The design and analysis of a computational model of cooperative coevolution
The design and analysis of a computational model of cooperative coevolution
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
A Game-Theoretic Approach to the Simple Coevolutionary Algorithm
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Robustness in cooperative coevolution
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Analyzing cooperative coevolution with evolutionary game theory
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Artificial Life
The Cooperative Coevolutionary (1+1) EA
Evolutionary Computation
Dynamic Cooperative Coevolutionary Sensor Deployment Via Localized Fitness Evaluation
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Pareto cooperative coevolutionary genetic algorithm using reference sharing collaboration
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Empirical analysis of cooperative coevolution using blind decomposition
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Natural vs. unnatural decomposition in cooperative coevolution
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
| Hi-index | 0.00 |
Cooperative coevolutionary algorithms (CCEAs) have been applied to many optimization problems with varied success. Recent empirical studies have shown that choices surrounding methods of collaboration may have a strong impact on the success of the algorithm. Moreover, certain properties of the problem landscape, such as variable interaction, greatly influence how these choices should be made. A more general view of variable interaction is one that considers epistatic linkages which span population boundaries. Such linkages can be caused by the decomposition of the actual problem, as well as by CCEA representation decisions regarding population structure. We posit that it is the way in which represented problem components interact, and not necessarily the existence of cross-population epistatic linkages that impacts these decisions. In order to explore this issue, we identify two different kinds of representational bias with respect to the population structure of the algorithm, decompositional bias and linkage bias. We provide analysis and constructive examples which help illustrate that even when the algorithm's representation is poorly suited for the problem, the choice of how best to select collaborators can be unaffected.