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Springer Multi-Objective Memetic Algorithms - Springer AI Computing

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Description

Multi-Objective Memetic Algorithms is a comprehensive reference that explores the theory and practice of memetic algorithms for solving complex engineering and scientific problems with multiple conflicting objectives. This book from Springer's Studies in Computational Intelligence series reflects the current state-of-the-art in evolutionary computing, presenting memetic algorithms as a proven success story in sophisticated computational approaches. Designed for a wide readership, it covers the application of advanced evolutionary methods to real-world challenges across science and engineering disciplines. The work demonstrates how memetic algorithms have emerged as one of the most successful approaches within the growing field of multi-objective optimisation, offering invaluable insights for researchers, practitioners, and students working with computational intelligence and evolutionary problem-solving techniques.

Key Features

Multi-Objective Memetic Algorithms focuses on solving complex problems with multiple conflicting objectives using sophisticated evolutionary computing methods.

This Springer publication (volume 171 in Studies in Computational Intelligence) serves as a state-of-the-art reference for researchers in computational intelligence.

The book covers both theoretical foundations and practical applications of memetic algorithms across science and engineering disciplines.

Designed for a wide readership, this reference explores how memetic algorithms successfully address real-world optimisation challenges.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
28 October 2010
Listed Since
28 December 2010

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