The Practical Handbook of Genetic Algorithms: Applications (Volume 1)

The mathematics employed by genetic algorithms (GAs)are among the most exciting discoveries of the last few decades. From the construction of a simple GA through to advanced implementation, the Practical Handbook of Genetic Algorithms stands as a vital source of compiled knowledge from respected experts around the world. But Volume I of this handbook does more than just describe GAs. Almost two hundred figures and numerous tables show how they should look and how they work. It 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 Practical Handbook of Genetic Algorithms is an exciting introduction to the power of this approach to solving new and exciting problems faced in the real world. It presents an intriguing collection of GA applications that represent a wide area of undertakings in which genetic algorithms have proven to be of value. With the valuable software included, Volume 1 offers a comprehensive selection of hybrid methods for designing efficient and effective solutions for even the most complex problems.

Model Building, Model Testing, and Model Fitting
Uses of Genetic Algorithms
Quantitative Models
Analytical Optimization
Iterative Hill Climbing Technique Assay Continuity in a Gold Prospect
Genie: A First GA
Introduction
Genie
Code Examples
Similes and Space
Data Structures
Individuals
Genes
Chromosomes
Fitness
Populations
Data Structures
Search Strategies
Population Size and Convergence
Breeding
Search Termination
Search Histories
Solution Evaluation
After Genie
Dynamic Populations
Parallel Fitness Evaluation
Niching
Search Refinement
Robustness
Niche and Species Formation in Genetic Algorithms
Introduction
Motivation
Isolation by Distance
Panmictic Mating
Summary Conclusion
Construction of Neural Networks
Introduction
Merging Neural Networks and Genetic Algorithms
Evolutionary Growth Perceptrons
Types of Crossover Operators
Empirical Results
Co-Evolution of Populations
Summary
Crossover Operators
Source Code
Random Numbers
Array
Chromosome
Crossover
Which Operator to Use?
The Boltzmann Selection Procedure
Introduction
Empirical Analysis
Introduction to Boltzmann Selection
Theoretical Analysis
Discussion and Related Work
Conclusion
Optimal State Space Representation via Evolutionary Algorithms: Supporting Expensive Fitness Functions
Introduction to the Problem
Introduction to the Method
Algorithm Overview
The Code Framework
The Genome
New Member Generation
Diversity Enforcement
Reaction to Simulated Annealing
Stopping Conditions
Examples
Conclusions
Using LibGA to Develop Genetic Algorithms for Solving Combinatorial Optimization Problems
Introduction
Genetic Algorithms
Combinatorial Optimization
LibGA
Examples
Conclusions
LibGA Availability
Strategic Modeling Using a Genetic Algorithm Approach
Introduction
Structure of a Model
A Simulation
Graphs
Populations
The Menus
Model Window
Edit Menu
Window Menu
The Windows
Model Menu
Cross Impacts Dialog
Factor Attributes Dialog
Model Preferences Dialog
Graph Browser Window
Graph Window
Population Window
The Population Window
The Genetic Window
Population Limits Dialog
Meet The People Dialog
Defaults and Limits
Model Construction and Interpretation of Results
Western Australian Transport Model
GAs as Assistors in Transport Model
GAs as Assistors in Understanding Systems
Evolving Timetables
Introduction
Timetabling Problems
Genetic Algorithms
Some Possible Methods for GA-Based Timetabling
Some Investigation of the Three Approaches
Results on Some Real Problems
Speeding Things Up: Delta Evaluation
Investigating Further: Scope and Limitation
Strong Methods and Stronger GAs
Some Final Discussion
Applications of Genetic Algorithms in Chemical Engineering
Introduction
Case Study 1: Best Controller Synthesis using Qualitative Criteria
Case Study 2: Optimal Control of a Semi-Batch Reactor
Case Study 3: Optimization of Backmix Reactors in Series
Case Study 4: Solution of Lattice Model to Predict the Adsorption of Polymer Molecules
Comparison with Other Techniques
Conclusions
Structure and Performance of Fine-Grain Parallelism in Genetic Search
Introduction
Three Fine-Grain Parallel GA Topologies
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 Methods
Top-n Selection
Evolutionary Programming Methods
The Effects of Noise
Conclusions
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 Perspectives
Algorithms for Multidimensional Scaling
Introduction
Multidimensional Scaling Examined in More Detail
A Genetic Algorithm for Multidimensional Scaling Methods
Experimental Results
The Computer Program
Using the EXTEND Program
How to Apply Genetic Algorithms to Constrained Problems
Introduction
A CSP Perspective
A GA Point of View
Presentations, Operators and Fitness
Case Studies
Conclusions
Genetic Algorithms for Routing and Scheduling Problems
Scheduling Genetic Algorithms
The Traveling Salesperson Problem
Job Shop and Open Shop Scheduling Problems
The Linear Order Crossover for JSS and OSS Problems
Other Genetic Algorithm Scheduling Problems
Beneficial Effect of Intentional Noise in the Genetic Algorithm
Introduction
Noise Assignment Scheme in the Binary Representation Chromosome
Noise Assignment of GA for Design of a Control System
Analysis of Noise Effects in Genetic Algorithms
Conclusions
Evolving Behavior in Repeated 2-Player Games
Introduction
Game Theory
Evolutionary Game Theory
Implementing a GA
A GA for DFAs in the IPD
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
Appendixes
ga-test.cfg
Frequently Asked Question
Crossover Code
GenAlg Code
Contributor Agreement