Titlebook: Genetic and Evolutionary Computation — GECCO 2004; Genetic and Evolutio Kalyanmoy Deb Conference proceedings 2004 Springer-Verlag Berlin He

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Dynamic Uniform Scaling for Multiobjective Genetic Algorithms in the non-dominated solutions found by the MOEA to be biased towards one objective, thus resulting in a less diverse set of tradeoffs. In this paper, the issue of obtaining a diverse set of solutions for badly scaled objective functions will be investigated and the proposed solutions will be implemented using the NSGA-II algorithm.

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How Are We Doing? Predicting Evolutionary Algorithm Performanceial trials. We derive such probability estimates and test the derivations using a genetic algorithm for the traveling salesman problem. We find that while the analysis holds promise, it should probably not depend on the assumption that the distribution of an EA’s results is normal.

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Introduction of a New Selection Parameter in Genetic Algorithm for Constrained Reliability Design Prle space. These two properties illustrate that an adapted choice of the selection parameter value allows to improve the performance of GA. Furthermore, our numerical examples tend to show that, with an adapted choice of the selection parameter, these GAs are in practice more efficient than previously proposed GAs for this class of problems.

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Conference proceedings 2004ological applications; coevolution; evolutionary robotics; evolution strategies and evolutionary programming; evolvable hardware; genetic algorithms; genetic programming; learning classifier systems; real world applications; and search-based software engineering..

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Demystifying the Dementia Divide meaning about how strongly the problem is independently optimizable with a partition of variables. We present examples of the problem decomposition and the results of experiments that support the consistency of the measures.

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