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
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
Monotonic solution concepts in coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The parallel Nash Memory for asymmetric games
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Unbiased coevolutionary solution concepts
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Monotonicity versus performance in co-optimization
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Free lunches in pareto coevolution
Proceedings of the 11th Annual conference on Genetic and 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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The No-Free-Lunch theorem is a fundamental result in the field of black-box function optimization. Recent work has shown that coevolution can exhibit free lunches. The question as to which classes of coevolution exhibit free lunches is still open. In this paper we present a novel framework for analyzing No-Free-Lunch like results for classes of coevolutionary algorithms. Our framework has the advantage of analyzing No-Free-Lunch like inquiries in terms of solution concepts and isomorphisms on the weak preference relation on solution configurations. This allows coevolutionary algorithms to be naturally classified by the type of solution they seek. Using the weak preference relation also permits us to present a simpler definition of performance metrics than that used in previous coevolutionary No-Free-Lunch work, more akin to the definition used in the original No-Free-Lunch theorem. The framework presented in this paper can be viewed as the combination of the ideas and definitions from two separate theoretical frameworks for analyzing search algorithms and coevolution consistent with the terminology of both. We also present a new instance of free lunches in coevolution which demonstrates the applicability of our framework to analyzing coevolutionary algorithms based upon the solution concept which they implement.