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Springer Scalable Optimization via Probabilistic Modeling - EDA Algorithms Book

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Description

This comprehensive volume from Springer explores estimation of distribution algorithms (EDAs), a transformative technique revolutionizing evolutionary computation and optimization. Part of the Studies in Computational Intelligence series (volume 33), the book examines how probabilistic modelling drives breakthroughs in genetic algorithms and broader optimisation challenges. The text synthesises current research whilst charting future directions in the field, making it an essential resource for researchers, practitioners and students working with evolutionary computation, machine learning and advanced optimisation methods. The book provides both theoretical foundations and practical applications, bridging algorithms and real-world implementations.

Key Features

Focuses on estimation of distribution algorithms (EDAs), a leading technique in evolutionary computation and optimisation.

Part of the Springer Studies in Computational Intelligence series, volume 33, establishing credibility within the academic field.

Covers both foundational theory and practical applications of probabilistic modelling for scalable optimisation.

Synthesises the state of the art whilst pointing towards emerging research directions in genetic and evolutionary computation.

Provides essential reference material for researchers working in machine learning, artificial intelligence and advanced optimisation.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
30 November 2010
Listed Since
20 September 2010

Barcode

No barcode data available

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