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
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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- 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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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
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
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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
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Niche and Species Formation in Genetic Algorithms
- Introduction
- Motivation
- Isolation by Distance
- Panmictic Mating
- Summary Conclusion
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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
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Crossover Operators
- Source Code
- Array
- Chromosome
- Crossover
- Which Operator to Use?
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The Boltzmann Selection Procedure
- Introduction
- Empirical Analysis
- Introduction to Boltzmann Selection
- Theoretical Analysis
- Discussion and Related Work
- Conclusion
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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
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Using LibGA to Develop Genetic Algorithms for Solving Combinatorial
Optimization Problems
- Introduction
- Genetic Algorithms
- Combinatorial Optimization
- LibGA
- Examples
- Conclusions
- LibGA Availability
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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
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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
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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
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Structure and Performance of Fine-Grain Parallelism in Genetic
Search
- Introduction
- Three Fine-Grain Parallel GA Topologies
- Future Directions
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Locating Putative Protein Signal Sequences
- Introduction
- Implementation
- Results of Sample Applications
- Parametrization Study
- Future Directions
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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
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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
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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
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How to Apply Genetic Algorithms to Constrained Problems
- Introduction
- A CSP Perspective
- A GA Point of View
- Presentations, Operators and Fitness
- Case Studies
- Conclusions
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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
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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
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Evolving Behavior in Repeated 2-Player Games
- Introduction
- Game Theory
- Evolutionary Game Theory
- Implementing a GA
- A GA for DFAs in the IPD
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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
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Appendixes
- ga-test.cfg
- Frequently Asked Question
- Crossover Code
- GenAlg Code
- Contributor Agreement
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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
- 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
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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
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Locating Putative Protein Signal Sequences
- Introduction
- Implementation
- Results of Sample Applications
- Parametrization Study
- Future Directions
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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
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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
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The Boltzmann Selection Procedure
- Introduction
- Empirical Analysis
- Introduction to Boltzmann Selection
- Theoretical Analysis
- Discussion and Related Work
- Conclusion
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Structure and Performance of Fine-Grain Parallelism in Genetic
Search
- Introduction
- Three Fine-Grain Parallel GA Topologies
- Performance of fgpGAs and cgpGAs
- Future Directions
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Parameter Estimation for a Generalized Parallel Loop Scheduling
Algorithm
- Introduction
- Current Scheduling Algorithms
- A New Scheduling Methodology
- Results
- Conclusion
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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
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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
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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
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Vehicle Routing with Time Windows Using Genetic Algorithms
- Introduction
- Mathematical Formulation for the VRPTW
- The GIDEON System
- Computational Results
- Summary and Conclusions
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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
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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
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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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