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DGA and Pareto Elitism : Improving Pareto Optimization

Abstract : Previous works have shown the efficiency of a new approach for the Genetic Algorithms, the Dual Genetic Algorithms, in the multiobjective optimization context. Dual Genetic Algorithms make use of a meta level to enhance the expressiveness of schemata, entities implicitly handle by Genetic Algorithms. In this paper, we show that this approach, coupled with a new method, Pareto Elitism, leads to very interesting results, in particular on an adaptation for multiobjective optimization of Royal Road Functions, the Multi Royal Road Functions. We begin with a quick reminder on multiobjective optimization, on what makes it different from single objective optimization and what has been done in this context. After this, the Dual Genetic Algorithm principles are briefly exposed, as well as previous results obtained. Then, we present Pareto Elitism, combining steady state and sharing techniques for Pareto optimization, and its behavior on Multi Royal Road Functions.
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Contributor : Manuel Clergue Connect in order to contact the contributor
Submitted on : Monday, July 30, 2007 - 2:46:45 PM
Last modification on : Tuesday, December 7, 2021 - 4:10:08 PM
Long-term archiving on: : Friday, April 9, 2010 - 12:12:03 AM


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  • HAL Id : hal-00165905, version 1



Manuel Clergue, Philippe Collard, Alessio Gaspar. DGA and Pareto Elitism : Improving Pareto Optimization. International ICSC Symposium on Soft Computing, 1997, Nîmes, France. p 315-321. ⟨hal-00165905⟩



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