Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods
نویسندگان
چکیده
Occasionally there have been long debates on whether to use elitist selection or not. In the present paper the simple (1,λ) EA and (1+λ) EA operating on {0, 1}n are compared by means of a rigorous runtime analysis. It turns out that only values for λ that are logarithmic in n are interesting. An illustrative function is presented for which newly developed proof methods show that the (1,λ) EA—where λ is logarithmic in n—outperforms the (1+λ) EA for any λ. For smaller offspring populations the (1,λ) EA is inefficient on every function with a unique optimum, whereas for larger λ the two randomized search heuristics behave almost equivalently.
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Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Coevolution for Classification
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UNIVERSITY OF DORTMUND REIHE COMPUTATIONAL INTELLIGENCE COLLABORATIVE RESEARCH CENTER 531 Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods A Uni ed Model of Non-Panmictic Population Structures in Evolutionary Algorithms
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Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Analysis of a Simple Evolutionary Algorithm for the Minimization in Euclidian Spaces
and was printed with financial support of the Deutsche Forschungsgemeinschaft.
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and was printed with financial support of the Deutsche Forschungsgemeinschaft.
متن کاملDesign and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods a Uniied Model of Non-panmictic Population Structures in Evolutionary Algorithms
This work is a product of the Collaborative Research Center 531, \Computational Intelligence", at the University of Dortmund and was printed with nancial support of the Deutsche Forschungsgemeinschaft.
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Often the Pareto front of a multi-objective optimization problem grows exponentially with the problem size. In this case, it is not possible to compute the whole Pareto front efficiently and one is interested in good approximations. We consider how evolutionary algorithms can achieve such approximations by using different diversity mechanisms. We discuss some well-known approaches such as the d...
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