially over time. Moreover, a high OFC is found to correspond to periods where the algorithm is able to increase the objective quality of individuals. This is evidence of the utility of OFC as a measure to evaluate and compare coevolutionary evaluation mechanisms. The Objective Fitness Correlation (OFC) provides a precise analytical tool to measure the accuracy of evaluation in coevolutionary algorithms.