Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems
نویسندگان
چکیده
منابع مشابه
Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems
In this paper, we study the problem features that may cause a multi-objective genetic algorithm (GA) difficulty in converging to the true Pareto-optimal front. Identification of such features helps us develop difficult test problems for multi-objective optimization. Multi-objective test problems are constructed from single-objective optimization problems, thereby allowing known difficult featur...
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Evolutionary Computation (EC) is the field of computational systems that use ideas and get inspiration from natural evolution [1]. Genetic Algorithms (GA) fall into the category of EC. GA are a type of search and optimization algorithm based on the mechanisms of genetics and natural selection. The canonical form of GA encodes each candidate solution to a given problem as a binary, integer, or r...
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Two multi-objective genetic algorithms, an elitist version of MOGA and NSGA-II, were applied to solve two linear control design problems. The first was a H2 problem with a PI controller structure, for a first order stable plant. The second was a mixed H2/H4 control problem. In both cases, three indicators were used to evaluate each algorithm performance: Set coverage, spread and hypervolume. It...
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ژورنال
عنوان ژورنال: Evolutionary Computation
سال: 1999
ISSN: 1063-6560,1530-9304
DOI: 10.1162/evco.1999.7.3.205