The Practical Handbook of Genetic Algorithms: Applications, Second Edition

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.

Table of Contents:
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