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Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
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Michael Affenzeller (Upper Austria University of Applied Sciences, Hagenberg, and Johannes Kepler University of Linz, Austria) Stefan Wagner (Upper Austria University of Applied Sciences, Hagenberg)
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications by Michael Affenzeller (Upper Austria University of Applied Sciences, Hagenberg, and Johannes Kepler University of Linz, Austria) at Abbey's Bookshop,

Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications

Michael Affenzeller (Upper Austria University of Applied Sciences, Hagenberg, and Johannes Kepler University of Linz, Austria) Stefan Wagner (Upper Austria University of Applied Sciences, Hagenberg) Stephan Winkler (Upper Austria University of Applied Sciences, Hagenberg) Andreas Beham (Upper Austria University of Applied Sciences, Hagenberg)


9781138114272

CRC Press


Artificial intelligence


Paperback

379 pages

$126.00
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Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes structure identification using HeuristicLab as a platform for algorithm development.

The book focuses on both theoretical and empirical aspects. The theoretical sections explore the important and characteristic properties of the basic GA as well as main characteristics of the selected algorithmic extensions developed by the authors. In the empirical parts of the text, the authors apply GAs to two combinatorial optimization problems: the traveling salesman and capacitated vehicle routing problems. To highlight the properties of the algorithmic measures in the field of GP, they analyze GP-based nonlinear structure identification applied to time series and classification problems.

Written by core members of the HeuristicLab team, this book provides a better understanding of the basic workflow of GAs and GP, encouraging readers to establish new bionic, problem-independent theoretical concepts. By comparing the results of standard GA and GP implementation with several algorithmic extensions, it also shows how to substantially increase achievable solution quality.

By:   Michael Affenzeller (Upper Austria University of Applied Sciences Hagenberg and Johannes Kepler University of Linz Austria), Stefan Wagner (Upper Austria University of Applied Sciences, Hagenberg), Stephan Winkler (Upper Austria University of Applied Sciences, Hagenberg), Andreas Beham (Upper Austria University of Applied Sciences, Hagenberg)
Imprint:   CRC Press
Country of Publication:   United Kingdom
Dimensions:   Height: 235mm,  Width: 156mm, 
Weight:   739g
ISBN:   9781138114272
ISBN 10:   1138114278
Series:   Numerical Insights
Pages:   379
Publication Date:   April 2018
Audience:   Professional and scholarly ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active

Introduction Simulating Evolution: Basics about Genetic Algorithms The Evolution of Evolutionary Computation The Basics of Genetic Algorithms (GAs) Biological Terminology Genetic Operators Problem Representation GA Theory: Schemata and Building Blocks Parallel Genetic Algorithms The Interplay of Genetic Operators Bibliographic Remarks Evolving Programs: Genetic Programming Introduction: Main Ideas and Historical Background Chromosome Representation Basic Steps of the Genetic Programming (GP)-Based Problem Solving Process Typical Applications of GP GP Schema Theories Current GP Challenges and Research Areas Conclusion Bibliographic Remarks Problems and Success Factors What Makes GAs and GP Unique Among Intelligent Optimization Methods? Stagnation and Premature Convergence Preservation of Relevant Building Blocks What Can Extended Selection Concepts Do to Avoid Premature Convergence? Offspring Selection (OS) The Relevant Alleles Preserving Genetic Algorithm (RAPGA) Consequences Arising out of Offspring Selection and RAPGA SASEGASA-More Than the Sum of All Parts The Interplay of Distributed Search and Systematic Recovery of Essential Genetic Information Migration Revisited SASEGASA: A Novel and Self-Adaptive Parallel Genetic Algorithm Interactions between Genetic Drift, Migration, and Self-Adaptive Selection Pressure Analysis of Population Dynamics Parent Analysis Genetic Diversity Characteristics of Offspring Selection and the RAPGA Introduction Building Block Analysis for Standard GAs Building Block Analysis for GAs Using Offspring Selection Building Block Analysis for the RAPGA Combinatorial Optimization: Route Planning The Traveling Salesman Problem The Capacitated Vehicle Routing Problem Evolutionary System Identification Data-Based Modeling and System Identification GP-Based System Identification in HeuristicLab Local Adaption Embedded in Global Optimization Similarity Measures for Solution Candidates Applications of Genetic Algorithms: Combinatorial Optimization The Traveling Salesman Problem Capacitated Vehicle Routing Data-Based Modeling with Genetic Programming Time Series Analysis Classification Genetic Propagation Single Population Diversity Analysis Multi-Population Diversity Analysis Code Bloat, Pruning, and Population Diversity Conclusion and Outlook Symbols and Abbreviations References Index

Michael Affenzeller is Professor for Applied Computer Science at the Department of Software Engineering of the Upper Austrian University of Applied Sciences in Hagenberg, Austria, as well as head of the Hueristic and Evolutionary Algorithms Laboratory research group. His interests include Heuristic Algorithms, Evolutionary Algorithms, Algorithm Theory and Development, Production Planning and Logistics Optimization, Nonlinear Systems Identification, Structure Identification, Regression and Time Series, Heuristic Optimization Techniques in Bioinformatics / Chemoinformatics. Stefan Wagner is an associate professor of strategy (with tenure). Stefan joined ESMT Berlin in February 2011 as an assistant professor and was the TUSIAD/TCCI Chair in European Economic Integration from 2014 to 2015. Previously he was an assistant professor in the Institute of Innovation Research, Technology Management, and Entrepreneurship (INNO-tec) at the Ludwig Maximilian University of Munich, Germany. Stefan received his Habilitation in 2010 and his Doctorate in Management (summa cum laude) in 2005 from LMU. Stefan's research interests cover the intersection of firm strategy, technological innovation, industrial organization and law. Currently, he is primarily interested in the interaction of the changing landscape of intellectual property rights (in particular patent systems) and firms' long term strategy regarding their innovative activities. From a more practical perspective, he is also interested in venture creation and growth strategies for young firms. Stephan Winkler is head of the Bioinformatics Research Group at University of Applied Sciences, Upper Austria. His research interests include Theory and Application of Genetic Algorithms and Genetic Programming, Machine Learning and Data Mining, Bioinformatics, Grey Box Identification of Nonlinear Systems (especially in the context of mechatronic applications), Software Development. Andreas Beham is a senior researcher at the School of Informatics, Communications and Media at the University of Applied Sciences, Upper Austria. His research interests include the development and analysis of solution methods and their application to real-world relevant problems. In particular, he is interested in developing a guidance system for choosing and parameterizing metaheuristic methods.

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