A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Journal of Global Optimization
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
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
Cooperative models of particle swarm optimizers
Cooperative models of particle swarm optimizers
Cooperative co-evolutionary differential evolution for function optimization
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Tiny GAs for image processing applications
IEEE Computational Intelligence Magazine
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Parallel cooperative micro-particle swarm optimization: A master-slave model
Applied Soft Computing
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High-dimensional optimization problems appear very often in demanding applications. Although evolutionary algorithms constitute a valuable tool for solving such problems, their standard variants exhibit deteriorating performance as dimension increases. In such cases, cooperative approaches have proved to be very useful, since they divide the computational burden to a number of cooperating subpopulations. In contrast, Micro-evolutionary approaches constitute light versions of the original evolutionary algorithms that employ very small populations of just a few individuals to address optimization problems. Unfortunately, this property is usually accompanied by limited efficiency and proneness to get stuck in local minima. In the present work, an approach that combines the basic properties of cooperation and Micro-evolutionary algorithms is presented for the Differential Evolution algorithm. The proposed Cooperative Micro-Differential Evolution approach employs small cooperative subpopulations to detect subcomponents of the original problem solution concurrently. The subcomponents are combined through cooperation of subpopulations to build complete solutions of the problem. The proposed approach is illustrated on high-dimensional instances of five widely used test problems with very promising results. Comparisons with the standard Differential Evolution algorithm are also reported and their statistical significance is analyzed.