Titlebook: Recent Advances in Memetic Algorithms; William E. Hart,J. E. Smith,N. Krasnogor Book 2005 Springer-Verlag Berlin Heidelberg 2005 algorithm

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性上瘾

语言学

-Fitness Landscapes and Memetic Algorithms with Greedy Operators and ,-opt Local Searchroblems, including the traveling salesman problem and the graph bipartitioning problem. In this contribution, a .-opt local search heuristic and a greedy heuristic for .-landscapes are proposed for use in memetic algorithms. The latter is used for the initialization of the population and in a greedy

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纤细

Designing Efficient Genetic and Evolutionary Algorithm Hybridsns use GEAs in combination with domain specific methods to achieve superior performance. Such combinations, often referred to as hybrids, stand to gain much from a system-level framework for efficiently combining global searchers such as GEAs with domain-specific and local searchers. This chapter pr

vitreous-humor

The Design of Memetic Algorithms for Scheduling and Timetabling Problemse often highly constrained, they require sophisticated solution representation schemes, and they usually require very time-consuming fitness evaluation routines. There is a considerable number of memetic algorithms that have been proposed in the literature to solve scheduling and timetabling problem

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宣誓书

Using Memetic Algorithms for Optimal Calibration of Automotive Internal Combustion Enginesntrol units is optimized. It is shown that in all cases MAs that work on locally optimal solutions calculated by the corresponding HCs significantly improve former results using Genetic Algorithms (GAs). The algorithms have been successfully applied at BMW Group Munich.

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-Fitness Landscapes and Memetic Algorithms with Greedy Operators and ,-opt Local Searchbination operator, are compared on three types of .-landscapes. In accordance with the landscape analysis, the MAs with recombination perform better than the MAs with mutation for landscapes with low epistasis. Moreover, the MAs are shown to be superior to previously proposed MAs using 1-opt local search.