Studies in Computational Intelligence

By: Oliver Kramer

Write A Review

Hardcover | 19 August 2008

At a Glance

Hardcover
196 Pages

Dimensions(cm)
24.13 x 15.88 x 1.91

Hardcover

$169.00

or 4 interest-free payments of $42.25 with

Ships in 5 to 7 business days

Evolutionary algorithms are successful biologically inspired meta-heuristics. Their success depends on adequate parameter settings. The question arises: how can evolutionary algorithms learn parameters automatically during the optimization? Evolution strategies gave an answer decades ago: self-adaptation. Their self-adaptive mutation control turned out to be exceptionally successful. But nevertheless self-adaptation has not achieved the attention it deserves.

This book introduces various types of self-adaptive parameters for evolutionary computation. Biased mutation for evolution strategies is useful for constrained search spaces. Self-adaptive inversion mutation accelerates the search on combinatorial TSP-like problems. After the analysis of self-adaptive crossover operators the book concentrates on premature convergence of self-adaptive mutation control at the constraint boundary. Besides extensive experiments, statistical tests and some theoretical investigations enrich the analysis of the proposed concepts.

Shipping

	Standard Shipping	Express Shipping
Metro postcodes:	$9.99	$14.95
Regional postcodes:	$9.99	$14.95
Rural postcodes:	$9.99	$14.95

How to return your order

At Booktopia, we offer hassle-free returns in accordance with our returns policy. If you wish to return an item, please get in touch with Booktopia Customer Care.

Additional postage charges may be applicable.

Defective items

If there is a problem with any of the items received for your order then the Booktopia Customer Care team is ready to assist you.

For more info please visit our Help Centre.

You Can Find This Book In

Non-Fiction Computing & I.T.Computer Programming & Software Development Programming & Scripting Languages Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Mathematics Applied Mathematics Graphical & Digital Media Applications Computer-Aided Design CAD Engineering & Technology

Technology in General Maths for Engineers Engineering in General

Introduction	p. 1
Motivation	p. 1
A Survey of This Book	p. 2
Foundations of Evolutionary Computation
Evolutionary Algorithms	p. 9
Introduction to Evolutionary Computation	p. 9
Computational Intelligence	p. 9
Optimization Problems and Stochastic Convergence	p. 11
Classic Optimization Methods	p. 12
A Short Excursus to Molecular Biology and Genetics	p. 13
Concepts of the Evolutionary Computation Framework	p. 13
Types of Evolutionary Algorithms	p. 17
Evolution Strategies	p. 21
The ([mu]/[rho superscript +], [lambda])-ES	p. 21
The ([mu], [kappa], [lambda], [rho])-ES	p. 22
Practical Guidelines for Evolutionary Algorithms	p. 22
Theories of Evolutionary Algorithms	p. 24
Summary	p. 27
Self-Adaptation	p. 29
History of Parameter Adaptation	p. 29
An Extended Taxonomy of Parameter Setting Techniques	p. 30
Preliminary Work	p. 30
Parameter Tuning	p. 31
Parameter Control	p. 32
Typical Adapted Parameters	p. 35
The Concept of Self-Adaptation	p. 37
Self-Adaptation of Global Parameters	p. 40
Theoretical Approaches Toward Self-Adaptation	p. 40
A Self-Adaptive Estimation of Distribution View on EAs	p. 42
Preliminary Views on Self-Adaptation	p. 42
Estimation of Distribution Algorithms	p. 43
Self-Adaptation: Evolving the Estimation of Distribution	p. 43
Views on the Proposed Operators and Problems of This Work	p. 44
Premature Convergence	p. 45
Summary	p. 46
Self-Adaptive Operators
Biased Mutation for Evolution Strategies	p. 51
Mutation Operators for Evolution Strategies	p. 51
Uncorrelated Isotropic Gaussian Mutation with One Step Size	p. 53
Uncorrelated Gaussian Mutation with N Step Sizes	p. 54
Correlated Mutation	p. 54
Asymmetric Density Functions - Directed Mutation	p. 55
Cauchy Mutation	p. 56
Covariance Matrix Adaptation (CMA)	p. 56
Self-Adaptive Mutation for Binary Representations	p. 58
Mutation Operators for Strategy Parameters	p. 58
The Biased Mutation Operator	p. 58
BMO Concept	p. 59
Sphere Biased Mutation Operator (sBMO)	p. 60
Cube Biased Mutation Operator (cBMO)	p. 61
Comparison of Computational Effort of the Randomized Operators	p. 61
The Descent Direction Mutation Operator (DMO)	p. 62
Success Rate on Monotone Functions	p. 63
Experimental Analysis	p. 64
Unconstrained Real Parameter Optimization	p. 65
Climbing Ridges with Biased Mutation	p. 71
Handling Constraints with Biased Mutation	p. 74
Excursus: Self-Adaptive Mutation Operator Selection	p. 79
Summary	p. 80
Self-Adaptive Inversion Mutation	p. 81
Introduction	p. 81
The Traveling Salesman Problem	p. 81
Evolutionary Combinatorial Optimization	p. 82
Self-Adaptation for Discrete Strategy Variables	p. 82
Self-Adaptive Inversion Mutation	p. 83
Convergence Properties	p. 84
Experimental Analysis	p. 86
TSP Instance Berlin52	p. 87
TSP Instance Bier127	p. 88
TSP Instance Gr666	p. 89
Small TSP Instances	p. 92
Statistical Test	p. 92
The Strategy Bound Problem and SA-INV-c	p. 92
Summary	p. 94
Self-Adaptive Crossover	p. 97
The Self-Adaptive Crossover Concept	p. 97
The Role of Crossover - Building Blocks or Genetic Repair?	p. 98
Preliminary Work	p. 99
Adaptation of the Crossover Structure	p. 99
Self-Adaptive n-Point Crossover	p. 101
SA-1-Point	p. 101
SA-n-Point	p. 103
Self-Adaptive Uniform and Multi Parent Crossover	p. 103
Self-Adaptive Partially Mapped Crossover	p. 105
Self-Adaptive Recombination for Evolution Strategies (SAR)	p. 107
Intermediate and Dominant Recombination	p. 108
Self-Adaptive Recombination	p. 108
SAR Variants	p. 109
Experimental Analysis	p. 109
Crossover Point Optimization	p. 111
Summary	p. 112
Constraint Handling
Constraint Handling Heuristics for Evolution Strategies	p. 117
The NLP Problem	p. 117
A Short Survey of Constraint-Handling Methods	p. 118
Penalty Functions	p. 118
Penalty-Related Methods	p. 119
Repair Algorithms	p. 119
Decoder Functions	p. 119
Multiobjective Optimization	p. 120
Constraint Preserving Operators and Representations	p. 120
Recent Developments	p. 120
Premature Step Size Reduction	p. 120
Experimental Analysis	p. 121
Theoretical Analysis	p. 121
The Death Penalty Step Control Approach	p. 127
Basic Minimum Step Size Reduction Mechanism	p. 127
Experimental Analysis	p. 128
Constraint-Handling with Two Sexes	p. 131
Biologically Inspired Constraint-Handling	p. 131
Modifications of the Basic Two Sexes Evolution Strategy	p. 132
Experimental Analysis	p. 133
The Nested Angle Evolution Strategy	p. 137
Meta-evolution for Mutation Ellipsoid Rotation	p. 137
Experimental Analysis	p. 138
Outlook: Covariance Matrix Adaptation by Feasibility Classification	p. 139
Summary	p. 139
Summary
Summary and Conclusion	p. 143
Contributions of This Book	p. 143
Conclusion	p. 146
Appendix
Continuous Benchmark Functions	p. 149
Unimodal Numerical Functions	p. 149
Multimodal Numerical Functions	p. 150
Ridge Functions	p. 152
Numerical Functions in Bit String Representations	p. 153
Constrained Numerical Functions	p. 154
Discrete Benchmark Functions	p. 159
Traveling Salesman Problems	p. 159
SAT-Problem	p. 161
References	p. 163
List of Figures	p. 173
List of Tables	p. 175
Index	p. 179
Table of Contents provided by Ingram. All Rights Reserved.

Studies in Computational Intelligence

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

More in Artificial Intelligence

Co-Existence

The Next Phase of AI

AGI Chronicles

The Inside Story of the Race to Create an Artificial Superintelligence

God AI

Boom or Doom? What to Expect When Machines Outsmart Us

HBR's 10 Must Reads 2027

The Definitive Management Ideas of the Year from Harvard Business Review (featuring 'The Hidden Beliefs That Hold Leaders Back' by Muriel M. Wilkins)

The Reverse Centaur's Guide to Life After AI

How to Think About Artificial Intelligence Before It's Too Late

Empire of AI

Inside the reckless race for total domination

Nexus

A Brief History of Information Networks from the Stone Age to AI

The AI Con

How To Fight Big Tech's Hype and Create the Future We Want

The Year in Tech, 2027

The Insights You Need from Harvard Business Review

The New Age of Sexism

How the AI Revolution is Reinventing Misogyny

AI at Work

How to Build a Full Stack Organization

The Coming Wave

AI, Power and the 21st Century's Greatest Dilemma

AI-Driven Security

Navigating Threats and Defenses in a New Era

Green Bytes

Mitigating the Environmental Impact of Digital Technologies

AI for Public Relations

A How-To Guide for Implementation and Management

Rational Investing

Perspectives from Behavioral Finance, Neuroeconomics and AI

Claude For Dummies

Securing the Internet of Things

Machine Learning and Beyond

Artificial Intelligence in Modeling and Simulation

Simulation Foundations, Methods and Applications

Digital Twin Pose Detection for Mechanical Equipment

Theory and Methods for Intelligent Multi-Source Pose Deduction and Reconstruction of Fully-Mechanized Mining Equipment Clusters

Mathematics of Uncertainty Applied to Global Issues

Sustainability, Mistreatment of Women, Nuclear Power, Smart Cities, Digitalization

Co-Intelligence

Living and Working with AI

The Infinity Machine

Demis Hassabis, DeepMind and the Quest for Superintelligence

Evals for AI Engineers

Systematically Measuring and Improving AI Applications

This product is categorised by