Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Solving the N-bit parity problem using neural networks
Neural Networks
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
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
Robust non-linear control through neuroevolution
Robust non-linear control through neuroevolution
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Accelerated Neural Evolution through Cooperatively Coevolved Synapses
The Journal of Machine Learning Research
Memetic algorithms for continuous optimisation based on local search chains
Evolutionary Computation
A new memetic algorithm using particle swarm optimization and genetic algorithm
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Microgenetic algorithms as generalized hill-climbing operators forGA optimization
IEEE Transactions on Evolutionary Computation
COVNET: a cooperative coevolutionary model for evolving artificial neural networks
IEEE Transactions on Neural Networks
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Cooperative coevolution has been a major approach to neuro-evolution. Memetic algorithms employ local search to selected individuals in a population. This paper presents a new cooperative coevolution framework that incorporates crossover-based local search. The proposed approach effectively makes use of local search without adding to the computational cost in the sub-populations of cooperative coevolution. The relationship between the intensity of, and interval between the local search is empirically investigated and a heuristic for the adaptation of the local search intensity during evolution is presented. The method is used for training feedforward neural networks on eight pattern classification problems. The results show an improved performance in terms of optimisation time, scalability and robustness for most of these problems.