An analysis of cooperative coevolutionary algorithms
An analysis of cooperative coevolutionary algorithms
Monotonic solution concepts in coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Ideal Evaluation from Coevolution
Evolutionary Computation
Emergent geometric organization and informative dimensions in coevolutionary algorithms
Emergent geometric organization and informative dimensions in coevolutionary algorithms
A no-free-lunch framework for coevolution
Proceedings of the 10th annual conference on Genetic and evolutionary computation
No free lunch and free leftovers theorems for multiobjective optimisation problems
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
On the practicality of optimal output mechanisms for co-optimization algorithms
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
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Recent work in test based coevolution has focused on employing ideas from multi-objective optimization in coevolutionary domains. So called Pareto coevolution treats the coevolving set of test cases as objectives to be optimized in the sense of multi-objective optimization. Pareto coevolution can be seen as a relaxation of traditional multi-objective evolutionary optimization. Rather than being forced to determine the outcome of a particular individual on every objective, pareto coevolution allows the examination of an individual's outcome on a particular objective. By introducing the notion of certifying pareto dominance and mutual non-dominance, this paper proves for the first time that free lunches exist for the class of pareto coevolutionary optimization problems. This theoretical result is of particular interest because we explicitly provide an algorithm for pareto coevolution which has better performance, on average, than all traditional multi-objective algorithms in the relaxed setting of pareto coevolution. The notion of certificates of preference/non-preference has potential implications for coevolutionary algorithm design in many classes of coevolution as well as for general multi-objective optimization in the relaxed setting of pareto coevolution.