Parameter Setting in Evolutionary Algorithms

@book{citeulike:2893205, abstract = {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.}, citeulike-article-id = {2893205}, editor = {Lobo, Fernando G. and Lima, Cl\'{a}udio F. and Michalewicz, Zbigniew }, keywords = {evolutionary-computation, metaheuristic, parameter-setting, performance-assessment}, posted-at = {2008-06-13 22:11:30}, priority = {0}, publisher = {Springer}, series = {Studies in Computational Intelligence}, title = {Parameter Setting in Evolutionary Algorithms}, volume = {54}, year = {2007} }

TY - BOOK ID - citeulike:2893205 L3 - citeulike-article-id:2893205 TI - Parameter Setting in Evolutionary Algorithms SE - Studies in Computational Intelligence VL - 54 PB - Springer N2 - 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. KW - evolutionary-computation KW - metaheuristic KW - parameter-setting KW - performance-assessment A2 - Lobo, Fernando A2 - Lima, Cláudio A2 - Michalewicz, Zbigniew PY - 2007/// ER -