Chambers The Practical Handbook of Genetic Algorithms

New Frontiers, Volume II
1. Auflage 2002
ISBN: 978-1-4200-5007-3
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 464 Seiten

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.

Zielgruppe

Audience Professionals who work with genetic algorithms in the fields of computer science, electrical engineering, and mathematics.

Autoren/Hrsg.

Chambers, Lance D.

Fachgebiete

• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik

Inhaltsverzeichnis

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