%{search_type} search results

• Introduction
• Related Work
• Architecture
• Evaluation
• Conclusions and future work.

This book presents a genetic algorithm that optimizes a grid template pattern detector to find the best point to trade in the SP 500. The pattern detector is based on a template using a grid of weights with a fixed size. The template takes in consideration not only the closing price but also the open, high, and low values of the price during the period under testing in contrast to the traditional methods of analysing only the closing price. Each cell of the grid encompasses a score, and these are optimized by an evolutionary genetic algorithm that takes genetic diversity into consideration through a speciation routine, giving time for each individual of the population to be optimized within its own niche. With this method, the system is able to present better results and improves the results compared with other template approaches. The tests considered real data from the stock market and against state-of-the-art solutions, namely the ones using a grid of weights which does not have a fixed size and non-speciated approaches. During the testing period, the presented solution had a return of 21.3% compared to 10.9% of the existing approaches. The use of speciation was able to increase the returns of some results as genetic diversity was taken into consideration.
(source: Nielsen Book Data)

• Front Cover; Foundations of Genetic Algorithms6; Copyright Page; Contents;
• Chapter 1. Introduction;
• Chapter 2. Overcoming Fitness Barriers in Multi-Modal Search Spaces;
• Chapter 3. Niches in NK-Landscapes;
• Chapter 4. New Methods for Tunable, Random Landscapes;
• Chapter 5. Analysis of Recombinative Algorithms on a Non-Separable Building-Block Problem;
• Chapter 6. Direct Statistical Estimation of GA Landscape Properties;
• Chapter 7. Comparing Population Mean Curves;
• Chapter 8. Local Performance of the ((/(I, () -ES in a Noisy Environment.

• Chapter 9. Recursive Conditional Scheme Theorem, Convergence and Population Sizing in Genetic Algorithms
• Chapter 10. Towards a Theory of Strong Overgeneral Classifiers;
• Chapter 11. Evolutionary Optimization through PAC Learning;
• Chapter 12. Continuous Dynamical System Models of Steady-State Genetic Algorithms;
• Chapter 13. Mutation-Selection Algorithm: A Large Deviation Approach;
• Chapter 14. The Equilibrium and Transient Behavior of Mutation and Recombination;
• Chapter 15. The Mixing Rate of Different Crossover Operators;
• Chapter 16. Dynamic Parameter Control in Simple Evolutionary Algorithms.

• Chapter 17. Local Search and High Precision Gray Codes: Convergence Results and Neighborhoods
• Chapter 18. Burden and Benefits of Redundancy; Author Index; Key Word Index.

Papers presented at the 2000 Foundations of Genetic Algorithms (FOGA-6) sixth biennial workshop held in Charlottesville, VA.

• Differential Evolution.- Neoteric Differential Evolution.- Strategies of Search.- Exploration and Exploitation.- New Performance Measures.- Transversal Differential Evolution.- On Analogy with Some Other Algorithms.- Energetic Selection Principle.- On Hybridization of Differential Evolution.- Applications.- End Notes.
• (source: Nielsen Book Data)

Individuals and enterprises are looking for optimal solutions for the problems they face. Most problems can be expressed in mathematical terms, and so the methods of optimization render a significant aid. This book details the latest achievements in optimization. It offers comprehensive coverage on Differential Evolution, presenting revolutionary ideas in population-based optimization and shows the best known metaheuristics through the prism of Differential Evolution.
(source: Nielsen Book Data)

• Part I: Foundations.- Introduction.- Genetic Algorithms.- Parameters.- Part II: Solution Spaces.- Multimodality.- Constraints.- Multiple Objectives.- Part III: Advanced Concepts.- Theory.- Machine Learning.- Applications.- Part IV: Ending.- Summary and Outlook.- Index.- References.
• (source: Nielsen Book Data)

This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.
(source: Nielsen Book Data)

• Machine generated contents note: Fuzzy Rule-Based Systems
• Evolutionary Computation
• Introduction to Genetic Fuzzy Systems
• Genetic Tuning Processes
• Learning with Genetic Algorithms
• Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach
• Genetic Fuzzy Rule-Based Systems Based on the Pittsburgh Approach
• Genetic Fuzzy Rule-Based Systems Based on the Iterative Rule Learning Approach
• Other Genetic Fuzzy Rule-Based System
• Other Kinds of Evolutionary Fuzzy Systems.

In recent years, a great number of publications have explored the use of genetic algorithms as a tool for designing fuzzy systems. Genetic Fuzzy Systems explores and discusses this symbiosis of evolutionary computation and fuzzy logic. The book summarizes and analyzes the novel field of genetic fuzzy systems, paying special attention to genetic algorithms that adapt and learn the knowledge base of a fuzzy-rule-based system. It introduces the general concepts, foundations and design principles of genetic fuzzy systems and covers the topic of genetic tuning of fuzzy systems. It also introduces the three fundamental approaches to genetic learning processes in fuzzy systems: the Michigan, Pittsburgh and Iterative-learning methods. Finally, it explores hybrid genetic fuzzy systems such as genetic fuzzy clustering or genetic neuro-fuzzy systems and describes a number of applications from different areas.Genetic Fuzzy System represents a comprehensive treatise on the design of the fuzzy-rule-based systems using genetic algorithms, both from a theoretical and a practical perspective. It is a valuable compendium for scientists and engineers concerned with research and applications in the domain of fuzzy systems and genetic algorithms.
(source: Nielsen Book Data)

"The Design of Innovation" illustrates how to design and implement competent genetic algorithms - genetic algorithms that solve hard problems quickly, reliably, and accurately - and how the invention of competent genetic algorithms amounts to the creation of an effective computational theory of human innovation. For the specialist in genetic algorithms and evolutionary computation, this book combines over two decades of hard-won research results in a single volume to provide a comprehensive step-by-step guide to designing genetic algorithms that scale well with problem size and difficulty. For the innovation researcher - whether from the social and behavioral sciences, the natural sciences, the humanities, or the arts - this unique book gives a consistent and valuable mathematical and computational viewpoint for understanding certain aspects of human innovation. For all readers, "The Design of Innovation" provides an entree into the world of competent genetic algorithms and innovation through a methodology of invention borrowed from the Wright brothers. Combining careful decomposition, cost-effective, little analytical models, and careful design, the road to competence is paved with easily understood examples, simulations, and results from the literature.
(source: Nielsen Book Data)

• Neural networks in systems identification and control - supervised learning in multilayer perceptions - the back-propagation algorithm
• identification of two-dimensional state space discrete systems using neural networks
• neural networks for control
• neuro-based adaptive regulator
• local model networks and self-tuning predictive control
• fuzzy and neuro-fuzzy systems in modelling, control and robot path planning - an on-line self constructing fuzzy modelling architecture based on neural and fuzzy concepts and techniques
• neuro-fuzzy model-based control
• fuzzy and neurofuzzy approaches to mobile robot path and motion planning under uncertainty
• genetic-evolutionary algorithms - a tutorial overview of genetic algorithms and their applications
• results from a variety of genetic algorithm applications showing the robustness of the approach
• evolutionary algorithms in computer-aided design of integrated circuits
• soft computing applications - soft data fusion
• application of neural networks to computer gaming
• coherent neural networks and their applications to control and signal processing
• neural, fuzzy and evolutionary reinforcement learning systems - an application case study
• neural networks in industrial and environmental applications.
• (source: Nielsen Book Data)

Soft computing is a branch of computing which, unlike hard computing, can deal with uncertain, imprecise and inexact data. The three constituents of soft computing are fuzzy-logic-based computing, neurocomputing, and genetic algorithms. Fuzzy logic contributes the capability of approximate reasoning, neurocomputing offers function approximation and learning capabilities, and genetic algorithms provide a methodology for systematic random search and optimization. These three capabilities are combined in a complementary and synergetic fashion.This book presents a cohesive set of contributions dealing with important issues and applications of soft computing in systems and control technology. The contributions include state-of-the-art material, mathematical developments, fresh results, and how-to-do issues. Among the problems studied via neural, fuzzy, neurofuzzy and genetic methodologies are: data fusion, reinforcement learning, approximation properties, multichannel imaging, signal processing, system optimization, gaming, and several forms of control.The book can serve as a reference for researchers and practitioners in the field. Readers can find in it a large amount of useful and timely information, and thus save considerable effort in searching for other scattered literature.
(source: Nielsen Book Data)

• Multiobjective Genetic Algorithms Basic Network Models Logistics Network Models Communication Network Models Advanced Planning and Scheduling Models Project Scheduling Models Assembly Line Balancing Models Tasks Scheduling Models Advanced Network Models.
• (source: Nielsen Book Data)

Network models are critical tools in business, management, science and industry. Network Models and Optimization: Multiobjective Genetic Algorithm Approach presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization problems in many disciplines, such as engineering, computer science, operations research, transportation, telecommunication, and manufacturing. Network Models and Optimization: Multiobjective Genetic Algorithm Approach extensively covers algorithms and applications, including shortest path problems, minimum cost flow problems, maximum flow problems, minimum spanning tree problems, travelling salesman and postman problems, location-allocation problems, project scheduling problems, multistage-based scheduling problems, logistics network problems, communication network problem, and network models in assembly line balancing problems, and airline fleet assignment problems. Network Models and Optimization: Multiobjective Genetic Algorithm Approach can be used both as a student textbook and as a professional reference for practitioners in many disciplines who use network optimization methods to model and solve problems.

• Evolutionary Multi-Multi-Objective Optimization - EMMOO.- Implementation of Multiobjective Memetic Algorithms for Combinatorial Optimization Problems: A Knapsack Problem Case Study.- Knowledge Infused in Design of Problem-Specific Operators.- Solving Time-Tabling Problems Using Evolutionary Algorithms and Heuristics Search.- An Efficient Genetic Algorithm with Uniform Crossover for the Multi-Objective Airport Gate Assignment Problem.- Application of Evolutionary Algorithms for Solving Multi-Objective Simulation Optimization Problems.- Feature Selection Using Single/Multi-Objective Memetic Frameworks.- Multi-Objective Robust Optimization Assisted by Response Surface Approximation and Visual Data-Mining.- Multiobjective Metamodel-Assisted Memetic Algorithms.- A Convergence Acceleration Technique for Multiobjective Optimisation.- Knowledge Propagation through Cultural Evolution.- Risk and Cost Tradeoff in Economic Dispatch Including Wind Power Penetration Based on Multi-Objective Memetic Particle Swarm Optimization.- Hybrid Behavioral-Based Multiobjective Space Trajectory Optimization.- Nature-Inspired Particle Mechanics Algorithm for Multi-Objective Optimization.- Information Exploited for Local Improvement.- Combination of Genetic Algorithms and Evolution Strategies with Self-adaptive Switching.- Comparison between MOEA/D and NSGA-II on the Multi-Objective Travelling Salesman Problem.- Integrating Cross-Dominance Adaptation in Multi-Objective Memetic Algorithms.- A Memetic Algorithm for Dynamic Multiobjective Optimization.- A Memetic Coevolutionary Multi-Objective Differential Evolution Algorithm.- Multiobjective Memetic Algorithm and Its Application in Robust Airfoil Shape Optimization.
• (source: Nielsen Book Data)

The application of sophisticated evolutionary computing approaches for solving complex problems with multiple conflicting objectives in science and engineering have increased steadily in the recent years. Within this growing trend, Memetic algorithms are, perhaps, one of the most successful stories, having demonstrated better efficacy in dealing with multi-objective problems as compared to its conventional counterparts. Nonetheless, researchers are only beginning to realize the vast potential of multi-objective Memetic algorithm and there remain many open topics in its design. This book presents a very first comprehensive collection of works, written by leading researchers in the field, and reflects the current state-of-the-art in the theory and practice of multi-objective Memetic algorithms. "Multi-Objective Memetic algorithms" is organized for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of Memetic algorithms and multi-objective optimization.
(source: Nielsen Book Data)

• Some combinatorial landscapes on which a genetic algorithm outperforms other stochastic iterative methods / Dave Corne and Peter Ross
• Maximum entropy analysis of genetic algorithm operators / Jonathan L. Shapiro and Adam Prügel-Bennett
• The ant colony metaphor for searching continuous design spaces / G. Bilchev and I.C. Parmee
• Broadcast based fitness sharing GA for conflict resolution among autonomous robots / Sadoyoshi Mikami, Yukinori Kakazu and Terence C. Fogarty
• An adaptive poly-parental recombination strategy / Jim Smith and T.C. Fogarty
• Neighbourhood seeding to reduce problem modality / A.J. Swann
• Specialized recombinative operators for timetabling problems / Edmund Burke, Dave Elliman and Rupert Weare
• The use of local search suggestion lists for improving the solution of timetable problems with evolutionary algorithms / Ben Paechter, Andrew Cumming and Henri Luchian
• Comparing genetic algorithms, simulated annealing, and stochastic hillclimbing on timetable problems / Peter Ross and Dave Corne
• Evolutionary learning in computational ecologies : an application to adaptive, distributed routing in communication networks / Brian Carse, Terence C. Fogarty, and Alistair Munro
• The radio link frequency assignment problem : a case study using genetic algorithms / A. Kapsalis [and others]
• Scheduling planned maintenance of the national grid / W.B. Langdon
• Genetic operators and constraint handling for pipe network optimization / Dragan A. Savic and Godfrey A. Walters
• A multi-objective approach to constrained optimisation of gas supply networks : COMOGA method / Patrick D. Surry, Nicholas J. Radcliffe and Ian D. Boyd
• Ternary decision diagram optimisation of Reed-Muller logic functions using a genetic algorithm for variable and simplification rule ordering / J.F. Miller, P. Thomson and P.V.G. Bradbeer
• An evolutionary algorithm for parametric array signal processing / Dekum Yang and Stuart Flockton
• Constraints on task and search complexity in GA + NN models of learning and adaptive behaviour / Mukesh J. Patel
• Load balancing application of the genetic algorithm in a nonstationary environment / Frank Vavak, Terence C. Fogarty and Phillip Cheng
• Exploring some commercial applications of genetic programming / Gerald Robinson and Paul McIlroy.

This volume is based on the Workshop on Evolutionary Computing held in Sheffield, U.K., in April 1995 under the sponsorship of the Society for the Study of Artificial Intelligence and Simulation of Behavior (AISB). The 18 full papers presented were selected during a post-workshop refereeing meeting and chosen from 32 submissions for the workshop. The papers are organized in sections on evolutionary computing theory and techniques, timetabling, routing and scheduling, optimization, signal processing and control, and genetic programming. The collection of papers has a certain bias towards real world applications of evolutionary computing and particularly genetic algorithms.

• Practical Genetic Algorithms.- Real-World Application: Routing Problem.- Elitist Compact Genetic Algorithms.- Real-coded Bayesian Optimization Algorithm.- Multiobjective Real-coded Bayesian Optimization Algorithm.- Conclusions.
• (source: Nielsen Book Data)

Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. This book provides effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms.
(source: Nielsen Book Data)

• Introduction. Reference Alignment. Genome Assembly. Variation Discovery by Mapping to Reference. RNA-seq. ChIP-seq. Meta-Genomic. Other Technologies.
• (source: Nielsen Book Data)

Advances in sequencing technology have allowed scientists to study the human genome in greater depth and on a larger scale than ever before - as many as hundreds of millions of short reads in the course of a few days. But what are the best ways to deal with this flood of data? Algorithms for Next-Generation Sequencing is an invaluable tool for students and researchers in bioinformatics and computational biology, biologists seeking to process and manage the data generated by next-generation sequencing, and as a textbook or a self-study resource. In addition to offering an in-depth description of the algorithms for processing sequencing data, it also presents useful case studies describing the applications of this technology.
(source: Nielsen Book Data)

• Front Cover; Foundations of Genetic Algorithms6; Copyright Page; Contents;
• Chapter 1. Introduction;
• Chapter 2. Overcoming Fitness Barriers in Multi-Modal Search Spaces;
• Chapter 3. Niches in NK-Landscapes;
• Chapter 4. New Methods for Tunable, Random Landscapes;
• Chapter 5. Analysis of Recombinative Algorithms on a Non-Separable Building-Block Problem;
• Chapter 6. Direct Statistical Estimation of GA Landscape Properties;
• Chapter 7. Comparing Population Mean Curves;
• Chapter 8. Local Performance of the ((/(I, () -ES in a Noisy Environment

• Chapter 9. Recursive Conditional Scheme Theorem, Convergence and Population Sizing in Genetic Algorithms
• Chapter 10. Towards a Theory of Strong Overgeneral Classifiers;
• Chapter 11. Evolutionary Optimization through PAC Learning;
• Chapter 12. Continuous Dynamical System Models of Steady-State Genetic Algorithms;
• Chapter 13. Mutation-Selection Algorithm: A Large Deviation Approach;
• Chapter 14. The Equilibrium and Transient Behavior of Mutation and Recombination;
• Chapter 15. The Mixing Rate of Different Crossover Operators;
• Chapter 16. Dynamic Parameter Control in Simple Evolutionary Algorithms

• Chapter 17. Local Search and High Precision Gray Codes: Convergence Results and Neighborhoods
• Chapter 18. Burden and Benefits of Redundancy; Author Index; Key Word Index

Foundations of Genetic Algorithms, Volume 6 is the latest in a series of books that records the prestigious Foundations of Genetic Algorithms Workshops, sponsored and organised by the International Society of Genetic Algorithms specifically to address theoretical publications on genetic algorithms and classifier systems. Genetic algorithms are one of the more successful machine learning methods. Based on the metaphor of natural evolution, a genetic algorithm searches the available information in any given task and seeks the optimum solution by replacing weaker populations with stronger ones. Includes research from academia, government laboratories, and industry Contains high calibre papers which have been extensively reviewed Continues the tradition of presenting not only current theoretical work but also issues that could shape future research in the field Ideal for researchers in machine learning, specifically those involved with evolutionary computation.

• Part I Concepts and Issues;
• Chapter 1 Foundations and Ideas;
• Chapter 2 Principal Model Types;
• Chapter 3 Approaches to Model Building; Part II Fuzzy Systems;
• Chapter 4 Fundamental Concepts of Fuzzy Logic;
• Chapter 5 Fundamental Concepts of Fuzzy Systems;
• Chapter 6 Fuzzy SQL and Intelligent Queries;
• Chapter 7 Fuzzy Clustering;
• Chapter 8 Fuzzy Rule Induction; Part III Evolutionary Strategies;
• Chapter 9 Fundamental Concepts of Genetic Algorithms;
• Chapter 10 Genetic Resource Scheduling Optimization;
• Chapter 11 Genetic Tuning of Fuzzy Models; Index.

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As you'll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems. You don't need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed inst.

• Differential Evolution Research
• Trends and Open Questions.- Eliminating Drift Bias from the Differential Evolution Algorithm.- An Analysis of the Control Parameters' Adaptation in DE.- Stopping Criteria for Differential Evolution in Constrained Single-Objective Optimization.- Constrained Optimization by e Constrained Differential Evolution with Dynamic e-level Control.- Opposition-Based Differential Evolution.- Multi-Objective Optimization using Differential Evolution: A Survey of the State-of-the-Art.- A Review of Major Application Areas of Differential Evolution.- The Differential Evolution Algorithm as Applied to Array Antennas and Imaging.- Applications of Differential Evolution in Power System Optimization.- Self-adaptive Differential Evolution Using Chaotic Local Search for Solving Power Economic Dispatch with Nonsmooth Fuel Cost Function.- An Adaptive Differential Evolution Algorithm with Opposition-Based Mechanisms, Applied to the Tuning of a Chess Program.- Differential Evolution for the Offline and Online Optimization of Fed-Batch Fermentation Processes.- Worst case analysis of control law for Re-Entry Vehicles using hybrid differential evolution.
• (source: Nielsen Book Data)

Differential evolution is arguably one of the hottest topics in today's computational intelligence research. This book seeks to present a comprehensive study of the state of the art in this technology and also directions for future research. The fourteen chapters of this book have been written by leading experts in the area. The first seven chapters focus on algorithm design, while the last seven describe real-world applications. Chapter 1 introduces the basic differential evolution (DE) algorithm and presents a broad overview of the field. Chapter 2 presents a new, rotationally invariant DE algorithm. The role of self-adaptive control parameters in DE is investigated in Chapter 3. Chapters 4 and 5 address constrained optimization; the former develops suitable stopping conditions for the DE run, and the latter presents an improved DE algorithm for problems with very small feasible regions. A novel DE algorithm, based on the concept of 'opposite' points, is the topic of Chapter 6. Chapter 7 provides a survey of multi-objective differential evolution algorithms. A review of the major application areas of differential evolution is presented in Chapter 8. Chapter 9 discusses the application of differential evolution in two important areas of applied electro magnetics. Chapters 10 and 11 focus on applications of hybrid DE algorithms to problems in power system optimization. Chapter 12 applies the DE algorithm to computer chess. The use of DE to solve a problem in bioprocess engineering is discussed in Chapter 13. Chapter 14 describes the application of hybrid differential evolution to a problem in control engineering.
(source: Nielsen Book Data)

• Intro; Preface; Contents; Introduction; 1 Exploring Grouping Problems in Industry; 1.1 Introduction; 1.2 Identifying Grouping Problems in Industry; 1.2.1 Cell Formation in Manufacturing Systems; 1.2.2 Assembly Line Balancing; 1.2.3 Job Shop Scheduling; 1.2.4 Vehicle Routing Problem; 1.2.5 Home Healthcare Worker Scheduling; 1.2.6 Bin Packing Problem; 1.2.7 Task Assignment Problem; 1.2.8 Modular Product Design; 1.2.9 Group Maintenance Planning; 1.2.10 Order Batching; 1.2.11 Team Formation; 1.2.12 Earnings Management; 1.2.13 Economies of Scale; 1.2.14 Timetabling

• 1.2.15 Student Grouping for Cooperative Learning1.2.16 Other Problems; 1.3 Extant Modeling Approaches to Grouping Problems; 1.4 Structure of the Book; References; 2 Complicating Features in Industrial Grouping Problems; 2.1 Introduction; 2.2 Research Methodology; 2.3 Research Findings; 2.4 Complicating Features; 2.4.1 Model Conceptualization; 2.4.2 Myriad of Constraints; 2.4.2.1 Intra-Group Relationship; 2.4.2.2 Inter-Group Relationship; 2.4.2.3 Group Size Limits; 2.4.2.4 Grouping Limit; 2.4.3 Fuzzy Management Goals; 2.4.4 Computational Complexity; 2.5 Suggested Solution Approaches

• 2.6 SummaryReferences; Grouping Genetic Algorithms; 3 Grouping Genetic Algorithms: Advances for Real-World Grouping Problems; 3.1 Introduction; 3.2 Grouping Genetic Algorithm: An Overview; 3.2.1 Group Encoding; 3.3 Crossover; 3.3.1 Mutation; 3.3.2 Inversion; 3.4 Grouping Genetic Algorithms: Advances and Innovations; 3.4.1 Group Encoding Strategies; 3.4.1.1 Encoding Strategy 1; 3.4.1.2 Encoding Strategy 2; 3.4.2 Initialization; 3.4.2.1 User-Generated Seeds; 3.4.2.2 Random Generation; 3.4.2.3 Constructive Heuristics; 3.4.3 Selection Strategies; 3.4.3.1 Stochastic Sampling Without Replacement

• 3.4.4 Rank-Based Wheel Selection Strategy3.4.5 Crossover Strategies; 3.4.5.1 Two-Point Group Crossover; 3.4.5.2 Adaptive Crossover; 3.4.6 Mutation Strategies; 3.4.6.1 Swap Mutation; 3.4.6.2 Split Mutation; 3.4.6.3 Merge Mutation; 3.4.6.4 Adaptive Mutation; 3.4.7 Inversion; 3.4.7.1 Two-Point Inversion; 3.4.7.2 Single-Point Inversion; 3.4.7.3 Adaptive Inversion; 3.4.8 Replacement Strategies; 3.4.9 Termination Strategies; 3.4.9.1 Iteration Count (ItCount); 3.4.9.2 Iterations Without Improvement (ItWithoutImp); 3.4.9.3 Hybrid Criteria; 3.5 Application Areas; 3.6 Summary; References

• 4 Fuzzy Grouping Genetic Algorithms: Advances for Real-World Grouping Problems4.1 Introduction; 4.2 Preliminaries: Fuzzy Logic Control; 4.3 Fuzzy Grouping Genetic Algorithms: Advances and Innovations; 4.3.1 FGGA Coding Scheme; 4.3.2 Initialization; 4.3.3 Fuzzy Fitness Evaluation; 4.3.3.1 Multifactor Evaluation; 4.3.3.2 Fuzzy Goal-Oriented Fitness Evaluation; 4.3.4 Fuzzy Genetic Operators; 4.3.4.1 Fuzzy Controlled Genetic Parameters; Convergence Measure; Diversity Measure; Crossover Probability; Mutation Probability; Inversion Probability; 4.3.4.2 Fuzzy Logic Controlled Crossover

This book presents advances and innovations in grouping genetic algorithms, enriched with new and unique heuristic optimization techniques. These algorithms are specially designed for solving industrial grouping problems where system entities are to be partitioned or clustered into efficient groups according to a set of guiding decision criteria. Examples of such problems are: vehicle routing problems, team formation problems, timetabling problems, assembly line balancing, group maintenance planning, modular design, and task assignment. A wide range of industrial grouping problems, drawn from diverse fields such as logistics, supply chain management, project management, manufacturing systems, engineering design and healthcare, are presented. Typical complex industrial grouping problems, with multiple decision criteria and constraints, are clearly described using illustrative diagrams and formulations. The problems are mapped into a common group structure that can conveniently be used as an input scheme to specific variants of grouping genetic algorithms. Unique heuristic grouping techniques are developed to handle grouping problems efficiently and effectively. Illustrative examples and computational results are presented in tables and graphs to demonstrate the efficiency and effectiveness of the algorithms. Researchers, decision analysts, software developers, and graduate students from various disciplines will find this in-depth reader-friendly exposition of advances and applications of grouping genetic algorithms an interesting, informative and valuable resource.

• Section 1 - Rule Discovery. Population Dynamics of Genetic Algorithms. Approximating Value Functions in Classifier Systems. Two Simple Learning Classifier Systems. Computational Complexity of the XCS Classifier System. An Analysis of Continuous-Valued Representations for Learning Classifier Systems.-
• Section 2 - Credit Assignment. Reinforcement Learning: a Brief Overview. A Mathematical Framework for Studying Learning Classifier Systems. Rule Fitness and Pathology in Learning Classifier Systems. Learning Classifier Systems: A Reinforcement Learning Perspective. Learning Classifier Systems with Convergence and Generalization.-
• Section 3 - Problem Characterization. On the Classification of Maze Problems. What Makes a Problem Hard?
• (source: Nielsen Book Data)

This volume brings together recent theoretical work in Learning Classifier Systems (LCS), which is a Machine Learning technique combining Genetic Algorithms and Reinforcement Learning. It includes self-contained background chapters on related fields (reinforcement learning and evolutionary computation) tailored for a classifier systems audience and written by acknowledged authorities in their area - as well as a relevant historical original work by John Holland.
(source: Nielsen Book Data)

• Optical configurations for imaging spectrometers / X. Prieto-Blanco [and others]
• Remote sensing data compression / Joan Serra-Sagristà, Francesc Aulí-Llinàs
• A multiobjective evolutionary algorithm for hyperspectral image watermarking / D. Sal, M. Graña
• Architecture and services for computational intelligence in remote sensing / Sergio D'Elia [and others]
• On content-based image retrieval systems for hyperspectral remote sensing images / Miguel A. Veganzones, José Orlando Maldenado, Manuel Graña
• An analytical approach to the optimal deployment of wireless sensor networks / J. Vales-Alonso [and others]
• Parallel spatial-spectral processing of hyperspectral images / Antonio J. Plaza
• Parallel classification of hyperspectral images using neural networks / Javier Plaza [and others]
• Positioning weather systems from remote sensing data using genetic algorithms / Wong Ka Yan, Yip Chi Lap
• A computation reduced technique to primitive feature extraction for image information mining via the use of wavelets / Vijay P. Shah [and others]
• Neural networks for land cover applications / Fabio Pacifici [and others]
• Information extraction for forest fire management / Andrea Pelizzari Ricardo Armas Goncalves, Mario Caetano
• Automatic preporocessing and classification system for high resolution ultra and hyperspectral images / Abraham Prieto [and others]
• Using Gaussian synapse ANNs for hyperspectral image segmentation and endmember extraction / R.J. Duro, F. Lopez-Pena, J.L. Crespo
• Unsupervised change detection from multichannel SAR data by Markov random fields / Sebastiano B. Serpico, Gabriele Moser.

This book is a composition of diverse points of view regarding the application of Computational Intelligence techniques and methods into Remote Sensing data and problems. It is the general consensus that classi?cation, and related data processing, and global optimization methods are the main topics of Compu- tional Intelligence. Global random optimization algorithms appear in this book, such as the Simulated Annealing in chapter 6 and the Genetic Algorithms p- posedinchapters3and9. Muchofthecontentsofthe bookaredevotedto image segmentationandrecognition, using diversetoolsfromregionsofComputational Intelligence, ranging from Arti?cial Neural Networks to Markov Random Field modelling. However, there are some fringe topics, such the parallel implem- tation of some algorithms or the image watermarking that make evident that thefrontiersbetweenComputationalIntelligenceandneighboringcomput ational disciplines are blurred and the fences run low and full of holes in many places. The book starts with a review of the current designs of hyperspectral sensors, more appropriately named Imaging Spectrometers. Knowing the shortcomings and advantages of the diverse designs may condition the results on some app- cations of Computational Intelligence algorithms to the processing and und- standing of them Remote Sensing images produced by these sensors. Then the book contentsmovesinto basic signalprocessing techniquessuch ascompression and watermarking applied to remote sensing images. With the huge amount of remotesensinginformationandtheincreasingrateatwhichitisbeingprod uced, itseems only naturalthatcompressiontechniques willleapintoa prominentrole in the near future, overcoming the resistances of the users against uncontrolled manipulation of "their" data. Watermarkingis the way to address issues of o- ership authentication in digital contents.
(source: Nielsen Book Data)