Parameter Setting in Evolutionary Algorithms
Contributor(s): Lobo, Fernando G [editor.1] | Lima, Cl�udio F [editor.1 ] | Michalewicz, Zbigniew [editor.2 ] | SpringerLink (Online service)0.
Material type:
BookSeries: Studies in Computational Intelligence, 540.Berlin, Heidelberg : Springer Berlin Heidelberg, 2007. Description: XII, 318 p. 100 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540694328.Subject(s): Engineering | Artificial intelligence | Applied mathematics | Engineering mathematics.1 | Engineering.2 | Appl.Mathematics/Computational Methods of Engineering.2 | Artificial Intelligence (incl. Robotics).1DDC classification: 519 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
| PK Kelkar Library, IIT Kanpur | Available | EBK10077 |
Parameter Setting in EAs: a 30 Year Perspective -- Parameter Control in Evolutionary Algorithms -- Self-Adaptation in Evolutionary Algorithms -- Adaptive Strategies for Operator Allocation -- Sequential Parameter Optimization Applied to Self-Adaptation for Binary-Coded Evolutionary Algorithms -- Combining Meta-EAs and Racing for Difficult EA Parameter Tuning Tasks -- Genetic Programming: Parametric Analysis of Structure Altering Mutation Techniques -- Parameter Sweeps for Exploring Parameter Spaces of Genetic and Evolutionary Algorithms -- Adaptive Population Sizing Schemes in Genetic Algorithms -- Population Sizing to Go: Online Adaptation Using Noise and Substructural Measurements -- Parameter-less Hierarchical Bayesian Optimization Algorithm -- Evolutionary Multi-Objective Optimization Without Additional Parameters -- Parameter Setting in Parallel Genetic Algorithms -- Parameter Control in Practice -- Parameter Adaptation for GP Forecasting Applications.
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.
There are no comments for this item.