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Using Case Based Learning to Improve Genetic Algorithm Based Design Optimization

Using Case Based Learning to Improve Genetic Algorithm Based Design Optimization,Khaled Rasheed,Haym Hirsh

Using Case Based Learning to Improve Genetic Algorithm Based Design Optimization   (Citations: 16)
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In this paper we describe a method for improving genetic-algorithm-based optimization using case-based learning. The idea is to utilize the sequence of points explored during a search to guide further exploration. The proposed method is particularly suitable for continuous spaces with expensive evaluation functions, such as arise in engineering design. Empirical results in two engineering design domains and across different representations demonstrate that the proposed method can significantly...
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    • ...Using the last population to seed the new run improves the iGA performance in case of few network changes while a diversity technique, similar to the one used in [7], is used to avoid premature convergence...

    Mustafa Y. ElNainayet al. Channel allocation for dynamic spectrum access cognitive networks usin...

    • ...Several approaches have been designed to optimize genetic algorithms [2, 11, 12]...
    • ...Another approach is to reduce the search space by utilizing the sequence of points that have already been analyzed to guide the search [12]...
    • ...(c) We increase the mutation probability (usually about 1 in 1000) to 1 in 10 (one of the parameters to the algorithm) in order to maintain a level of diversity in the population as opposed to [12] where the authors reject points that are too close together...

    W. G. Osborneet al. Instrumented MultiStage Word-Length Optimization

    • ...Specifically, “Shrinking Window Mutation (SWM)” [11] and “Simulated Annealing (SA)” [12] procedures are used in the updatePopulation routine...

    Jun-young Kwaket al. Path Planning for Autonomous Information Collecting Vehicles

    • ...Several approaches havebeendesigned tooptimize genetic algorithms [2,11,12].In[11] theauthors systematically cross-over solutions bytrying all possible combinations toproduce better populations; theproblem isthat this can beslow...
    • ...Another approach istoreduce thesearch space byutilizing thesequence ofpoints that havealready been analyzed toguide thesearch [12]...
    • ...(c)We increase themutation probability (usually about1in1000) to1in10(oneoftheparameters tothealgorithm) inorder tomaintain alevel ofdiversity inthepopulation asopposed to[12] wheretheauthors reject points that aretooclose together...

    W. G. Osborneet al. Instrumented MultiStage Word-Length Optimization

    • ...Rasheed et al. propose to cluster data and to construct separate approximation models for the different clusters [30, 32, 29]...

    Laetitia Jourdanet al. Using datamining techniques to help metaheuristics: a short survey

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