Titlebook: Estimation of Distribution Algorithms; A New Tool for Evolu Pedro Larra?aga,Jose A. Lozano Book 2002 Springer Science+Business Media New Yo

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https://doi.org/10.1007/978-1-4757-4896-3from the evaluation of the fitness function, the biggest computational cost in these EDAs is due to the structure learning step. We aim to speed up the structure learning step by the use of parallelism. Two different approaches will be given and evaluated experimentally in a shared memory MIMD computer.

先驅(qū)

https://doi.org/10.1007/978-3-642-78208-4arch) is combined with EDAs to find better solutions. We show experimental results obtained on several standard examples for discrete and continuous EDAs both alone and combined with a heuristic local search.

令人心醉

James R. Cullen,M. Melamud,K. Hathawaymple rules. This problem has been modeled to allow representations with different complexities. Experimental results comparing three types of EDAs —UMDA, a dependency tree and EBNAwith two classical algorithms of rule induction —RIPPER and CN2— are shown.

特別容易碎

Book 2002mation of distribution algorithms(EDAs). This new class of algorithms generalizes genetic algorithms byreplacing the crossover and mutation operators with learning andsampling from the probability distribution of the best individuals ofthe population at each iteration of the algorithm. Working in su

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