On using genetic algorithms to search program spaces
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
The design and analysis of a computational model of cooperative coevolution
The design and analysis of a computational model of cooperative coevolution
Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Proceedings of the 5th International Conference on Genetic Algorithms
A real-coded genetic algorithm using the unimodal normal distribution crossover
Advances in evolutionary computing
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Markov chain models of bare-bones particle swarm optimizers
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Heterogeneous particle swarm optimization
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Information Sciences: an International Journal
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Most optimization algorithms suffer from a significant deterioration in performance as the dimensionality and complexity of the problem search space increases. Also these algorithms, given certain configurations, typically show markedly improved performance on a particular problem only to exhibit poor performance on another. The first issue could be resolved by using a cooperative algorithm to divide the problem complexity among its participating algorithms, making the problem easier to solve. The second issue could then be resolved with the use of differently configured participating algorithms within the overall cooperative algorithm. This paper investigates the possibility of combining different population-based algorithms within a cooperative algorithm. The aim is to take advantage of different algorithm characteristics regarding parameter settings, explorative/exploitative capacity, convergence speed and other behaviors in finding solutions to various optimization problems.