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Springer Representations for Genetic and Evolutionary Algorithms - Springer

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Description

This comprehensive book examines how problem representations fundamentally influence the performance of genetic and evolutionary algorithms (GEAs). Rather than treating representation as a secondary concern, the author provides in-depth analysis of representation theory and its practical impact on algorithm effectiveness. The work synthesizes existing knowledge about problem representations and explores key properties including redundancy, scaling, and locality, demonstrating how these factors directly affect GEA performance and other heuristic optimisation methods. Essential reading for researchers and practitioners seeking to understand the critical relationship between representation design and algorithmic success in evolutionary computing.

Key Features

Comprehensive overview of problem representations and their influence on genetic and evolutionary algorithms performance.

Detailed analysis of fundamental representation properties including redundancy, scaling, and locality effects.

Theoretical framework and empirical evidence connecting representation design to GEA optimisation outcomes.

Insights applicable to heuristic optimisation methods beyond genetic and evolutionary algorithms.

Addresses the traditionally overlooked role of problem representation in algorithm design and implementation.

Product Specifications

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

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