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• An introduction to evolutionary computation
• evolutionary algorithms as search algorithms
• theoretical analysis of evolutionary algorithms
• advanced search operators in evolutionary algorithms
• parallel evolutionary algorithms
• a comparison of simulated annealing and an evolutionary algorithm on traveling salesman problems
• power system design and management by evolutionary algorithms
• telecommunications network design and management by evolutionary algorithms
• an optimization tool based on evolutionary algorithms
• the evolution of artificial neural network architectures
• an experimental study of generalization in evolutionary learning
• an evolutionary approach to the N-person prisoner's dilemma game
• automated design and generalisation of heuristics
• high-order credit assignment in classifier systems.
• (source: Nielsen Book Data)

Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation. This book is devoted to the theory and application of evolutionary computation. It is a self-contained volume which covers both introductory material and selected advanced topics. The book can roughly be divided into two major parts: the introductory one and the one on selected advanced topics. Each part consists of several chapters which present an in-depth discussion of selected topics. A strong connection is established between evolutionary algorithms and traditional search algorithms. This connection enables us to incorporate ideas in more established fields into evolutionary algorithms. The book is aimed at a wide range of readers. It does not require previous exposure to the field since introductory material is included. It will be of interest to anyone who is interested in adaptive optimization and learning. People in computer science, artificial intelligence, operations research, and various engineering fields will find it particularly interesting.
(source: Nielsen Book Data)

• Part 1: Fascinating Evolution
• Part 2: Evolutionary Computation
• Part 3: If Darwin was a Programmer
• Part 4: Evolution of Developmental Programs.
• (source: Nielsen Book Data)

An essential capacity of intelligence is the ability to learn. An artificially intelligent system that could learn would not have to be programmed for every eventuality; it could adapt to its changing environment and conditions just as biological systems do. Illustrating Evolutionary Computation with Mathematica introduces evolutionary computation to the technically savvy reader who wishes to explore this fascinating and increasingly important field. Unique among books on evolutionary computation, the book also explores the application of evolution to developmental processes in nature, such as the growth processes in cells and plants. If you are a newcomer to the evolutionary computation field, an engineer, a programmer, or even a biologist wanting to learn how to model the evolution and coevolution of plants, this book will provide you with a visually rich and engaging account of this complex subject.
(source: Nielsen Book Data)

• Part 1 - Setting the Stage
• Chapter 1 - Intelligent Machines: Imitating Life
• Chapter 2 - Deep Blue: A Triumph of AI?
• Chapter 3 - Building An Artificial Brain
• Chapter 4 - Evolutionary Computation: Putting Nature to Work
• Chapter 5 - Blue Hawaii: Natural Selection
• Chapter 6 - Checkers
• Chapter 7 - Chinook: The Man-machine Checkers Champion
• Chapter 8 - Samuel's Learning Machine
• Chapter 9 - The Samuel-Newell Challenge
• Part 2 - The Making of Blondie
• Chapter 10 - Evolving in the Checkers Environment
• Chapter 11 - In The Zone
• Chapter 12 - A Repeat Performance
• Chapter 13 - A New Dimension
• Chapter 14 - Letting the Genie Out of the Bottle
• Chapter 15 - Blondie24 Epilogue: The Future of Artificial Intelligence Appendix: Your Honor, I Object! Notes Index About the Author.
• (source: Nielsen Book Data)

Blondie24 tells the story of a computer that taught itself to play checkers far better than its creators ever could by using a program that emulated the basic principles of Darwinian evolution--random variation and natural selection-- to discover on its own how to excel at the game. Unlike Deep Blue, the celebrated chess machine that beat Garry Kasparov, the former world champion chess player, this evolutionary program didn't have access to strategies employed by human grand masters, or to databases of moves for the endgame moves, or to other human expertise about the game of chekers. With only the most rudimentary information programmed into its "brain, " Blondie24 (the program's Internet username) created its own means of evaluating the complex, changing patterns of pieces that make up a checkers game by evolving artificial neural networks---mathematical models that loosely describe how a brain works. It's fitting that Blondie24 should appear in 2001, the year when we remember Arthur C. Clarke's prediction that one day we would succeed in creating a thinking machine. In this compelling narrative, David Fogel, author and co-creator of Blondie24, describes in convincing detail how evolutionary computation may help to bring us closer to Clarke's vision of HAL. Along the way, he gives readers an inside look into the fascinating history of AI and poses provocative questions about its future.
(source: Nielsen Book Data)

• 1 Evolutionary Computation in Economics and Finance: An Overview of the Book.- 2 Playing Games with Genetic Algorithms.- 3 Genetic Algorithm Learning and Economic Evolution.- 4 Using Symbolic Regression to Infer Strategies from Experimental Data.- 5 The Efficiency of an Artificial Double Auction Stock Market with Neural Learning Agents.- 6 On AIE-ASM: Software to Simulate Artificial Stock Markets with Genetic Programming.- 7 Exchange Rate Volatility in the Artificial Foreign Exchange Market.- 8 Using an Artificial Market Approach to Analyze Exchange Rate Scenarios.- 9 Emulating Trade in Emissions Permits: An Application of Genetic Algorithms.- 10 Cooperative Computation with Market Mechanism.- 11 Hysteresis in an Evolutionary Labor Market with Adaptive Search.- 12 Computable Learning, Neural Networks and Institutions.- 13 On Two Types of GA-Learning.- 14 Evolutionary Computation and Economic Models: Sensitivity and Unintended Consequences.- 15 Tinkering with Genetic Algorithms: Forecasting and Data Mining in Finance and Economics.- 16 Forecasting Ability But No Profitability: An Empirical Evaluation of Genetic Algorithm-Optimised Technical Trading Rules.- 17 Evolutionary Induction of Trading Models.- 18 Optimizing Technical Trading Strategies with Split Search Genetic Algorithms.- 19 GP Forecasts of Stock Prices for Profitable Trading.- 20 Option Pricing Via Genetic Programming.- 21 Evolutionary Computation in Option Pricing: Determining Implied Volatilities Based on American Put Options.- 22 Evolutionary Computation in Economics and Finance: A Bibliography.
• (source: Nielsen Book Data)

After a decade's development, evolutionary computation (EC) proves to be a powerful tool kit for economic analysis. While the demand for this equipment is increasing, there is no volume exclusively written for economists. This volume for the first time helps economists to get a quick grasp on how EC may support their research. A comprehensive coverage of the subject is given, that includes the following three areas: game theory, agent-based economic modelling and financial engineering. Twenty leading scholars from each of these areas contribute a chapter to the volume. The reader will find himself treading the path of the history of this research area, from the fledgling stage to the burgeoning era. The results on games, labour markets, pollution control, institution and productivity, financial markets, trading systems design and derivative pricing, are new and interesting for different target groups. The book also includes informations on web sites, conferences, and computer software.
(source: Nielsen Book Data)

• An Introduction to Multi-Objective Evolutionary Algorithms and Their Applications
• Optimal Design of Industrial Electromagnetic Devices: A Multiobjective Evolutionary Approach
• Using a Particle Swarm Optimizer with a Multi-Objective Selection Scheme to Design Combinational Logic Circuits
• Automatic Control System Design via a Multiobjective Evolutionary Algorithm
• Evolutionary Multi-Objective Optimization of Trusses
• A Multi-Objective Evolutionary Algorithm for the Covering Tour Problem
• Multiobjective Aerodynamic Design and Visualization of Supersonic Wings by Using Adaptive Range Multiobjective Genetic Algorithms
• Mutli-Objective Spectroscopic Data Analysis of Inertial Confinement Fusion Implosion Cores: Plasma Gradient Determination
• On Machine Learning with Multiobjective Genetic Optimization
• and other papers.
• (source: Nielsen Book Data)

This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs). The topics discussed serve to promote a wider understanding as well as the use of MOEAs, the aim being to find good solutions for high-dimensional real-world design applications. The book contains a large collection of MOEA applications from many researchers, and thus provides the practitioner with detailed algorithmic direction to achieve good results in their selected problem domain.
(source: Nielsen Book Data)

• Evolutionary Algorithms for Data Mining and Knowledge Discovery.- Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining.- GAP: Constructing and Selecting Features with Evolutionary Computing.- Multi-Agent Data Mining using Evolutionary Computing.- A Rule Extraction System with Class-Dependent Features.- Knowledge Discovery in Data Mining via an Evolutionary Algorithm.- Diversity and Neuro-Ensemble.- Unsupervised Niche Clustering: Discovering an Unknown Number of Clusters in Noisy Data Sets.- Evolutionary Computation in Intelligent Network Management.- Genetic Programming in Data Mining for Drug Discovery.- Microarray Data Mining with Evolutionary Computation.- An Evolutionary Modularized Data Mining Mechanism for Financial Distress Forecasts.
• (source: Nielsen Book Data)

Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation).
(source: Nielsen Book Data)

• Preface to the Third Edition.Preface to the Second Edition.Preface to the First Edition.1 Defining Artificial Intelligence.1.1 Background.1.2 The Turing Test.1.3 Simulation of Human Expertise.1.3.1 Samuel's Checker Program.1.3.2 Chess Programs.1.3.3 Expert Systems.1.3.4 A Criticism of the Expert Systems or Knowledge-Based Approach.1.3.5 Fuzzy Systems.1.3.6 Perspective on Methods Employing Specific Heuristics.1.4 Neural Networks.1.5 Definition of Intelligence.1.6 Intelligence, the Scientific Method, and Evolution.1.7 Evolving Artificial Intelligence.References.
• Chapter 1 Exercises.2 Natural Evolution.2.1 The Neo-Darwinian Paradigm.2.2 The Genotype and the Phenotype: The Optimization of Behavior.2.3 Implications of Wright's Adaptive Topography: Optimization Is Extensive Yet Incomplete.2.4 The Evolution of Complexity: Minimizing Surprise.2.5 Sexual Reproduction.2.6 Sexual Selection.2.7 Assessing the Beneficiary of Evolutionary Optimization.2.8 Challenges to Neo-Darwinism.2.8.1 Neutral Mutations and the Neo-Darwinian Paradigm.2.8.2 Punctuated Equilibrium.2.9 Summary.References.
• Chapter 2 Exercises.3 Computer Simulation of Natural Evolution.3.1 Early Speculations and Specific Attempts.3.1.1 Evolutionary Operation.3.1.2 A Learning Machine.3.2 Artificial Life.3.3 Evolutionary Programming.3.4 Evolution Strategies.3.5 Genetic Algorithms.3.6 The Evolution of Evolutionary Computation.References.
• Chapter 3 Exercises.4 Theoretical and Empirical Properties of Evolutionary Computation.4.1 The Challenge.4.2 Theoretical Analysis of Evolutionary Computation.4.2.1 The Framework for Analysis.4.2.2 Convergence in the Limit.4.2.3 The Error of Minimizing Expected Losses in Schema Processing.4.2.3.1 The Two-Armed Bandit Problem.4.2.3.2 Extending the Analysis for "Optimally" Allocating Trials.4.2.3.3 Limitations of the Analysis.4.2.4 Misallocating Trials and the Schema Theorem in the Presence of Noise.4.2.5 Analyzing Selection.4.2.6 Convergence Rates for Evolutionary Algorithms.4.2.7 Does a Best Evolutionary Algorithm Exist?4.3 Empirical Analysis.4.3.1 Variations of Crossover.4.3.2 Dynamic Parameter Encoding.4.3.3 Comparing Crossover to Mutation.4.3.4 Crossover as a Macromutation.4.3.5 Self-Adaptation in Evolutionary Algorithms.4.3.6 Fitness Distributions of Search Operators.4.4 Discussion.References.
• Chapter 4 Exercises.5 Intelligent Behavior.5.1 Intelligence in Static and Dynamic Environments.5.2 General Problem Solving: Experiments with Tic-Tac-Toe.5.3 The Prisoner's Dilemma: Coevolutionary Adaptation.5.3.1 Background.5.3.2 Evolving Finite-State Representations.5.4 Learning How to Play Checkers without Relying on Expert Knowledge.5.5 Evolving a Self-Learning Chess Player.5.6 Discussion.References.
• Chapter 5 Exercises.6 Perspective.6.1 Evolution as a Unifying Principle of Intelligence.6.2 Prediction and the Languagelike Nature of Intelligence.6.3 The Misplaced Emphasis on Emulating Genetic Mechanisms.6.4 Bottom-Up Versus Top-Down.6.5 Toward a New Philosophy of Machine Intelligence.References.
• Chapter 6 Exercises.Glossary.Index.About the Author.
• (source: Nielsen Book Data)

This third edition provides the latest tools and techniques that enable computers to learn. The third edition of this internationally acclaimed publication provides the latest theory and techniques for using simulated evolution to achieve machine intelligence. As a leading advocate for evolutionary computation, the author has successfully challenged the traditional notion of artificial intelligence, which essentially programs human knowledge fact by fact, but does not have the capacity to learn or adapt as evolutionary computation does. Readers gain an understanding of the history of evolutionary computation, which provides a foundation for the author's thorough presentation of the latest theories shaping current research. Balancing theory with practice, the author provides readers with the skills they need to apply evolutionary algorithms that can solve many of today's intransigent problems by adapting to new challenges and learning from experience.Several examples are provided that demonstrate how these evolutionary algorithms learn to solve problems. In particular, the author provides a detailed example of how an algorithm is used to evolve strategies for playing chess and checkers. As readers progress through the publication, they gain an increasing appreciation and understanding of the relationship between learning and intelligence. Readers familiar with the previous editions will discover much new and revised material that brings the publication thoroughly up to date with the latest research, including the latest theories and empirical properties of evolutionary computation. The third edition also features new knowledge-building aids.Readers will find a host of new and revised examples. New questions at the end of each chapter enable readers to test their knowledge. Intriguing assignments that prepare readers to manage challenges in industry and research have been added to the end of each chapter as well. This is a must-have reference for professionals in computer and electrical engineering; it provides them with the very latest techniques and applications in machine intelligence. With its question sets and assignments, the publication is also recommended as a graduate-level textbook.
(source: Nielsen Book Data)

• Parallel Evolutionary Optimization.- A Model for Parallel Operators in Genetic Algorithms.- Parallel Evolutionary Multiobjective Optimization.- Parallel Hardware for Genetic Algorithms.- A Reconfigurable Parallel Hardware for Genetic Algorithms.- Reconfigurable Computing and Parallelism for Implementing and Accelerating Evolutionary Algorithms.- Distributed Evolutionary Computation.- Performance of Distributed GAs on DNA Fragment Assembly.- On Parallel Evolutionary Algorithms on the Computational Grid.- Parallel Evolutionary Algorithms on Consumer-Level Graphics Processing Unit.- Parallel Particle Swarm Optimization.- Intelligent Parallel Particle Swarm Optimization Algorithms.- Parallel Ant Colony Optimization for 3D Protein Structure Prediction using the HP Lattice Model.
• (source: Nielsen Book Data)

This book focuses on the aspects related to the parallelization of evolutionary computations, such as parallel genetic operators, parallel fitness evaluation, distributed genetic algorithms, and parallel hardware implementations, as well as on their impact on several applications. It offers a wide spectrum of sample works developed in leading research about parallel implementations of efficient techniques at the heart of computational intelligence.
(source: Nielsen Book Data)

• Linking Entropy to Estimation of Distribution Algorithms.- Entropy-based Convergence Measurement in Discrete Estimation of Distribution Algorithms.- Real-coded Bayesian Optimization Algorithm.- The CMA Evolution Strategy: A Comparing Review.- Estimation of Distribution Programming: EDA-based Approach to Program Generation.- Multi-objective Optimization with the Naive
• ID A.- A Parallel Island Model for Estimation of Distribution Algorithms.- GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm.- Bayesian Classifiers in Optimization: An EDA-like Approach.- Feature Ranking Using an EDA-based Wrapper Approach.- Learning Linguistic Fuzzy Rules by Using Estimation of Distribution Algorithms as the Search Engine in the COR Methodology.- Estimation of Distribution Algorithm with 2-opt Local Search for the Quadratic Assignment Problem.
• (source: Nielsen Book Data)

Estimation of Distribution Algorithms (EDAs) are a set of algorithms in the Evolutionary Computation (EC) field characterized by the use of explicit probability distributions in optimization. Contrarily to other EC techniques such as the broadly known Genetic Algorithms (GAs) in EDAs, the crossover and mutation operators are substituted by the sampling of a distribution previously learnt from the selected individuals. EDAs have experienced a high development that has transformed them into an established discipline within the EC field. This book attracts the interest of new researchers in the EC field as well as in other optimization disciplines, and that it becomes a reference for all of us working on this topic. The twelve chapters of this book can be divided into those that endeavor to set a sound theoretical basis for EDAs, those that broaden the methodology of EDAs and finally those that have an applied objective.
(source: Nielsen Book Data)

• Multiobjective Evolutionary Optimization.- A New Evolutionary Decision Theory for Many-Objective Optimization Problems.- A Multi-Objective Genetic Algorithm Based on Density.- Interplanetary Trajectory Optimization with Swing-Bys Using Evolutionary Multi-objective Optimization.- A Hybrid Evolutionary Multi-objective and SQP Based Procedure for Constrained Optimization.- Study on Application of Multi-Objective Differential Evolution Algorithm in Space Rendezvous.- The Multi-objective ITO Algorithms.- An Evolutionary Algorithm for Dynamic Multi-Objective TSP.- The Construction of Dynamic Multi-objective Optimization Test Functions.- An Effective Dynamical Multi-objective Evolutionary Algorithm for Solving Optimization Problems with High Dimensional Objective Space.- Evolutionary Algorithms and Operators.- Operator Adaptation in Evolutionary Programming.- A Comparison of GAs Using Penalizing Infeasible Solutions and Repairing Infeasible Solutions on Average Capacity Knapsack.- About the Limit Behaviors of the Transition Operators Associated with EAs.- Differential Evolution Algorithm Based on Simulated Annealing.- A Novel Memetic Algorithm for Global Optimization Based on PSO and SFLA.- Building on Success in Genetic Programming: Adaptive Variation and Developmental Evaluation.- A Granular Evolutionary Algorithm Based on Cultural Evolution.- A Self-adaptive Mutations with Multi-parent Crossover Evolutionary Algorithm for Solving Function Optimization Problems.- Evolutionary Optimization.- A Quantum Genetic Simulated Annealing Algorithm for Task Scheduling.- Optimized Research of Resource Constrained Project Scheduling Problem Based on Genetic Algorithms.- An Evolutionary Agent System for Mathematical Programming.- Agent-Based Coding GA and Application to Combat Modeling and Simulation.- A Two-Stage Genetic Algorithm for the Multi-multicast Routing.- A Novel Lower-Dimensional-Search Algorithm for Numerical Optimization.- Performance Evaluation of Three Kinds of Quantum Optimization.- An Efficient Multilevel Algorithm for Inverse Scattering Problem.- Evolutionary Learning.- A New Evolutionary Neural Network and Its Application for the Extraction of Vegetation Anomalies.- Dynamic System Evolutionary Modeling: The Case of SARS in Beijing.- An Tableau Automated Theorem Proving Method Using Logical Reinforcement Learning.- Gene Expression Programming with DAG Chromosome.- Neural Networks.- Zhang Neural Network for Online Solution of Time-Varying Sylvester Equation.- A Global Optimization Algorithm Based on Novel Interval Analysis for Training Neural Networks.- Approximate Interpolation by Neural Networks with the Inverse Multiquadric Functions.- Decomposition Mixed Pixel of Remote Sensing Image Based on Tray Neural Network Model.- A Novel Kernel Clustering Algorithm Based Selective Neural Network Ensemble Model for Economic Forecasting.- An Evolutionary Neural Network Based Tracking Control of a Human Arm in the Sagittal Plane.- Ant Colony, Particle Swarm Optimization and Artificial Immune Systems.- New Ant Colony Optimization for Optimum Multiuser Detection Problem in DS-CDMA Systems.- A Fast Particle Swarm Optimization Algorithm with Cauchy Mutation and Natural Selection Strategy.- Fast Multi-swarm Optimization with Cauchy Mutation and Crossover Operation.- Particle Swarm Optimization Using Levy Probability Distribution.- Re-diversification Based Particle Swarm Algorithm with Cauchy Mutation.- An Improved Multi-Objective Particle Swarm Optimization Algorithm.- Dynamic Population Size Based Particle Swarm Optimization.- An Improved Particle Swarm Optimization for Data Streams Scheduling on Heterogeneous Cluster.- A Steepest Descent Evolution Immune Algorithm for Multimodal Function Optimization.- A Hybrid Clonal Selection Algorithm Based on Multi-parent Crossover and Chaos Search.- Pattern Recognition.- Spatial Clustering Method Based on Cloud Model and Data Field.- Diversity Analysis of Information Pattern and Information Clustering Algorithm.- Instant Message Clustering Based on Extended Vector Space Model.- Continuous K-Nearest Neighbor Queries for Moving Objects.- Texture Classification of Aerial Image Based on Bayesian Networks with Hidden Nodes.- Human Motion Recognition Based on Hidden Markov Models.- Data Mining.- Parameter Setting for Evolutionary Latent Class Clustering.- Automatic Data Mining by Asynchronous Parallel Evolutionary Algorithms.- Texture Image Retrieval Based on Contourlet Coefficient Modeling with Generalized Gaussian Distribution.- Heterogeneous Spatial Data Mining Based on Grid.- A Clustering Scheme for Large High-Dimensional Document Datasets.- Intelligent Systems.- Self-tuning PID Control of Hydro-turbine Governor Based on Genetic Neural Networks.- Adaptive Rate Selection Scheme Based on Intelligent Learning Algorithm in Wireless LANs.- The Research on Generic Project Risk Element Network Transmission Parallel Computing Model.- On the Performance of Metamodel Assisted MOEA/D.- A High Precision OGC Web Map Service Retrieval Based on Capability Aware Spatial Search Engine.- Analysis of the Performance of Balance of Digital Multi-value Based on Chebyshev Chaotic Sequence.- The Transformation Between Fuzzy Cognitive Maps and a Class of Simplified Dynamical Cognitive Networks.- Evolutionary Design.- Cryptanalysis of Two-Round DES Using Genetic Algorithms.- A Novel Artistic Image Generation Technique: Making Relief Effects Through Evolution.- Data Genome: An Abstract Model for Data Evolution.- Intrinsic Evolution of Frequency Splitter with a New Analog EHW Platform.- Towards the Role of Heuristic Knowledge in EA.- Using Instruction Matrix Based Genetic Programming to Evolve Programs.- Fuzzy Pattern Rule Induction for Information Extraction.- An Orthogonal and Model Based Multiobjective Genetic Algorithm for LEO Regional Satellite Constellation Optimization.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Second International Symposium on Intelligence Computation and Applications, ISICA 2007, held in Wuhan, China, in September 2007. The 71 revised full papers cover such topics as evolutionary computation, evolutionary learning, neural networks, swarms, pattern recognition, and data mining.
(source: Nielsen Book Data)

• to Evolutionary Computing in System Design.- Evolutionary Neuro-Fuzzy Systems and Applications.- Evolution of Fuzzy Controllers and Applications.- A Neuro-Genetic Framework for Multi-Classifier Design: An Application to Promoter Recognition in DNA Sequences.- Evolutionary Grooming of Traffic in WDM Optical Networks.- EPSO: Evolutionary Particle Swarms.- Design of Type-Reduction Strategies for Type-2 Fuzzy Logic Systems using Genetic Algorithms.- Designing a Recurrent Neural Network-based Controller for Gyro-Mirror Line-of-Sight Stabilization System using an Artificial Immune Algorithm.- Distributed Problem Solving using Evolutionary Learning in Multi-Agent Systems.- Evolutionary Computing within Grid Environment.- Application of Evolutionary Game Theory to Wireless Mesh Networks.- Applying Hybrid Multiobjective Evolutionary Algorithms to the Sailor Assignment Problem.- Evolutionary Techniques Applied to Hardware Optimization Problems: Test and Verification of Advanced Processors.
• (source: Nielsen Book Data)

Evolutionary computing paradigms offer robust and powerful adaptive search mechanisms for system design. This book's thirteen chapters cover a wide area of topics in evolutionary computing and applications, including an introduction to evolutionary computing in system design; evolutionary neuro-fuzzy systems; and evolution of fuzzy controllers. The book will be useful to researchers in intelligent systems with interest in evolutionary computing, as well as application engineers and system designers.
(source: Nielsen Book Data)

• Overall Design and Fundations.- Hybrid Multi-agent Systems: Integrating Swarming and BDI Agents.- An Analysis and Design Concept for Self-organization in Holonic Multi-agent Systems.- Design Patterns for Decentralised Coordination in Self-organising Emergent Systems.- Measuring Stigmergy: The Case of Foraging Ants.- Algorithms and Techniques.- Dynamic Decentralized Any-Time Hierarchical Clustering.- Behaviosites: A Novel Paradigm for Affecting Distributed Behavior.- Applications.- Programming Modular Robots with the TOTA Middleware.- ASOS: An Adaptive Self-organizing Protocol for Surveillance and Routing in Sensor Networks.- Towards the Control of Emergence by the Coordination of Decentralized Agent Activity for the Resource Sharing Problem.- Self-organization and Evolutionary Computing.- Reinforcement Learning for Online Control of Evolutionary Algorithms.- Greedy Cheating Liars and the Fools Who Believe Them.- Evolution and Hypercomputing in Global Distributed Evolvable Virtual Machines Environment.- A Decentralised Car Traffic Control System Simulation Using Local Message Propagation Optimised with a Genetic Algorithm.
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-proceedings of the 4th International Workshop on Engineering Self-Organising Applications, ESOA 2006, held in Hakodate, Japan in May 2006. This was an associated event of AAMAS 2006, the 5th International Joint Conference on Autonomous Agents and Multi-Agent Systems. The seven full papers presented together with six invited papers were carefully selected for inclusion in the book.
(source: Nielsen Book Data)

• Basic Concepts.- MOP Evolutionary Algorithm Approaches.- MOEA Local Search and Coevolution.- MOEA Test Suites.- MOEA Testing and Analysis.- MOEA Theory and Issues.- Applications.- MOEA Parallelization.- Multi-Criteria Decision Making.- Alternative Metaheuristics.
• (source: Nielsen Book Data)

This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.
(source: Nielsen Book Data)

• A New Local Search Algorithm for the DNA Fragment Assembly Problem.- A Hybrid Immune-Based System for the Protein Folding Problem.- A Genetic Algorithm for the Resource Renting Problem with Minimum and Maximum Time Lags.- A Probabilistic Beam Search Approach to the Shortest Common Supersequence Problem.- Genetic Algorithms for Word Problems in Partially Commutative Groups.- A GRASP and Branch-and-Bound Metaheuristic for the Job-Shop Scheduling.- Reducing the Size of Traveling Salesman Problem Instances by Fixing Edges.- Iterated k-Opt Local Search for the Maximum Clique Problem.- Accelerating Local Search in a Memetic Algorithm for the Capacitated Vehicle Routing Problem.- Evolutionary Algorithms for Real-World Instances of the Automatic Frequency Planning Problem in GSM Networks.- A New Metaheuristic for the Vehicle Routing Problem with Split Demands.- Generation of Tree Decompositions by Iterated Local Search.- Edge Assembly Crossover for the Capacitated Vehicle Routing Problem.- Tackling the Container Loading Problem: A Hybrid Approach Based on Integer Linear Programming and Genetic Algorithms.- A Population-Based Local Search for Solving a Bi-objective Vehicle Routing Problem.- Combining Lagrangian Decomposition with an Evolutionary Algorithm for the Knapsack Constrained Maximum Spanning Tree Problem.- Exact/Heuristic Hybrids Using rVNS and Hyperheuristics for Workforce Scheduling.- An Analysis of Problem Difficulty for a Class of Optimisation Heuristics.- A New Grouping Genetic Algorithm for the Quadratic Multiple Knapsack Problem.- A Hybrid Method for Solving Large-Scale Supply Chain Problems.- Crossover Operators for the Car Sequencing Problem.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 7th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2007, held in Valencia, Spain in April 2007. The 21 revised full papers cover evolutionary algorithms as well as various other metaheuristics, like scatter search, tabu search, memetic algorithms, variable neighborhood search, ant colony optimization, and particle swarm optimization algorithms.
(source: Nielsen Book Data)

• Identifying Regulatory Sites Using Neighborhood Species.- Genetic Programming and Other Machine Learning Approaches to Predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding Levels (%PPB) of Drugs.- Hypothesis Testing with Classifier Systems for Rule-Based Risk Prediction.- Robust Peak Detection and Alignment of nanoLC-FT Mass Spectrometry Data.- One-Versus-One and One-Versus-All Multiclass SVM-RFE for Gene Selection in Cancer Classification.- Understanding Signal Sequences with Machine Learning.- Targeting Differentially Co-regulated Genes by Multiobjective and Multimodal Optimization.- Modeling Genetic Networks: Comparison of Static and Dynamic Models.- A Genetic Embedded Approach for Gene Selection and Classification of Microarray Data.- Modeling the Shoot Apical Meristem in A. thaliana: Parameter Estimation for Spatial Pattern Formation.- Evolutionary Search for Improved Path Diagrams.- Simplifying Amino Acid Alphabets Using a Genetic Algorithm and Sequence Alignment.- Towards Evolutionary Network Reconstruction Tools for Systems Biology.- A Gaussian Evolutionary Method for Predicting Protein-Protein Interaction Sites.- Bio-mimetic Evolutionary Reverse Engineering of Genetic Regulatory Networks.- Tuning ReliefF for Genome-Wide Genetic Analysis.- Dinucleotide Step Parameterization of Pre-miRNAs Using Multi-objective Evolutionary Algorithms.- Amino Acid Features for Prediction of Protein-Protein Interface Residues with Support Vector Machines.- Predicting HIV Protease-Cleavable Peptides by Discrete Support Vector Machines.- Inverse Protein Folding on 2D Off-Lattice Model: Initial Results and Perspectives.- Virtual Error: A New Measure for Evolutionary Biclustering.- Characterising DNA/RNA Signals with Crisp Hypermotifs: A Case Study on Core Promoters.- Evaluating Evolutionary Algorithms and Differential Evolution for the Online Optimization of Fermentation Processes.- The Role of a Priori Information in the Minimization of Contact Potentials by Means of Estimation of Distribution Algorithms.- Classification of Cell Fates with Support Vector Machine Learning.- Reconstructing Linear Gene Regulatory Networks.- Individual-Based Modeling of Bacterial Foraging with Quorum Sensing in a Time-Varying Environment.- Substitution Matrix Optimisation for Peptide Classification.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2007, held in Valencia, Spain, April 2007. Coverage brings together experts in computer science with experts in bioinformatics and the biological sciences. It presents contributions on fundamental and theoretical issues along with papers dealing with different applications areas.
(source: Nielsen Book Data)

• Methodology.- Memetic Algorithms in Planning, Scheduling, and Timetabling.- Landscapes, Embedded Paths and Evolutionary Scheduling.- Classical and Non-Classical Models of Production Scheduling.- Scheduling of Flow-Shop, Job-Shop, and Combined Scheduling Problems using MOEAs with Fixed and Variable Length Chromosomes.- Designing Dispatching Rules to Minimize Total Tardiness.- A Robust Meta-Hyper-Heuristic Approach to Hybrid Flow-Shop Scheduling.- Hybrid Particle Swarm Optimizers in the Single Machine Scheduling Problem: An Experimental Study.- An Evolutionary Approach for Solving the Multi-Objective Job-Shop Scheduling Problem.- Timetabling.- Multi-Objective Evolutionary Algorithm for University Class Timetabling Problem.- Metaheuristics for University Course Timetabling.- Energy Applications.- Optimum Oil Production Planning using an Evolutionary Approach.- A Hybrid Evolutionary Algorithm for Service Restoration in Power Distribution Systems.- Particle Swarm Optimisation for Operational Planning: Unit Commitment and Economic Dispatch.- Evolutionary Generator Maintenance Scheduling in Power Systems.- Networks.- Evolvable Fuzzy Scheduling Scheme for Multiple-ChannelPacket Switching Network.- A Multi-Objective Evolutionary Algorithm for Channel Routing Problems.- Transport.- Simultaneous Planning and Scheduling for Multi-Autonomous Vehicles.- Scheduling Production and Distribution of Rapidly Perishable Materials with Hybrid GA's.- A Scenario-based Evolutionary Scheduling Approach for Assessing Future Supply Chain Fleet Capabilities.- Business.- Evolutionary Optimization of Business Process Designs.- Using a Large Set of Low Level Heuristics in a Hyperheuristic Approach to Personnel Scheduling.- A Genetic-Algorithm-Based Reconfigurable Scheduler.- Evolutionary Algorithm for an Inventory Location Problem.
• (source: Nielsen Book Data)

Evolutionary scheduling is a vital research domain at the interface of artificial intelligence and operational research. This edited book gives an overview of many of the current developments in the large and growing field of evolutionary scheduling. It demonstrates the applicability of evolutionary computational techniques to solve scheduling problems, not only to small-scale test problems, but also fully-fledged real-world problems.
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• Digital Hardware Evolution.- An Online EHW Pattern Recognition System Applied to Sonar Spectrum Classification.- Design of Electronic Circuits Using a Divide-and-Conquer Approach.- Implementing Multi-VRC Cores to Evolve Combinational Logic Circuits in Parallel.- An Intrinsic Evolvable Hardware Based on Multiplexer Module Array.- Estimating Array Connectivity and Applying Multi-output Node Structure in Evolutionary Design of Digital Circuits.- Research on the Online Evaluation Approach for the Digital Evolvable Hardware.- Research on Multi-objective On-Line Evolution Technology of Digital Circuit Based on FPGA Model.- Evolutionary Design of Generic Combinational Multipliers Using Development.- Analog Hardware Evolution.- Automatic Synthesis of Practical Passive Filters Using Clonal Selection Principle-Based Gene Expression Programming.- Research on Fault-Tolerance of Analog Circuits Based on Evolvable Hardware.- Analog Circuit Evolution Based on FPTA-2.- Bio-inspired Systems.- Knowledge Network Management System with Medicine Self Repairing Strategy.- Design of a Cell in Embryonic Systems with Improved Efficiency and Fault-Tolerance.- Design on Operator-Based Reconfigurable Hardware Architecture and Cell Circuit.- Bio-inspired Systems with Self-developing Mechanisms.- Development of a Tiny Computer-Assisted Wireless EEG Biofeedback System.- Steps Forward to Evolve Bio-inspired Embryonic Cell-Based Electronic Systems.- Evolution of Polymorphic Self-checking Circuits.- Mechanical Hardware Evolution.- Sliding Algorithm for Reconfigurable Arrays of Processors.- System-Level Modeling and Multi-objective Evolutionary Design of Pipelined FFT Processors for Wireless OFDM Receivers.- Reducing the Area on a Chip Using a Bank of Evolved Filters.- Evolutionary Design.- Walsh Function Systems: The Bisectional Evolutional Generation Pattern.- Extrinsic Evolvable Hardware on the RISA Architecture.- Evolving and Analysing "Useful" Redundant Logic.- Adaptive Transmission Technique in Underwater Acoustic Wireless Communication.- Autonomous Robot Path Planning Based on Swarm Intelligence and Stream Functions.- Research on Adaptive System of the BTT-45 Air-to-Air Missile Based on Multilevel Hierarchical Intelligent Controller.- The Design of an Evolvable On-Board Computer.- Evolutionary Algorithms in Hardware Design.- Extending Artificial Development: Exploiting Environmental Information for the Achievement of Phenotypic Plasticity.- UDT-Based Multi-objective Evolutionary Design of Passive Power Filters of a Hybrid Power Filter System.- Designing Electronic Circuits by Means of Gene Expression Programming II.- Designing Polymorphic Circuits with Evolutionary Algorithm Based on Weighted Sum Method.- Robust and Efficient Multi-objective Automatic Adjustment for Optical Axes in Laser Systems Using Stochastic Binary Search Algorithm.- Minimization of the Redundant Sensor Nodes in Dense Wireless Sensor Networks.- Evolving in Extended Hamming Distance Space: Hierarchical Mutation Strategy and Local Learning Principle for EHW.- Hardware Implementation of Evolutionary Algorithms.- Adaptive and Evolvable Analog Electronics for Space Applications.- Improving Flexibility in On-Line Evolvable Systems by Reconfigurable Computing.- Evolutionary Design of Resilient Substitution Boxes: From Coding to Hardware Implementation.- A Sophisticated Architecture for Evolutionary Multiobjective Optimization Utilizing High Performance DSP.- FPGA-Based Genetic Algorithm Kernel Design.- Using Systolic Technique to Accelerate an EHW Engine for Lossless Image Compression.
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This book constitutes the refereed proceedings of the 7th International Conference on Evolvable Systems, ICES 2007, held in Wuhan, China, in September 2007. The 41 revised full papers collected in this volume are organized in topical sections on digital hardware evolution, analog hardware evolution, bio-inspired systems, mechanical hardware evolution, evolutionary design, evolutionary algorithms in hardware design, and hardware implementation of evolutionary algorithms.
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• Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews.- Quantum-Inspired Evolutionary Algorithm for Numerical Optimization.- Enhanced Evolutionary Algorithms for Multidisciplinary Design Optimization: A Control Engineering Perspective.- Hybrid Evolutionary Algorithms and Clustering Search.- A Novel Hybrid Algorithm for Function Optimization: Particle Swarm Assisted Incremental Evolution Strategy.- An Efficient Nearest Neighbor Classifier.- Hybrid Genetic: Particle Swarm Optimization Algorithm.- A Hybrid Genetic Algorithm and Bacterial Foraging Approach for Global Optimization and Robust Tuning of PID Controller with Disturbance Rejection.- Memetic Algorithms Parametric Optimization for Microlithography.- Significance of Hybrid Evolutionary Computation for Ab Initio Protein Folding Prediction.- A Hybrid Evolutionary Heuristic for Job Scheduling on Computational Grids.- Clustering Gene-Expression Data: A Hybrid Approach that Iterates Between k-Means and Evolutionary Search.- Robust Parametric Image Registration.- Pareto Evolutionary Algorithm Hybridized with Local Search for Biobjective TSP.
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This edited volume is targeted at presenting the latest state-of-the-art methodologies in "Hybrid Evolutionary Algorithms". The chapters deal with the theoretical and methodological aspects, as well as various applications to many real world problems from science, technology, business or commerce. Overall, the book has 14 chapters including an introductory chapter giving the fundamental definitions and some important research challenges. The contributions were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed.
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• Parameter Setting in EAs: a 30 Year Perspective.- Parameter Control in Evolutionary Algorithms.- Self-Adaptation in Evolutionary Algorithms.- Adaptive Strategies for Operator Allocation.- Sequential Parameter Optimization Applied to Self-Adaptation for Binary-Coded Evolutionary Algorithms.- Combining Meta-EAs and Racing for Difficult EA Parameter Tuning Tasks.- Genetic Programming: Parametric Analysis of Structure Altering Mutation Techniques.- Parameter Sweeps for Exploring Parameter Spaces of Genetic and Evolutionary Algorithms.- Adaptive Population Sizing Schemes in Genetic Algorithms.- Population Sizing to Go: Online Adaptation Using Noise and Substructural Measurements.- Parameter-less Hierarchical Bayesian Optimization Algorithm.- Evolutionary Multi-Objective Optimization Without Additional Parameters.- Parameter Setting in Parallel Genetic Algorithms.- Parameter Control in Practice.- Parameter Adaptation for GP Forecasting Applications.
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One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.
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• Heuristics I.- Alternative Solution Representations for the Job Shop Scheduling Problem in Ant Colony Optimisation.- Analyzing the Role of "Smart" Start Points in Coarse Search-Greedy Search.- Concealed Contributors to Result Quality - The Search Process of Ant Colony System.- Ants Guide Future Pilots.- Complex Systems I.- Information Transfer by Particles in Cellular Automata.- An Artificial Development Model for Cell Pattern Generation.- Rounds Effect in Evolutionary Games.- Modelling Architectural Visual Experience Using Non-linear Dimensionality Reduction.- An Evolutionary Benefit from Misperception in Foraging Behaviour.- Simulated Evolution of Discourse with Coupled Recurrent Networks.- How Different Hierarchical Relationships Impact Evolution.- A Dual Phase Evolution Model of Adaptive Radiation in Landscapes.- Biological Systems I.- Directed Evolution of an Artificial Cell Lineage.- An Integrated QAP-Based Approach to Visualize Patterns of Gene Expression Similarity.- Complement-Based Self-Replicated, Self-Assembled Systems (CBSRSAS).- Self-maintained Movements of Droplets with Convection Flow.- Structural Circuits and Attractors in Kauffman Networks.- The Effects of Learning on the Roles of Chance, History and Adaptation in Evolving Neural Networks.- Unsupervised Acoustic Classification of Bird Species Using Hierarchical Self-organizing Maps.- The Prisoner's Dilemma with Image Scoring on Networks: How Does a Player's Strategy Depend on Its Place in the Social Network?.- Heuristics II.- Population-Based Ant Colony Optimisation for Multi-objective Function Optimisation.- Mechanisms for Evolutionary Reincarnation.- An Evolutionary Algorithm with Spatially Distributed Surrogates for Multiobjective Optimization.- Examining Dissimilarity Scaling in Ant Colony Approaches to Data Clustering.- Complex Systems II.- A Framework for the Co-evolution of Genes, Proteins and a Genetic Code Within an Artificial Chemistry Reaction Set.- In-Formation Flocking: An Approach to Data Visualization Using Multi-agent Formation Behavior.- A Principled Approach to Swarm-Based Wall-Building.- Pattern Extraction Improves Automata-Based Syntax Analysis in Songbirds.- Heuristics III.- A Modified Strategy for the Constriction Factor in Particle Swarm Optimization.- A Differential Evolution Variant of NSGA II for Real World Multiobjective Optimization.- Investigating a Hybrid Metaheuristic for Job Shop Rescheduling.- Enhancements to Extremal Optimisation for Generalised Assignment.- Biological Systems II.- Identification of Marker Genes Discriminating the Pathological Stages in Ovarian Carcinoma by Using Support Vector Machine and Systems Biology.- Ancestral DNA Sequence Reconstruction Using Recursive Genetic Algorithms.
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This book constitutes the refereed proceedings of the Third Australian Conference on Artificial Life, ACAL 2007, held in Gold Coast, Australia, in December 2007. The 34 revised full papers presented were carefully reviewed and selected from 70 submissions. Research in Alife covers the main areas of biological behaviour as a metaphor for computational models, computational models that reproduce/duplicate a biological behaviour, and computational models to solve biological problems.
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