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    STUDIA INFORMATICA - Issue no. 1 / 2000  

  Abstract:  Several evolutionary algorithms for solving multiobjective opti- mization problems have been proposed ([2, 5, 6, 7, 8, 9, 10, 12, 13], see also the reviews [1, 11, 14]). AII algorithms aim to give a discrete picture of the Pareto optimal set (and of the corresponding Pareto frontier). But Pareto optimal set is usually a continuous region in the search space. It follows that a continuous region is represented by a discrete picture. When continuos decision regions are represented by discrete solutions there is an information loss. In this paper we propose a new evolutionary approach combing a new solution representation, new variation operators and a multimodal optimiza- tion technique. In the proposed approach continuous decision regions may be detected. A solution is either a closed interval or a point. The solutions in the final population will give a realistic representation of Pareto optimal set. Each solution in this population corresponds to a decision region of Pareto set. Proposed technique does not use a secondary population of non-dominated already founded.  
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