Practical Handbook of Genetic Algorithms
Volume II, Applications

Editor - Lance D. Chambers

Department of Transport, Nedlands, Western Australia

Description | Features | Contents | Publication Information and Price

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.

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.

Volume II picks up where the first book leaves off and presents the topic from more of an applications point of view. The focus of the book is to show the reader how to develop their own genetic algorithm coding schemes and how and when to employ the GA to solve problems.

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Features

• Provides pages and pages of computer codes and a diskette with code lists and applications that are ready to cut, paste, and run
• Offers a wide selection of real-world problem examples
• Chapters are written by international experts on the applications of Genetic Algorithms
• Provides an excellent introduction to the subject as well as the most complete collection of intermediate applications of GAs for the experienced practitioner

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Contents

Introduction

Multi-Niche Crowding for Multi-modal Search

• Introduction
• Genetic Algorithms for Multi-modal Search
• Application of MNC to Multi-modal Test Functions
• Application to DNA Restriction Fragment Map Assembly
• Results and Discussion
• Conclusions
• Previous Related Work and Scope of Present Work
• Appendix

Artificial Neural Network Evolution: Learning to Steer a Land Vehicle

• Overview
• Introduction to Artificial Neural Networks
• Introduction to ALVINN
• The Evolutionary Approach
• Task Specifics
• Implementation and Results
• Conclusions
• Future Directions

Locating Putative Protein Signal Sequences

• Introduction
• Implementation
• Results of Sample Applications
• Parametrization Study
• Future Directions

Selection Methods for Evolutionary Algorithms

• Fitness Proportionate Selection (FPS)
• Windowing
• Sigma Scaling
• Linear Scaling
• Sampling Algorithms
• Ranking
• Linear Ranking
• Exponential Ranking
• Tournament Selection
• Genitor or Steady State Models
• Evolution Strategy and Evolutionary Programming Methods
• Evolution Strategy Approaches
• Top-n Selection
• Evolutionary Programming Methods
• The Effects of Noise
• Conclusions
• References

Parallel Cooperating Genetic Algorithms: An Application to Robot Motion Planning

• Introduction
• Principles of Genetic Algorithms
• The Search Algorithm
• The Explore Algorithm
• The Ariadne's CLEW Algorithm
• Parallel Implementation
• Conclusion, Results, and Perspective

The Boltzmann Selection Procedure

• Introduction
• Empirical Analysis
• Introduction to Boltzmann Selection
• Theoretical Analysis
• Discussion and Related Work
• Conclusion

Structure and Performance of Fine-Grain Parallelism in Genetic Search

• Introduction
• Three Fine-Grain Parallel GA Topologies
• Performance of fgpGAs and cgpGAs
• Future Directions

Parameter Estimation for a Generalized Parallel Loop Scheduling Algorithm

• Introduction
• Current Scheduling Algorithms
• A New Scheduling Methodology
• Results
• Conclusion

Controlling a Dynamic Physical System Using Genetic-based Learning Methods

• Introduction
• The Control Task
• Previous Learning Algorithms for the Pole-Cart Problem
• Genetic Algorithms (GA)
• Generating Control Rules Using a Simple GA
• Implementation Details
• Experimental Results
• Difficulties with GAPOLE Approach
• A Different Genetic Approach for the Problem
• The Structured Genetic Algorithm
• Evolving Neuro-controllers Using sGA
• Fitness Measure and Reward Scheme
• Simulation Results
• Discussion

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

• Introduction
• Hierarchical Generic Controller
• Implementing the Optimization Function
• An Example
• Remarks

Chemical Engineering

• Introduction
• Case Study 1: Best Controller Synthesis Using Qualitative Criteria
• Case Study 2: Optimization of Back Mix Reactors in Series
• Case Study 3: Solution of Lattice Model to Predict Adsorption of Polymer Molecules
• Comparison with Other Techniques

Vehicle Routing with Time Windows Using Genetic Algorithms

• Introduction
• Mathematical Formulation for the VRPTW
• The GIDEON System
• Computational Results
• Summary and Conclusions

Evolutionary Algorithms and Dialogue

• Introduction
• Methodology
• Evolutionary Algorithms
• Natural Language Processing
• Dialogue in LOLITA
• Tuning the Parameters
• Target Dialogues
• Application of EAs to LOLITA
• Results
• Improving the Fitness Function
• Discussion
• Summary
• References

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

• Introduction
• The Structured GA
• Use of sGA in a Time-varying Problem
• Experimental Details
• Conclusions

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

• Introduction
• A Heuristic Method
• Genetic Algorithm Based Method
• Results

Appendix I

• An Indexed Bibliography of Genetic Algorithms

Appendix II

• Publications Contract

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Publication and Pricing

Catalog number 2529WGBA

August 1995, 448 pp., ISBN: 0-8493-2529-3

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