Estimation of Distribution Algorithms (EDAs) are a set of
algorithms in the Evolutionary Computation (EC) field characterized
by the use of explicit probability distributions in optimization.
Contrarily to other EC techniques such as the broadly known Genetic
Algorithms (GAs) in EDAs, the crossover and mutation operators are
substituted by the sampling of a distribution previously learnt
from the selected individuals. EDAs have experienced a high
development that has transformed them into an established
discipline within the EC field.
This book attracts the interest of new researchers in the EC
field as well as in other optimization disciplines, and that it
becomes a reference for all of us working on this topic. The twelve
chapters of this book can be divided into those that endeavor to
set a sound theoretical basis for EDAs, those that broaden the
methodology of EDAs and finally those that have an applied
objective.
General
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