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• Variants of Genetics Algorithm and their Applications
• Genetic Algorithms Applications for Challenging Real-World Problems: Some Recent Advances and Future Trends
• Genetic Algorithm for Route Optimization
• Design weight minimization of a reinforced concrete beam through genetic algorithm and its variants
• IGA: an improved genetic algorithm for real-optimization problem
• Application of Genetic Algorithm based controllers in Wind Energy Systems for Smart Energy Management
• Application of Genetic Algorithm in Predicting Mental Illness: A Case Study of Schizophrenia
• Comparison of Biological Inspired Algorithm with Socio Inspired Technique on Load Frequency Control of Multisource Single Area Power system
• Genetic Algorithm and Accelerating Fuzzification for Optimum Sizing and Topology Design of Real-Size Tall Building Systems
• Evaluation of Underwater Images using Genetic Algorithm Monitored Preprocessing and Morphological Segmentation

This book provides fundamental concepts related to various types of genetic algorithms and practical applications in various domains such as medical imaging, manufacturing, and engineering design. The book discusses genetic algorithms which are used to solve a variety of optimization problems. The genetic algorithms are demonstrated to offer reliable search in complex spaces. The book presents high-quality research work by academics and researchers which is useful for young researchers and students

• Introduction.- Genetic Algorithms and Genetic Linkage.- Genetic Linkage Learning Techniques
• Linkage Learning Genetic Algorithm.- Preliminaries: Assumptions and the Test Problem.- A First Improvement: Using Promoters.- Convergence Time for the Linkage Learning Genetic Algorithm.-Introducing Subchromosome Representations.- Conclusions.
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Genetic algorithms (GAs) are powerful search techniques based on principles of evolution and widely applied to solve problems in many disciplines. However, most GAs employed in practice nowadays are unable to learn genetic linkage and suffer from the linkage problem. The linkage learning genetic algorithm (LLGA) was proposed to tackle the linkage problem with several specially designed mechanisms. While the LLGA performs much better on badly scaled problems than simple GAs, it does not work well on uniformly scaled problems as other competent GAs. Therefore, we need to understand why it is so and need to know how to design a better LLGA or whether there are certain limits of such a linkage learning process. This book aims to gain better understanding of the LLGA in theory and to improve the LLGA's performance in practice. It starts with a survey of the existing genetic linkage learning techniques and describes the steps and approaches taken to tackle the research topics, including using promoters, developing the convergence time model, and adopting subchromosomes.
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• Preface.Preface to First Edition.List of Symbols.1. Introduction to Optimization.1.1 Finding the Best Solution.1.2 Minimum-Seeking Algorithms.1.3 Natural Optimization Methods.1.4 Biological Optimization: Natural Selection.1.5 The Genetic Algorithm.2. The Binary Genetic Algorithm.2.1 Genetic Algorithms: Natural Selection on a Computer.2.2 Components of a Binary Genetic Algorithm.2.3 A Parting Look.3. The Continuous Genetic Algorithm.3.1 Components of a Continuous Genetic Algorithm.3.2 A Parting Look.4. Basic Applications.4.1 "Mary Had a Little Lamb".4.2 Algorithmic Creativity-Genetic Art.4.3 Word Guess.4.4 Locating an Emergency Response Unit.4.5 Antenna Array Design.4.6 The Evolution of Horses.4.7 Summary.5. An Added Level of Sophistication.5.1 Handling Expensive Cost Functions.5.2 Multiple Objective Optimization.5.3 Hybrid GA.5.4 Gray Codes.5.5 Gene Size.5.6 Convergence.5.7 Alternative Crossovers for Binary GAs.5.8 Population.5.9 Mutation.5.10 Permutation Problems.5.11 Selling GA Parameters.5.12 Continuous versus Binary GA.5.13 Messy Genetic Algorithms.5.14 Parallel Genetic Algorithms.6. Advanced Applications.6.1 Traveling Salespersons Problem.6.2 Locating an Emergency Response Unit Revisited.6.3 Decoding a Secret Message.6.4 Robot Trajectory Planning.6.5 Stealth Design.6.6 Building Dynamical Inverse Models-The Linear Case.6.7 Building Dynamical Inverse Models-The Nonlinear Case.6.8 Combining GAs with Simulations-Air Pollution Receptor Modeling.6.9 Combining Methods Neural Nets with GAs.6.10 Solving High-Order Nonlinear Partial Differential Equations.7. More Natural Optimization Algorithms.7.1 Simulated Annealing.7.2 Particle Swarm Optimization (PSO).7.3 Ant Colony Optimization (ACO).7.4 Genetic Programming (GP).7.5 Cultural Algorithms.7.6 Evolutionary Strategies.7.7 The Future of Genetic Algorithms.
• Appendix I: Test Functions.
• Appendix II: MATLAB Code.
• Appendix III. High-Performance Fortran Code.Glossary.Index.
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This book deals with the fundamentals of genetic algorithms and their applications in a variety of different areas of engineering and science. Most significant update to the second edition is the MATLAB codes that accompany the text. It provides a thorough discussion of hybrid genetic algorithms Features more examples than first edition.
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"Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory" is a survey of some important theoretical contributions, many of which have been proposed and developed in the Foundations of Genetic Algorithms series of workshops. However, this theoretical work is still rather fragmented, and the authors believe that it is the right time to provide the field with a systematic presentation of the current state of theory in the form of a set of theoretical perspectives. The authors do this in the interest of providing students and researchers with a balanced foundational survey of some recent research on GAs. The scope of the book includes chapter-length discussions of Basic Principles, Schema Theory, "No Free Lunch", GAs and Markov Processes, Dynamical Systems Model, Statistical Mechanics Approximations, Predicting GA Performance, Landscapes and Test Problems.
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• 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 Work.
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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 deeper.
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The Simple Genetic Algorithm (SGA) is a classical form of genetic search. Viewing the SGA as a mathematical object, Michael D. Vose provides an introduction to what is known (or proven) about the theory of the SGA. Vose also makes available algorithms for the computation of mathematical objects related to the SGA. Although he describes the SGA in terms of heuristic search, this book is not about search or optimization. Rather, the focus is on the SGA as an evolutionary system. The intention is that the text should serve as an outline for exploring topics in mathematics and computer science in a goal-oriented way.
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• HARD PROBLEMS AND GENETIC ALGORITHMS. What's the Problem? Genetic Algorithms. THE GROUPING GENETIC ALGORITHM. Grouping Problems. Drawbacks of Previous GAs for Grouping Problems. The Grouping Genetic Algorithm. INDUSTRIAL APPLICATIONS OF GROUPING GENETIC ALGORITHMS. Bin Packing and Line Balancing. Economies of Scale. Creation of Part Families--Conceptual Clustering. Equal Piles. EPILOGUE. Where Have We Been? Where Are We Now? Where Are We Heading? Appendices. The NP-Hard Problems. Complexity and Genetic Research. References. Index.
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A reader-friendly introduction to the exciting, vast potential of Genetic Algorithms. The book gives readers a general understanding of the concepts underlying the technology, an insight into its perceived benefits and failings, and a clear and practical illustration of how optimization problems can be solved more efficiently using Falkenauer's new class of algorithms.
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• Introduction to Optimization
• The Binary Genetic Algorithm
• The Continuous Parameter Genetic Algorithm
• Applications
• An Added Level of Sophistication
• Advanced Applications
• Evolutionary Trends
• Appendix
• Glossary
• Index.
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A tutorial on genetic algorithms with an emphasis on practical applications The rapidly expanding field of genetic algorithms has given rise to many new applications in a variety of disciplines. However, most of the existing books on the subject concentrate on theory. Practical Genetic Algorithms is the first introductory-level book to emphasize practical applications through the use of example problems. In an accessible style, the authors explain why the genetic algorithm is superior in many real-world applications, cover continuous parameter genetic algorithms, and provide in-depth trade-off analysis of genetic algorithm parameter selection. Written for the end user in engineering, science, and computer programming, as well as upper-level undergraduate and graduate students, Practical Genetic Algorithms: Provides numerous practical example problems Contains over 80 illustrations Features many figures and tables Includes three appendices: a glossary of terms, a list of genetic algorithm routines in pseudocode, and a list of symbols used in the book.
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• Introduction to genetic algorithms
• levels of evolution for control systems
• multiobjective genetic algorithms
• constraint resolution in genetic algorithms
• toward the evolution of scaleable neutral architectures
• chaotic system identification
• job shop scheduling
• evolutionary algorithms for robotic systems
• aerodynamic inverse optimisation problems
• genetic design of VLSI layouts.
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A broad survey of the current trends and techniques in genetic algorithms (GAs), which are general purpose search and optimisation methods applicable to a wide variety of problems. Theoretical innovations and practical applications in engineering systems are then discussed in the text.
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This volume and the included software present a selection of hybrid methods for designing efficient and effective solutions for complex problems. The diskette is filled with codes, applications and descriptions of how each code can be implemented.
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This book constitutes the thoroughly refereed post-proceedings of the 9th Workshop on the Foundations of Genetic Algorithms, FOGA 2007, held in Mexico City, Mexico in January 2007. The 11 revised full papers presented were carefully reviewed and selected during two rounds of reviewing and improvement from 22 submissions. The papers address all current topics in the field of theoretical evolutionary computation including evolution strategies, evolutionary programming, and genetic programming, and also depict the continuing growth in interactions with other fields such as mathematics, physics, and biology.
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This book constitutes the refereed proceedings of the 8th workshop on the foundations of genetic algorithms, FOGA 2005, held in Aizu-Wakamatsu City, Japan, in January 2005. The 16 revised full papers presented provide an outstanding source of reference for the field of theoretical evolutionary computation including evolution strategies, evolutionary programming, and genetic programming, as well as the continuing growth in interactions with other fields such as mathematics, physics, and biology.
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• Foreword * Preface * Genetic algorithms as multi-coordinators in large-scale optimization * Telecommunication network optimization with genetic algorithms: A decade of practice * Using evolutionary algorithms to search for control parameters in a nonlinear partial differential equation * Applying genetic algorithms to real-world problems. An overview of evolutionary programming * A hierarchical genetic algorithm for system identification and curve fitting with a supercomputer implementation * Experiences with the PGAPack parallel genetic algorithm library * The significance of the evaluation function in evolutionary algorithms * Genetic algorithm optimization of atomic clusters * Search, binary representations and counting optima * An investigation of GA performance results for different cardinality alphabets * Genetic algorithms and the design of experiments * Efficient parameter optimization based on combination of direct global and local search methods * What are genetic algorithms? A mathematical perspective * Survey of projects involving evolutionary algorithms sponsored by the Electric Power Research Institute.
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The IMA Workshop on Evolutionary Algorithms brought together many of the top researchers in the area of Evolutionary Computation for a week of intensive interaction. The field of Evolutionary Computation has developed significantly over the past 30 years and today consists of a variety of subfields such as genetic algorithms, evolution strategies, evolutionary programming, and genetic programming, each with its own algorithmic perspectives and goals. The workshop did a great deal to clarify the current state of the theory of Evolutionary Algorithms. The existing theory might be characterized as deriving from two principal approaches. There is a high level macro-theory that looks at the processing of "building blocks" and "schemata" that are shared by many good solutions when searching a problem space. There is also a low level micro-theory that builds exact Markov models of the search process. It is sometimes hard for researchers working at such different levels of abstraction to interact. The IMA workshop allowed researchers working at these different levels to present their points of view and to move toward common ground. There was real progress in communication between theorists and practitioners in the evolutionary computation field. Speakers presented applications across a wide range of problem areas. In some of those cases, theoretically motivated methods work quite well. In other cases, practitioners used domain-based methods to obtain better performance than could be achieved by using a "pure" evolutionary algorithm. Individuals on both sides went away with a better appreciation of the successes and failures of current theory.
(source: Nielsen Book Data)