Constraint satisfaction on dynamic environments by the means of coevolutionary genetic algorithms

We discuss adaptability of evolutionary computations in dynamic environments. We introduce two classes of dynamic environments which are utilizing the notion of constraint satisfaction problems: changeover and gradation. The changeover environment is a problem class which consists of a sequence of the constraint networks with the same nature. On the other hand, the gradation environment is a problem class which consists of a sequence of the constraint networks such that the sequence is associated with two constraint networks, i. e., initial and target, and all constraint networks in the sequence metamorphosis from the initial constraint network to the target constraint network. We compare coevolutionary genetic algorithms with SGA in computational simulations. Experimental results on the above dynamic environments confirm us the effectiveness of our approach, i.e., coevolutionary genetic algorithm

Digital Object Identifier: 10.1109/IECON.2000.972464
Published with permission from the copyright holder. This is the institute's copy, as published in Industrial Electronics Society, 2000. IECON 2000. 26th Annual Confjerence of the IEEE, 22-28 Oct. 2000, Vol. 4, Pages 2935-2940.
Publisher URL:http://dx.doi.org/10.1109/IECON.2000.972464
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