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Linkage in Evolutionary Computation

Linkage in Evolutionary Computation

Ying-ping Chen

328,45 €
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Editorial:
Springer Nature B.V.
Año de edición:
2010
Materia
Programación informática/desarrollo de software
ISBN:
9783642098765
328,45 €
IVA incluido
Disponible

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In recent years, the issue of linkage in GEAs has garnered greater attention and recognition from researchers. Conventional approaches that rely much on ad hoc tweaking of parameters to control the search by balancing the level of exploitation and exploration are grossly inadequate. As shown in the work reported here, such parameters tweaking based approaches have their limits; they can be easily 'fooled' by cases of triviality or peculiarity of the class of problems that the algorithms are designed to handle. Furthermore, these approaches are usually blind to the interactions between the decision variables, thereby disrupting the partial solutions that are being built up along the way.

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    Genetic algorithms (GAs) are powerful search techniques based on principles of evolution and widely applied to solve problems in many disciplines. However, most GAs employed in practice nowadays are unable to learn genetic linkage and suffer from the linkage problem. The linkage learning genetic algorithm (LLGA) was proposed to tackle the linkage prob...
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