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
The Effects of Representational Bias on Collaboration Methods in Cooperative Coevolution
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
Large scale evolutionary optimization using cooperative coevolution
Information Sciences: an International Journal
Pareto cooperative coevolutionary genetic algorithm using reference sharing collaboration
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Comparison of sorting algorithms for multi-fitness measurement of cooperative coevolution
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
A cooperative coevolutionary approach to partitional clustering
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
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Problem decomposition is the first step to apply a cooperative coevolutionary algorithm (CCEA) to a problem. This step determines how to divide the problem into components with an appropriate granularity. Most of the current methods implement a natural-based decomposition where each component plays a specific role or represents an emergent property. However, there could exist some real problems that the roles or the properties are hard to determine or somewhat unclear. This paper offers a solution by decomposing the problems in an unnatural way, which implements a blind decomposition. Our primary analysis indicates that the blind decomposition is feasible. We also provide some basic advice on how to implement the blind decomposition in combination with different collaboration methods.