Evolutionary Search and the Job Shop

Evolutionary Search and the Job Shop
Title Evolutionary Search and the Job Shop PDF eBook
Author Dirk C. Mattfeld
Publisher Springer Science & Business Media
Pages 162
Release 2013-04-17
Genre Business & Economics
ISBN 3662117126

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Production scheduling dictates highly constrained mathematical models with complex and often contradicting objectives. Evolutionary algorithms can be formulated almost independently of the detailed shaping of the problems under consideration. As one would expect, a weak formulation of the problem in the algorithm comes along with a quite inefficient search. This book discusses the suitability of genetic algorithms for production scheduling and presents an approach which produces results comparable with those of more tailored optimization techniques.

Intelligent and Evolutionary Systems

Intelligent and Evolutionary Systems
Title Intelligent and Evolutionary Systems PDF eBook
Author Mitsuo Gen
Publisher Springer Science & Business Media
Pages 218
Release 2009-03-12
Genre Computers
ISBN 3540959777

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This book offers fourteen select papers presented at the recent Asia-Pacific Symposia on Intelligent and Evolutionary Systems. They illustrate the breadth of research in the field with applications ranging from business to medicine to network optimization.

Evolutionary Optimization Algorithms

Evolutionary Optimization Algorithms
Title Evolutionary Optimization Algorithms PDF eBook
Author Dan Simon
Publisher John Wiley & Sons
Pages 776
Release 2013-06-13
Genre Mathematics
ISBN 1118659503

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A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear but theoretically rigorous understanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAs including opposition-based learning, artificial fish swarms, bacterial foraging, and many others and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the author's website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.

Evolutionary Algorithms

Evolutionary Algorithms
Title Evolutionary Algorithms PDF eBook
Author Alain Petrowski
Publisher John Wiley & Sons
Pages 256
Release 2017-04-24
Genre Computers
ISBN 1848218044

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Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms. Chapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning.

Handbook of Genetic Algorithms

Handbook of Genetic Algorithms
Title Handbook of Genetic Algorithms PDF eBook
Author Lawrence Davis
Publisher Van Nostrand Reinhold Company
Pages 406
Release 1991
Genre Mathematics
ISBN

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Evolutionary Computation in Combinatorial Optimization

Evolutionary Computation in Combinatorial Optimization
Title Evolutionary Computation in Combinatorial Optimization PDF eBook
Author Arnaud Liefooghe
Publisher Springer
Pages 231
Release 2019-04-10
Genre Computers
ISBN 3030167119

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This book constitutes the refereed proceedings of the 19th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2019, held as part of Evo* 2019, in Leipzig, Germany, in April 2019, co-located with the Evo* 2019 events EuroGP, EvoMUSART and EvoApplications. The 14 revised full papers presented were carefully reviewed and selected from 37 submissions. The papers cover a wide spectrum of topics, ranging from the foundations of evolutionary computation algorithms and other search heuristics to their accurate design and application to both single- and multi-objective combinatorial optimization problems. Fundamental and methodological aspects deal with runtime analysis, the structural properties of fitness landscapes, the study of metaheuristics core components, the clever design of their search principles, and their careful selection and configuration. Applications cover domains such as scheduling, routing, partitioning and general graph problems.

Introduction to Evolutionary Computing

Introduction to Evolutionary Computing
Title Introduction to Evolutionary Computing PDF eBook
Author A.E. Eiben
Publisher Springer Science & Business Media
Pages 328
Release 2007-08-06
Genre Computers
ISBN 9783540401841

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The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.