An Empirical Study on GAs "Without Parameters"
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
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
The Cooperative Coevolutionary (1+1) EA
Evolutionary Computation
Improving multiclass pattern recognition with a co-evolutionary RBFNN
Pattern Recognition Letters
Exploring the explorative advantage of the cooperative coevolutionary (1+1) EA
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Dual-population based coevolutionary algorithm for designing RBFNN with feature selection
Expert Systems with Applications: An International Journal
A multi-agent organizational framework for coevolutionary optimization
Transactions on Petri nets and other models of concurrency IV
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Typically GAs have a number of fixed control parameters which have a significant effect upon the performance of the search. This paper deals with the effects of self-adapting control parameters, and the adaptation of population size within the sub-populations of a coevolutionary model. We address the need to investigate the potential of these adaptive techniques within a co-evolutionary GA, and propose a number of model variants implementing adaptation. These models were tested on some well known function optimisation problems. The experimental results show that one or more of the model variants yield improvements over the baseline co-evolutionary model.