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One of the main difficulties of applying an evolutionary algorithm
(or, as a matter of fact, any heuristic method) to a given problem
is to decide on an appropriate set of parameter values. Typically
these are specified before the algorithm is run and include
population size, selection rate, operator probabilities, not to
mention the representation and the operators themselves. This book
gives the reader a solid perspective on the different approaches
that have been proposed to automate control of these parameters as
well as understanding their interactions. The book covers a broad
area of evolutionary computation, including genetic algorithms,
evolution strategies, genetic programming, estimation of
distribution algorithms, and also discusses the issues of specific
parameters used in parallel implementations, multi-objective
evolutionary algorithms, and practical consideration for real-world
applications. It is a recommended read for researchers and
practitioners of evolutionary computation and heuristic
methods.
One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.
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