Genetic Algorithm Essentials / by Oliver Kramer.

Format:

Book

Author/Creator:

Kramer, Oliver, Author.

Series:

Studies in Computational Intelligence, 1860-949X ; 679

Language:

English

Subjects (All):

Computational intelligence.

Artificial intelligence.

Computational Intelligence.

Artificial Intelligence.

Local Subjects:

Computational Intelligence.

Artificial Intelligence.

Physical Description:

1 online resource (IX, 92 p. 38 illus. in color.)

Edition:

1st ed. 2017.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2017.

Summary:

This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.

Contents:

Part I: Foundations

Introduction

Genetic Algorithms

Parameters

Part II: Solution Spaces

Multimodality

Constraints

Multiple Objectives

Part III: Advanced Concepts

Theory

Machine Learning

Applications

Part IV: Ending

Summary and Outlook

Index

References.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

ISBN:

3-319-52156-X

OCLC:

969344131