Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
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Proceedings of the 1st International Conference on Genetic Algorithms
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PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
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Evolutionary Computation
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IEEE Transactions on Evolutionary Computation
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IEEE Transactions on Evolutionary Computation
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IEEE Transactions on Evolutionary Computation
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International Journal of Information and Communication Technology
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Engineering Applications of Artificial Intelligence
Information Sciences: an International Journal
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International Journal of Intelligent Information and Database Systems
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ACM Computing Surveys (CSUR)
International Journal of Metaheuristics
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Artificial Intelligence Review
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This paper studies the strategies for multi-objective optimization in a dynamic environment. In particular, we focus on problems with objective replacement, where some objectives may be replaced with new objectives during evolution. It is shown that the Pareto-optimal sets before and after the objective replacement share some common members. Based on this observation, we suggest the inheritance strategy. When objective replacement occurs, this strategy selects good chromosomes according to the new objective set from the solutions found before objective replacement, and then continues to optimize them via evolution for the new objective set. The experiment results showed that this strategy can help MOGAs achieve better performance than MOGAs without using the inheritance strategy, where the evolution is restarted when objective replacement occurs. More solutions with better quality are found during the same time span.