Artificial Life
Coevolutionary search among adversaries
Coevolutionary search among adversaries
Solving a timetabling problem using hybrid genetic algorithms
Software—Practice & Experience
A Host-Parasite Genetic Algorithm for Asymmetric Tasks
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Methods for Competitive Co-Evolution: Finding Opponents Worth Beating
Proceedings of the 6th International Conference on Genetic Algorithms
Co-evolutionary Constraint Satisfaction
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
A new object motion estimation technique for video images, based on a genetic algorithm
IEEE Transactions on Consumer Electronics
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Raising the Dead: Extending Evolutionary Algorithms with a Case-Based Memory
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
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This paper studies the role of two mechanisms - memory and balance - to exploit the arms race resulting from predator-prey interactions when solving a given problem. Memory ensures that individuals are not only well adapted to the current members of the opposite population but also to earlier generations of opponents. A balanced (co)evolution, on the other hand, adapts the speed of evolution (i.e. the reproduction rate) to the performance of a population. It leads to a steady progress in both populations. Indirectly, a balanced (co)evolution avoids a premature loss of genetic diversity. This in turn, diminishes the need for a long memory span. The current paper shows how both mechanisms can be incorporated in Coevolutionary Genetic Algorithms (CGAs). Empirical results support the importance of, and interaction between, both mechanisms.