Practical Handbook of Genetic Algorithms, Volume I

With a general description and contents of the forthcoming Volume II

Editor - Lance D. Chambers

Department of Transport, Nedlands, Western Australia


Description | Features | Contents - VI | Contents - VII | Price and Publication | Other Titles


Description

The mathematics employed by genetic algorithms (Gas) are among the most exciting discoveries of the last few decades. But what exactly is a genetic algorithm? A genetic algorithm is a problem-solving method that uses genetics as its model of problem solving. It applies the rules of reproduction, gene crossover, and mutation to pseudo-organisms so those "organisms" can pass beneficial and survival-enhancing traits to new generations. GAs are useful in the selection of parameters to optimize a system's performance. A second potential use lies in testing and fitting quantitative models. Unlike any other book available, this interesting new text/reference takes you from the construction of a simple GA to advanced implementations. As you come to understand GAs and their processes, you will begin to understand the power of the genetic-based problem-solving paradigms that lie behind them.

The Practical Handbook of Genetic Algorithms presents for the first time new areas of research and implementation. Problems that for many have been considered intractable are shown to be solvable using the techniques described in this work. Specific solution descriptions to real-world problems are provided, or use these as examples to develop solutions to unique problems.

Volume 1 offers extensive code lists in a number of languages-C++, Modl, QuickBasic, C, LISP, and many more. Because the book contains compiled knowledge from respected international experts, you gain confidence in the efficacy of the applications and code examples. An accompanying diskette is filled with codes that are ready to cut and paste, ready-to-run applications, and detailed descriptions of how each code can be implemented.

The book does more than just describe GAs. Almost two hundred figures and numerous tables show how they should look and how they work. This first volume of the Practical Handbook of Genetic Algorithms (GAs) and the included software present the most comprehensive selection of hybrid methods for designing efficient and effective solutions for even the most highly complex problems.

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Features

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Contents

Volume I

Quick cross-reference - just select topic of interest:

Model Building, Model Testing, and Model Fitting

Genie: A First GA

Niche and Species Formation in Genetic Algorithms

Construction of Neural Networks

Crossover Operators

The Boltzmann Selection Procedure

Optimal State Space Representation via Evolutionar y Algorithms: Supporting Expensive Fitness Functions

Using LibGA to Develop Genetic Algorithms for Solving Comb inatorial Optimization Problems

Strategic Modeling Using a Genetic Algorithm Appro ach

Evolving Timetables

Applications of Genetic Algorithms in Chemical En gineering

Structure and Performance of Fine-Grain Parallelism in Genetic Search

Locating Putative Protein Signal Sequences

Selection Methods for Evolutionary Algorithms

Parallel Cooperating Genetic Algorithms: An Applicatio n to Robot Motion Planning

Algorithms for Multidimensional Scaling

How to Apply Genetic Algorithms to Constrained Probl ems

Genetic Algorithms for Routing and Scheduling Problem s

Beneficial Effect of Intentional Noise in the Gene tic Algorithm

Evolving Behavior in Repeated 2-Player Games

Artificial Neural Network Evolution: Learning to Steer a Land Vehicle

Appendixes


Volume I - Full Contents

Model Building, Model Testing, and Model Fitting

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Genie: A First GA

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Niche and Species Formation in Genetic Algorithms

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Construction of Neural Networks

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Crossover Operators

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The Boltzmann Selection Procedure

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Optimal State Space Representation via Evolutionary Algorithms: Supporting Expensive Fitness Functions

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Using LibGA to Develop Genetic Algorithms for Solving Combinatorial Optimization Problems

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Strategic Modeling Using a Genetic Algorithm Approach

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Evolving Timetables

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Applications of Genetic Algorithms in Chemical Engineering

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Structure and Performance of Fine-Grain Parallelism in Genetic Search

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Locating Putative Protein Signal Sequences

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Selection Methods for Evolutionary Algorithms

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Parallel Cooperating Genetic Algorithms: An Application to Robot Motion Planning

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Algorithms for Multidimensional Scaling

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How to Apply Genetic Algorithms to Constrained Problems

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Genetic Algorithms for Routing and Scheduling Problems

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Beneficial Effect of Intentional Noise in the Genetic Algorithm

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Evolving Behavior in Repeated 2-Player Games

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Artificial Neural Network Evolution: Learning to Steer a Land Vehicle

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Appendixes

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Contents: Volume II

Quick cross-reference - just select topic of interest:

Introduction

Multi-Niche Crowding for Multi-modal Search

Artificial Neural Network Evolution: Learning to Steer a Land Vehicle

Locating Putative Protein Signal Sequences

Selection Methods for Evolutionary Algorithms

Parallel Cooperating Genetic Algorithms: A n Application to Robot Motion Planning

The Boltzmann Selection Procedure

Structure and Performance of Fine-Grain Paralleli sm in Genetic Search

Parameter Estimation for a Generalized Parallel L oop Scheduling Algorithm

Controlling a Dynamic Physical System Using Genet ic-based Learning Methods

A Hybrid Approach Using Neural Networks, Simulation, G enetic Algorithms, and Machine Learning for Real-time Sequencing and Scheduling Problems

Chemical Engineering

Vehicle Routing with Time Windows Using Genetic Algori thms

Evolutionary Algorithms and Dialogue

Incorporating Redundancy and Gene Activation Mecha nisms in Genetic Search for Adapting to Non-Stationary Environments

Input Space Segmentation with a Genetic Algorithm for G eneration of Rule-based Classifier Systems

Appendixes


Volume II - Full Contents

Introduction

Multi-Niche Crowding for Multi-modal Search

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Artificial Neural Network Evolution: Learning to Steer a Land Vehicle

Return to Contents V1 index

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Locating Putative Protein Signal Sequences

Return to Contents V1 index

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Selection Methods for Evolutionary Algorithms

Return to Contents V1 index

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Parallel Cooperating Genetic Algorithms: An Application to Robot Motion Planning

Return to Contents V1 index

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The Boltzmann Selection Procedure

Return to Contents V1 index

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Structure and Performance of Fine-Grain Parallelism in Genetic Search

Return to Contents V1 index

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Parameter Estimation for a Generalized Parallel Loop Scheduling Algorithm

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Controlling a Dynamic Physical System Using Genetic-based Learning Methods

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A Hybrid Approach Using Neural Networks, Simulation, Genetic Algorithms, and Machine Learning for Real-time Sequencing and Scheduling Problems

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Chemical Engineering

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Vehicle Routing with Time Windows Using Genetic Algorithms

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Evolutionary Algorithms and Dialogue

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Incorporating Redundancy and Gene Activation Mechanisms in Genetic Search for Adapting to Non-Stationary Environments

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Input Space Segmentation with a Genetic Algorithm for Generation of Rule-based Classifier Systems

Appendix I: An Indexed Bibliography of Genetic Algorithms

Appendix II: Publications Contract

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