The Practical Handbook of Genetic Algorithms: New Frontiers, Volume II, 1st Edition (Hardback) book cover

The Practical Handbook of Genetic Algorithms

New Frontiers, Volume II, 1st Edition

Edited by Lance D. Chambers

CRC Press

448 pages

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pub: 1995-08-15
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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.

Table of Contents

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

About the Authors/Editor

Chambers\, Lance D.

Subject Categories

BISAC Subject Codes/Headings:
MAT000000
MATHEMATICS / General
MAT021000
MATHEMATICS / Number Systems