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
Coevolution, memory and balance
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Artificial Life
A new object motion estimation technique for video images, based on a genetic algorithm
IEEE Transactions on Consumer Electronics
Pareto Optimality in Coevolutionary Learning
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
| Hi-index | 0.00 |
This paper shows that the performance of coevolutionary genetic algorithms can be improved considerably by introducing a balancing mechanism. This is to prevent one population from "out-evolving" the other one. As a result, fitness variance is maintained and can be used to guide coevolution. Two different balancing mechanisms are introduced here. Their performance is compared to an unbalanced coevolutionary genetic algorithm. Finally, causal links are suggested between: a lack of balance, the loss of important niches and coevolutionary cycles.