£107.43

Springer - Representations for Genetic and Evolutionary Algorithms

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Description

In the study of genetic and evolutionary algorithms (GEAs), researchers often focus on operators and test problems while treating problem representation as a given. This book changes that approach by providing a deep look into how representation affects performance. This text offers a comprehensive overview of the influence that different problem representations have on GEA outcomes. It summarizes existing knowledge and explains how specific properties of a representation can change the way heuristic optimization methods work. By understanding these mechanics, readers can better grasp why certain algorithms succeed or fail based on how the problem is structured. The book covers essential properties such as redundancy, scaling, and locality. These elements are fundamental to understanding how representations impact the efficiency and success of GEAs and other heuristic optimization methods. It is an essential resource for those looking to move beyond standard practices and master the underlying mechanics of evolutionary computation.

Key Features

Provides a comprehensive overview of how problem representations influence the performance of genetic and evolutionary algorithms.

Summarizes existing scientific knowledge regarding various problem representations used in the field.

Explains how basic properties like redundancy, scaling, and locality affect GEA outcomes.

Offers a detailed look at the relationship between representation and heuristic optimization methods.

Breaks from traditional research focus on operators and test problems to prioritize representation study.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
14 October 2010
Listed Since
20 September 2010

Barcode

No barcode data available

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