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New frontier in evolutionary algorithms [electronic resource] : theory and applications / Hitoshi Iba, Nasimul Noman.

By: Iba, Hitoshi.
Contributor(s): Noman, Nasimul.
Material type: materialTypeLabelBookPublisher: Singapore : Imperial College Press, [2019], c2012Description: 1 online resource (xii, 304 p.).ISBN: 9781848166820; 1848166826.Other title: New frontiers in evolutionary algorithms.Subject(s): Algorithms | Genetic algorithms | Evolutionary computationGenre/Form: Electronic books.DDC classification: 005.73 Online resources: Access to full text is restricted to subscribers.
Contents:
A practical guide to genetic algorithms using Excel simulators -- Real-valued GA and its variants -- Theoretical background of GA search performance -- The memetic computing approach -- Real-world applications of evolutionary algorithms.
Summary: "This book delivers theoretical and practical knowledge of Genetic Algorithms (GA) for the purpose of practical applications. It provides a methodology for a GA-based search strategy with the integration of several Artificial Life and Artificial Intelligence techniques, such as memetic concepts, swarm intelligence, and foraging strategies. The development of such tools contributes to better optimizing methodologies when addressing tasks from areas such as robotics, financial forecasting, and data mining in bioinformatics."--Publisher's website.
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Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

"This book delivers theoretical and practical knowledge of Genetic Algorithms (GA) for the purpose of practical applications. It provides a methodology for a GA-based search strategy with the integration of several Artificial Life and Artificial Intelligence techniques, such as memetic concepts, swarm intelligence, and foraging strategies. The development of such tools contributes to better optimizing methodologies when addressing tasks from areas such as robotics, financial forecasting, and data mining in bioinformatics."--Publisher's website.

Includes bibliographical references (p. 285-296) and index.

A practical guide to genetic algorithms using Excel simulators -- Real-valued GA and its variants -- Theoretical background of GA search performance -- The memetic computing approach -- Real-world applications of evolutionary algorithms.

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