Genetic algorithm with rough set theory

Last Friday we discussed the paper “The generic genetic algorithm incorporates with rough-set theory – An application of the web services composition” of Liang and Huang.  This is the standard mixture paper: a kind of algorithm + another soft computing technique + an application of the real world = a complete paper. I am not kidding, I think that combining several techniques, and more important, using them in real applications, should be a basis of research.

Rought set theory provides a way to create a set of decission rules that can be selected in every problem with functional requirements. For example in the extensive area of web services composition. We can provide this information to a genetic algorithm to compose services avoiding constrained solutions and initial population using that decission rules. The authors also use non-functional requirements, such QoS, cost or avilability in the fitness function. They conclude that the usage of rough set in a GA could increase the convergence (but it is necessary to keep some unfeasible solutions during the process, just in case).

It is a easy-to-read paper, so probably you would like to read it instead my summary ;) Moreover, they present some ideas in the web service composition area.

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