£84.86

Springer - Towards a New Evolutionary Computation Book

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Description

Explore the evolving landscape of Evolutionary Computation with this specialized text from Springer. This volume focuses on Estimation of Distribution Algorithms (EDAs), a significant set of algorithms within the field that utilize explicit probability distributions for optimization. Unlike traditional techniques like Genetic Algorithms (GAs) that rely on crossover and mutation, EDAs use sampling from a distribution learned from selected individuals to drive the optimization process. As EDAs have matured into an established discipline, this book provides essential insights for those studying the rapid development of these methods. It serves as a valuable resource for new researchers looking to understand how probability distributions transform optimization tasks. Whether you are studying computer science, AI, or machine learning, this book offers a deep look into the methodologies that define modern estimation of distribution techniques.

Key Features

Focuses on Estimation of Distribution Algorithms (EDAs) and their role in the Evolutionary Computation field.

Explains the transition from traditional Genetic Algorithms to distribution-based sampling methods.

Provides technical insights into how explicit probability distributions are used in optimization.

Serves as a foundational resource for new researchers entering the field of Evolutionary Computation.

Part of the Studies in Fuzziness and Soft Computing series (Volume 192) from Springer.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
12 January 2006
Listed Since
31 December 2006

Barcode

No barcode data available

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