The Practical Handbook of Genetic Algorithms

The Practical Handbook of Genetic Algorithms : Applications

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Rapid developments in the field of genetic algorithms along with the popularity of the first edition precipitated this completely revised, thoroughly updated second edition of The Practical Handbook of Genetic Algorithms. Like its predecessor, this edition helps practitioners stay up to date on recent developments in the field and provides material they can use productively in their own endeavors. For this edition, the editor again recruited authors at the top of their field and from a cross section of academia and industry, theory and practice. Their contributions detail their own research, new applications, experiment results, and recent advances. Among the applications explored are scheduling problems, optimization, multidimensional scaling, constraint handling, and feature selection and classification. The science and art of GA programming and application has come a long way in the five years since publication of the bestselling first edition. But there still is a long way to go before its bounds are reached-we are still just scratching the surface of GA applications and refinements. By introducing intriguing new applications, offering extensive lists of code, and reporting advances both subtle and dramatic, The Practical Handbook of Genetic Algorithms is designed to help readers contribute to scratching that surface a bit more

Product details

  • Hardback | 544 pages
  • 164.6 x 241.6 x 35.3mm | 1,015.19g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • Revised
  • 2nd Revised edition
  • 200 black & white illustrations, 68 black & white tables
  • 1584882409
  • 9781584882404

Table of contents

MODEL BUILDING, MODEL TESTING, AND MODEL FITTING Uses of Genetic Algorithms Quantitative Models Analytical Optimization Iterative Hill-Climbing Techniques Assay Continuity in a Gold Prospect Conclusions COMPACT FUZZY MODELS AND CLASSIFIERS THROUGH MODEL REDUCTION AND EVOLUTIONARY OPTIMIZATION Fuzzy Modeling Transparency and Accuracy of Fuzzy Models Genetic Algorithms Crossover Operators Examples TS Singleton Model TS Linear Model Conclusion ON THE APPLICATION OF REORGANIZATION OPERATORS FOR SOLVING A LANGUAGE RECOGNITION PROBLEM Performance Across a New Problem Set Reorganization Operators The Experimentation Data Obtained from the Experimentation General Evaluation Criteria Evaluation Conclusions and Further Directions USING GA TO OPTIMIZE THE SELECTION AND SCHEDULING OF ROAD PROJECTS Introduction Formulation of the Genetic Algorithm Mapping the GA String into a Project Schedule and Computing the Fitness Results Conclusions: Scheduling Interactive Road Projects by GA DECOUPLED OPTIMIZATION OF POWER ELECTRONICS CIRCUITS USING GENETIC ALGORITHMS Introduction Decoupled Regulator Configuration Fitness Function for FN Steps of Optimization Design Example Conclusions FEATURE SELECTION AND CLASSIFICATION IN THE DIAGNOSIS OF CERVICAL CANCER Introduction Feature Selection Feature Selection by Genetic Algorithm Developing a Neural Genetic Classifier Validation of the Algorithm Parameterization of the GA Experiments with the Cell Image Data Set ALGORITHMS FOR MULTIDIMENSIONAL SCALING Introduction Multidimensional Scaling Examined in more Detail A Genetic Algorithm for Multidimensional Scaling Experimental Results The Computer Program Using the Extend Program GENETIC ALGORITHM-BASED APPROACH FOR TRANSPORTATION OPTIMIZATION PROBLEMS GAs-Based Solution Approach for Transport Models GAs-Based Calibration Approach for Transport Models Concluding Remarks SOLVING JOB-SHOP SCHEDULING PROBLEMS BY MEANS OF GENETIC ALGORITHMS Introduction The Job Shop Scheduling Constraint Satisfaction Problem The Genetic Algorithm Fitness Refinement Heuristic Initial Population Experimental Results Conclusions APPLYING THE IMPLICIT REDUNDANT REPRESENTATION GENETIC ALGORITHM IN AN UNSTRUCTURED PROBLEM DOMAIN Introduction Motivation for Frame Synthesis Research Notes in Mathematics series The Implicit Redundant Representation of Genetic Algorithm The IRR Genotype/Phenotype Representation Applying the IRR GA to Frame Design Synthesis in an Unstructured Domain IRR GA Fitness Evaluation of Frame Design Synthesis Alternatives Discussion of the Genetic Control Operators Used by the IRR GA Results of the Implicit Redundant Representation Frame Synthesis Trials HOW TO HANDLE CONSTRAINTS WITH EVOLUTIONARY ALGORITHMS Introduction Constraints Handling in EAs Evolutionary CSP Solvers Discussion Assessment of Eas for CSPs Conclusion AN OPTIMIZED FUZZY LOGIC CONTROLLER FOR ACTIVE POWER FACTOR CORRECTOR USING GENETIC ALGORITHM Introduction FLC for the Boost Rectifier Optimization of FLC by the Genetic Algorithm Illustrative Example Conclusions MULTILEVEL FUZZY PROCESS CONTROL OPTIMIZED BY GENETIC ALGORITHM Introduction Intelligent Control Multilevel Control Optimizing Aided by Genetic Algorithm Laboratory Cascaded Plant Multilevel Control using Genetic Algorithm Fuzzy Multilevel Coordinated Control Conclusions Evolving Neural Networks for Cancer Radiotherapy EVOLVING NEURAL NETWORKS FOR CANCER RADIOTHERAPY Introduction and Chapter Overview An Introduction to Radiotherapy Evolutionary Artificial Neural Networks Radiotherapy Treatment Planning with EANNs Summary Discussion and Future Workshow more

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