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"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.
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• The cumulative consensus of cognitive agents in scenarios: A framework for evolutionary processes in semantic memory
• Preferential partner selection in evolutionary labor markets: A study in agent-based computational economics
• Subspace pursuit for exploratory modeling
• Complete classes of strategies for the Classical Iterated Prisoner's Dilemma
• Evolutionary computing in multi-agent environments: Operators
• Evaluation of a simple host-parasite genetic algorithm
• Testing three paradigms for evolving groups of cooperative, simple agents
• Acquisition of general adaptive features by evolution
• Hybrid interior-lagrangian penalty based evolutionary optimization
• GA-Optimal fitness functions
• Scaling up evolutionary programming algorithms
• Short notes on the schema theorem and the building block hypothesis in genetic algorithms
• A superior evolutionary algorithm for 3-SAT
• Evolvable hardware control for dynamic reconfigurable and adaptive computing
• Evolutionary programming strategies with self-adaptation applied to the design of rotorcraft using parallel processing
• Optimization of thinned phased arrays using evolutionary programming
• Evolutionary domain covering of an inference system for function approximation
• Learning to re-engineer semantic networks using cultural algorithms
• Integration of slicing methods into a Cultural Algorithm in order to assist in large-scale engineering systems design
• Genetic search for object identification
• Fuzzy cultural algorithms with evolutionary programming
• Culturing evolution strategies to support the exploration of novel environments by an intelligent robotic agent
• Skeuomorphs and cultural algorithms
• Sphere operators and their applicability for constrained parameter optimization problems
• Numeric mutation as an improvement to symbolic regression in genetic programming
• Variable-dimensional optimization with evolutionary algorithms using fixed-length representations
• On making problems evolutionarily friendly part 1: Evolving the most convenient representations
• On making problems evolutionarily friendly part 2: Evolving the most convenient coordinate systems within which to pose (and solve) the given problem
• An experimental investigation of self-adaptation in evolutionary programming
• On the application of evolutionary pattern search algorithms
• The schema theorem and the misallocation of trials in the presence of stochastic effects
• On the "Explorative power" of ES/EP-like algorithms
• Resampling and its avoidance in genetic algorithms
• Evolutionary search for minimal elements in partially ordered finite sets
• Tailoring mutation to landscape properties
• A genetic programming methodology for missile countermeasures optimization under uncertainty
• Evolutionary algorithms for vertex cover
• An evolutionary self-learning methodology: Some preliminary results from a case study
• Evolving IIR filters in multipath environments
• Fuzzy partition and input selection by genetic algorithms for designing fuzzy rule-based classification systems
• Evolving nonlinear controllers for backing up a truck-and-trailer using evolutionary programming
• Reconstruction of DNA sequence information from a simulated DNA chip using evolutionary programming
• Using programmatic motifs and genetic programming to classify protein sequences as to cellular location
• Fully automated and rapid flexible docking of inhibitors covalently bound to serine proteases
• Microtubule networks as a medium for adaptive information processing
• Evolve IV: A metabolically-based artificial ecosystem model
• Sex, mate selection, and evolution
• Finding low energy conformations of atomic clusters using evolution strategies
• Estimating the distribution of neural connections in the saccadic system using a biologically plausible learning rule
• Preliminary results
• Evolutionary algorithms combined with deterministic search
• Steady state memetic algorithm for partial shape matching
• A fully characterized test suite for genetic programming
• Genetic algorithms for belief network inference: The role of scaling and niching
• Building software frameworks for evolutionary computation
• Recorded step directional mutation for faster convergence
• The behavior of particles
• Parameter selection in particle swarm optimization
• Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences
• Comparison between genetic algorithms and particle swarm optimization
• A hybrid evolutionary learning system for synthesizing neural network pattern recognition systems
• An evolutionary algorithm for designing feedforward neural networks
• Dual network representation applied to the evolution of neural controllers
• What does the landscape of a Hopfield associative memory look like?
• Visualization of evolutionary adaptation in R n
• On the application of cohort-driven operators to continuous optimization problems using evolutionary computation
• Random search versus genetic programming as engines for collective adaptation
• Optimal mutation and crossover rates for a genetic algorithm operating in a dynamic environment
• Local selection
• Asymmetric mutations for stochastic search
• Automated rule extraction for engine health monitoring
• Genetic programming for automatic target classification and recognition in synthetic aperture radar imagery
• Evolving heuristics for planning
• Solving cutting stock problems by evolutionary programming
• An empirical investigation of an evolutionary algorithm's ability to maintain a known good solution
• Evolving integrated low-level behaviors into intelligently interactive simulated forces
• Using offset invariant crossover as a tool for discovering cycle lengths of a periodic function
• Evolving a generalised behaviour: Artificial ant problem revisited
• Co-evolving functions in genetic programming: Dynamic ADF creation using GliB
• Evolving spatially-localized projection filters for SAR automatic target recognition
• Genetic programming in the overlapping generations model: An illustration with the dynamics of the inflation rate.

This book constitutes the thoroughly refereed post-conference proceedings of the 7th International Conference on Evolutionary Programming, EP98, held in San Diego, CA, USA, in March 1998. The volume presents 81 revised full papers selected from an overwhelming number of submissions. The papers are organized in topical sections on economics, emergence and complex systems; issues and innovations in evolutionary computation; applications; evolution-based approaches to engineering design; examining representations and operators; evolutionary computation theory; evolutionary computation and biological modeling; particle swarm; and combinations of evolutionary and neural computation.

• Practical Genetic Algorithms.- Real-World Application: Routing Problem.- Elitist Compact Genetic Algorithms.- Real-coded Bayesian Optimization Algorithm.- Multiobjective Real-coded Bayesian Optimization Algorithm.- Conclusions.
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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.
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• Introduction.- Grammatical Evolution.- Survey of EC in Dynamic Environments.- GE in Dynamic Environments.- Constant Creation and Adaptation in Grammatical Evolution.- Constant Creation with meta-Grammars.- Controlled Static Trading with GE.- Adaptive Dynamic Trading with GE.- Conclusions and The Future.
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Dynamic environments abound, encompassing many real-world problems in fields as diverse as finance, engineering, biology and business. A vibrant research literature has emerged which takes inspiration from evolutionary processes to develop problem-solvers for these environments. "Foundations in Grammatical Evolution for Dynamic Environments" is a cutting edge volume illustrating current state of the art in applying grammar-based evolutionary computation to solve real-world problems in dynamic environments. The book provides a clear introduction to dynamic environments and the types of change that can occur. This is followed by a detailed description of evolutionary computation, concentrating on the powerful Grammatical Evolution methodology. It continues by addressing fundamental issues facing all Evolutionary Algorithms in dynamic problems, such as how to adapt and generate constants, how to enhance evolvability and maintain diversity. Finally, the developed methods are illustrated with application to the real-world dynamic problem of trading on financial time-series. The book was written to be accessible to a wide audience and should be of interest to practitioners, academics and students, who are seeking to apply grammar-based evolutionary algorithms to solve problems in dynamic environments. "Foundations in Grammatical Evolution for Dynamic Environments" is the second book dedicated to the topic of Grammatical Evolution.
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The past few years have seen the rise of agile or evolutionary methods in software development. These methods embrace change in requirements even late in the project. The ability to change software is because of certain practices that are followed within teams, such as Test Driven Development, Pair Programming, and Continuous Integration. Continuous Integration provides a way for software teams to integrate their work more than once a day, and promotes confidence in the software that is being developed by the team. It is thought that this practice is difficult to apply when continuously integrating the database with application code; hence, Evolutionary Database Development is considered a mismatch with agile methods. Pramod Sadalage shows that this is not necessarily true. Continuous Integration changed the way software is written. Why not extend and make the database part of the same Continuous Integration cycle so that you can see integrated results of your application as well as your database? Delivered in PDF format for quick and easy access, Recipes for Continuous Database Integration shows how the database can be brought under the preview of Continuous Integration, allowing all teams to integrate not only their application code, but also their database. This Short Cut presents a recipe for each task that needs to be done. Each recipe starts with a statement of a problem, followed by an explanation and solution. It provides concrete ways and examples to implement ideas in Refactoring Databases: Evolutionary Database Design by Scott W Ambler and Pramod Sadalage. Table of Contents What This Short Cut Covers Introduction Recipe 1 Continuously Integrating? Recipe 2 Extracting Your Database in Scripts Recipe 3 Using Version Control for Your Database Recipe 4 Automating Database or Schema Creation Recipe 5 Creating Objects in Your Database Recipe 6 Removing Database Objects Recipe 7 Removing Your Database Recipe 8 Using the Build Property Files Recipe 9 Re-Creating Your Application Database for Any Build Recipe 10 Making It Easy for New Developers to Join the Team Recipe 11 Integrating on Every Check-In Recipe 12 Naming Upgrade Scripts Recipe 13 Automating Database Change Script Creation Recipe 14 Implementing Database Version Checking Recipe 15 Sending Upgrades to Customers Sample Code Further Reading About ...

• 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.
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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.
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• 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.
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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.
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• Preface ;
• PART 1: Evolutionary Theory ;
• Chapter 1: Co-Evolutionary Learning in Strategic Environments ;
• Chapter 2: Using Evolution to Learn User Preferences ;
• Chapter 3: A Parallel Genetic Algorithm for Clustering.

• Chapter 4: Using SIMD Genetic Programming for Fault-Tolerant Trading Strategies
• Chapter 5: An Efficient Coevolutionary Algorithm Based on Merging and Splitting of Species ;
• Chapter 6: Schema Analysis of Genetic Algorithms on Multiplicative Landscape.

• Chapter 7: Evolutionary Learning Strategies for Artificial Life Characters
• Chapter 8: Adaptive Strategy for GA Inspired by Biological Evolution ;
• Chapter 9: The Influence of Stochastic Quality Functions on Evolutionary Search.

Inspired by the Darwinian framework of evolution through natural selection and adaptation, the field of evolutionary computation has been growing very rapidly, and is today involved in many diverse application areas. This book covers the latest advances in the theories, algorithms, and applications of simulated evolution and learning techniques. It provides insights into different evolutionary computation techniques and their applications in domains such as scheduling, control and power, robotics, signal processing, and bioinformatics. The book will be of significant value to all postgraduates, research scientists and practitioners dealing with evolutionary computation or complex real-world problems.This book has been selected for coverage in:* Index to Scientific & Technical Proceedings (ISTP CDROM version / ISI Proceedings)* CC Proceedings - Engineering & Physical Sciences.
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• 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.
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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.
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• 1. Evolutionary and neural synthesis of intelligent agents / by Karthik Balakrishnan and Vasant Honavar
• 2. Cellular encoding for interactive evolutionary robotics / by Frédéric Gruau and Kameel Quatramaran
• 3. The emergence of communication through synthetic evolution / by Bruce J. MacLennan
• 4. Optimization of classifiers using genetic algorithms / by J.J. Merelo, A. Prieto and F. Morán
• 5. Evolving neuro-controllers and sensors for artificial agents / by Karthik Balakrishnan and Vasant Honavar
• 6. Combined biological metaphors / by Egbert J.W. Boers and Ida G. Sprinkhuizen-Kuyper
• 7. Evolutionary neurogenesis applied to mobile robotics / by Oliver Michel
• 8. Development in neural networks / by Domenico Parisi and Stefano Nolfi
• 9. Evolution and learning in radial basis function neural networks : a hybrid approach / by Brian Carse, Terence C. Fogarty and John C.W. Sullivan
• 10. Co-evolution and ontogenetic change in competing robots / by Dario Floreano, Stefano Nolfi and Francesco Mondada
• 11. Goal directed adaptive behavior in second-order neural networks : learning and evolving in the MAXSON architecture / by Federick L. Crabbe and Michael G. Dyer
• 12. Evolving heterogeneous neural agents by local selection / by Filippo Menczer, W. Nick Street and Melania Degeratu
• 13. Learning sequential decision tasks through symbiotic evolution of neural networks / by David E. Moriarty and Risto Miikkulainen
• 14. From evolving a single neural network to evolving neural network ensembles / by Xin Yao and Yong Liu
• 15. Evolutionary synthesis of Bayesian networks for optimization / by Heinz Mühlenbein and Thilo Mahnig.

This book explores a central issue in artificial intelligence, cognitive science, and artificial life: how to design information structures and processes that create and adapt intelligent agents through evolution and learning. Among the first uses of the computer was the development of programs to model perception, reasoning, learning, and evolution. Further developments resulted in computers and programs that exhibit aspects of intelligent behavior. The field of artificial intelligence is based on the premise that thought processes can be computationally modeled. Computational molecular biology brought a similar approach to the study of living systems. In both cases, hypotheses concerning the structure, function, and evolution of cognitive systems (natural as well as synthetic) take the form of computer programs that store, organize, manipulate, and use information. Systems whose information processing structures are fully programmed are difficult to design for all but the simplest applications. Real-world environments call for systems that are able to modify their behavior by changing their information processing structures. Cognitive and information structures and processes, embodied in living systems, display many effective designs for biological intelligent agents. They are also a source of ideas for designing artificial intelligent agents. This book explores a central issue in artificial intelligence, cognitive science, and artificial life: how to design information structures and processes that create and adapt intelligent agents through evolution and learning. The book is organized around four topics: the power of evolution to determine effective solutions to complex tasks, mechanisms to make evolutionary design scalable, the use of evolutionary search in conjunction with local learning algorithms, and the extension of evolutionary search in novel directions.
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• 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.
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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.
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• Part I Models & Theories.- Parallel BMDA with Probability Model Migration.- Linkages Detection in Histogram-based Estimation of Distribution Algorithm.- Linkage in Island Models.- Real-coded ECGA for Solving Decomposable Real-Valued Optimization Problems.- Linkage Learning Accuracy in the Bayesian Optimization Algorithm.- The Impact of Exact Probabilistic Learning Algorithms in EDAs based on Bayesian Networks.- Linkage Learning in Estimation of Distribution Algorithms.- Part II Operators & Frameworks.- Parallel GEAs with Linkage Analysis over Grid.- Identification and Exploitation of Linkage by Means of Alternative Splicing.- A Clustering-based Approach for Linkage Learning Applied to Multimodal Optimization.- Studying the Effects of Dual Coding on the Adaptation of Representation for Linkage in Evolutionary Algorithms.- Symbiotic Evolution to avoid Linkage Problem.- EpiSwarm, A Swarm-based System for Investigating Genetic Epistasis.- Real-Coded Extended Compact Genetic Algorithm based on Mixtures of Models.- Part III Applications.- Genetic Algorithms for the Airport Gate Assignment: Linkage, Representation and Uniform Crossover.- A Decomposed Approach for the Minimum Interference Frequency Assignment.- Set Representation and Multi-parent Learning within an Evolutionary Algorithm for Optimal Design of Trusses.- A Network Design Problem by a GA with Linkage Identification and Recombination for Overlapping Building Blocks.- Knowledge-based Evolutionary Linkage in MEMS Design Synthesis.
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In recent years, the issue of linkage in GEAs has garnered greater attention and recognition from researchers. Conventional approaches that rely much on ad hoc tweaking of parameters to control the search by balancing the level of exploitation and exploration are grossly inadequate. As shown in the work reported here, such parameters tweaking based approaches have their limits; they can be easily "fooled" by cases of triviality or peculiarity of the class of problems that the algorithms are designed to handle. Furthermore, these approaches are usually blind to the interactions between the decision variables, thereby disrupting the partial solutions that are being built up along the way.
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• Index Fund Optimization Using Genetic Algorithm and Scatter Diagram Based on Coefficients of Determination.- Mining Bayesian Networks from Direct Marketing Databases with Missing Values.- Fuzzy Local Currency based on Social Network Analysis for Promoting Community Businesses.- Evolving Failure Resilience in Scale-Free Networks
• Evolving Networks with Enhanced Linear Stability Properties.- Effectiveness of Close-loop Congestion Controls for DDoS Attacks.- Priority-based Genetic Algorithm for Shortest Path Routing Problem in OSPF.- Evolutionary Network Design by Multiobjective Hybrid Genetic Algorithm.- Hybrid Genetic Algorithm for Designing Logistics Network, VRP and AGV Problems.- Multiobjective Genetic Algorithm for Bicriteria Network Design Problems.- Use of Serendipity Power for Discoveries and Inventions.- Evolution of Retinal Blood Vessel Segmentation Methodology using Wavelet Transforms for Assessment of Diabetic Retinopathy.- Multistage-based Genetic Algorithm for Flexible Job-shop Scheduling Problems.- Implementation of Parallel Genetic Algorithms on Graphics Processing Units.
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Artificial evolutionary systems are computer systems, inspired by ideas from natural evolution and related phenomena. The field has a long history, dating back to the earliest days of computer science, but it has only become an established scientific and engineering discipline since the 1990s, with packages for the commonest form, genetic algorithms, now widely available. Researchers in the Asia-Pacific region have participated strongly in the development of evolutionary systems, with a particular emphasis on the evolution of intelligent solutions to highly complex problems. The Asia-Pacific Symposia on Intelligent and Evolutionary Systems have been an important contributor to this growth in impact, since 1997 providing an annual forum for exchange and dissemination of ideas. Participants come primarily from East Asia and the Western Pacific, but contributions are welcomed from around the World. This volume features a selection of fourteen of the best papers from recent APSIES. They illustrate the breadth of research in the region, with applications ranging from business to medicine, from network optimization to the promotion of innovation.
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• Adaptive Business Intelligence Based on Evolution Strategies: Some Application Examples of Self-Adaptive Software Extending the Boundaries of Design Optimization by Integrating Fast Optimization Techniques with Machine-Code-Based, Linear Genetic Programming Evolutionary Optimization of Approximating Triangulations for Surface Reconstruction from Unstructured 3D Data An Evolutionary Algorithm based on Morphological Associative Memories for Endmember Selection in Hyperspectral Images On a Gradient Based Evolution Strategy for Parametric Illumination Correction A New Chromosome Codification for Scheduling Problems Evolution-based Learning of Ontological Knowledge for a Large-scale Multi-agent Simulation An Evolutionary Algorithms Approach to Phlogenetic Tree Construction Robot Controller Evolution with Macroevolutionary Algorithms Evolving Natural Language Grammars Evaluating Protein Structure Prediction Models with Evolutionary Algorithms Learning Decision Rules by Means of Hybrid-Encoded Evolutionary Algorithms Evolvable Hardware Techniques for Gate-Level Synthesis of Combinational Circuits The Evolutionary Learning Rule in a System Identification Current and Future Research Trends in Evolutionary Multiobjective Optimization Genetic Algorithms with Limited Convergence Evolution with Sampled Fitness Functions Molecular Computing by Signaling Pathways Strategy Oriented Evolutionary Games: Towards a Grammatical Model of Games Discrete Multi-Phase Particle Swarm Optimization.
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Provides a broad sample of current information processing applications Includes examples of successful applications that will encourage practitioners to apply the techniques described in the book to real-life problems.
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• Bio-Inspired Computational Intelligence for the Hardware of Adaptive Systems.- Advanced Hardware Implementation of the Computational Intelligence and Intelligent Technologies.- Bio-Inspired Analogue and Digital Circuits and Their Applications.
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Evolvable Hardware (EHW) has emerged as a sub-domain of artificial evolution represented by a design methodology (consortium of methods) involving the application of Evolutionary Algorithms (EA) to the synthesis of digital and analogue electronic circuits and systems. Nevertheless, the most benefit for the society and indeed most revolutionizing application of EA is its hardware implementation leading to the EHW. These new EA based methodologies led to a new type of machines that is evolved to attain a desired behaviour, which means they have a behavioural computational intelligence. EHW is a special case of the adaptive hardware, namely being strongly related to the Adaptive Systems (AS) and the Adaptive Hardware (AH). The book presents a careful selection of the field that very well reflects the breadth of this high technology and its terminology and applications in context of the AS/AH. The harmonious symbiosis of the engineering approach and the accurate scientific methodology features the aspects of highly relevant and practical design principles governing the development of EHW and its connections with AS/AH. This book is both attractive and useful for everybody interested in the design and analysis of EHW in context of AS/AH and implementation of real time adaptive hardware hybrid intelligent systems.
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• Introduction.- RelatedWork and Background.- Theoretical Analysis of Differential Evolution.- Parameter Adaptive Differential Evolution.- Surrogate Model-based Differential Evolution.- Adaptive Multi-Objective Differential Evolution.- Application to Winner Determination Problems in Combinatorial Auctions.- Application to Flight Planning in Air Traffic Control Systems.- Application to the TPM Optimization in Credit Decision Making.- Conclusions and Future Work.
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Optimization problems are ubiquitous in academic research and real-world applications wherever such resources as space, time and cost are limited. Researchers and practitioners need to solve problems fundamental to their daily work which, however, may show a variety of challenging characteristics such as discontinuity, nonlinearity, nonconvexity, and multimodality. It is expected that solving a complex optimization problem itself should easy to use, reliable and efficient to achieve satisfactory solutions. Differential evolution is a recent branch of evolutionary algorithms that is capable of addressing a wide set of complex optimization problems in a relatively uniform and conceptually simple manner. For better performance, the control parameters of differential evolution need to be set appropriately as they have different effects on evolutionary search behaviours for various problems or at different optimization stages of a single problem. The fundamental theme of the book is theoretical study of differential evolution and algorithmic analysis of parameter adaptive schemes. Topics covered in this book include: Theoretical analysis of differential evolution and its control parameters Algorithmic design and comparative analysis of parameter adaptive schemes Scalability analysis of adaptive differential evolution Adaptive differential evolution for multi-objective optimization Incorporation of surrogate model for computationally expensive optimization Application to winner determination in combinatorial auctions of E-Commerce Application to flight route planning in Air Traffic Management Application to transition probability matrix optimization in credit-decision making.

• A Novel Ant Colony Optimization Strategy for the Quantum Circuit Compilation Problem.- Hybridization of Racing Methods with Evolutionary Operators for Simulation Optimization of Traffic Lights Programs.- Decomposition-based Multi-objective Landscape Features and Automated Algorithm Selection.- MATE: A Model-Based Algorithm Tuning Engine.- An Improvement Heuristic Based on Variable Neighborhood Search for a Dynamic Orienteering Problem.- Runtime Analysis of the (mu+1)-EA on the Dynamic BinVal Function.- Tabu-Driven Quantum Neighborhood Samplers.- On Hybrid Heuristics for Steiner Trees on the Plane with Obstacles.- Flowshop NEH-Based Heuristic Recommendation.- Stagnation Detection with Randomized Local Search.- An Artificial Immune System for Black Box Test Case Selection.- Symmetry Breaking for Voting Mechanisms.- A Heuristic Algorithm for School Bus Routing with Bus Stop Selection.- Hybrid Heuristic and Metaheuristic for Solving Electric Vehicle Charging Scheduling Problem.
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This book constitutes the refereed proceedings of the 21st European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2021, held as part of Evo*2021, as Virtual Event, in April 2021, co-located with the Evo*2021 events: EvoMUSART, EvoApplications, and EuroGP. The 14 revised full papers presented in this book were carefully reviewed and selected from 42 submissions. They cover a wide spectrum of topics, ranging from the foundations of evolutionary algorithms and other search heuristics to their accurate design and application to combinatorial optimization problems. Fundamental and methodological aspects deal with runtime analysis, the structural properties of fitness landscapes, the study of core components of metaheuristics, the clever design of their search principles, and their careful selection and configuration. Applications cover problem domains such as scheduling, routing, search-based software engineering and general graph problems. The range of topics covered in this volume reflects the current state of research in the fields of evolutionary computation and combinatorial optimization.
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• Optimizing Prices and Periods in Time-of-use Electricity Tariff Design Using Bilevel Programming.- An Algebraic Approach for the Search Space of Permutations with Repetition.- A Comparison of Genetic Representations for Multi-Objective Shortest Path Problems on Multigraphs.- The Univariate Marginal Distribution Algorithm Copes well with Deception and Epistasis.- A Beam Search Approach to the Traveling Tournament Problem.- Cooperative Parallel SAT Local Search with Path Relinking.- Dynamic Compartmental Models for Large Multi-Objective Landscapes and Performance Estimation.- Fitness Landscape Analysis of Automated Machine Learning Search Spaces.- On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D.- A Grouping Genetic Algorithm for Multi Depot Pickup and Delivery Problems with Time Windows and Heterogeneous Vehicle Fleets.- MILPIBEA: Algorithm for Multi-Objective Features Selection in (Evolving) Software Product Lines.- A Group Genetic Algorithm for Resource Allocation in Container-Based Clouds.- The Local Optima Level in Chemotherapy Schedule Optimisation.- Genetic Programming with Adaptive Search Based on the Frequency of Features for Dynamic Flexible Job Shop Scheduling.
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This book constitutes the refereed proceedings of the 20th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EuroGP, EvoMUSART and EvoApplications. The 14 full papers presented in this book were carefully reviewed and selected from 37 submissions. The papers cover a wide spectrum of topics, ranging from the foundations of evolutionary computation algorithms and other search heuristics, to their accurate design and application to combinatorial optimization problems.
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• EvoCOMNET Contributions.- Web Application Security through Gene Expression Programming.- Location Discovery in Wireless Sensor Networks Using a Two-Stage Simulated Annealing.- Wireless Communications for Distributed Navigation in Robot Swarms.- An Evolutionary Algorithm for Survivable Virtual Topology Mapping in Optical WDM Networks.- Extremal Optimization as a Viable Means for Mapping in Grids.- Swarm Intelligence Inspired Multicast Routing: An Ant Colony Optimization Approach.- A Framework for Evolutionary Peer-to-Peer Overlay Schemes.- Multiuser Scheduling in HSDPA with Particle Swarm Optimization.- Efficient Signal Processing and Anomaly Detection in Wireless Sensor Networks.- Peer-to-Peer Optimization in Large Unreliable Networks with Branch-and-Bound and Particle Swarms.- Evolving High-Speed, Easy-to-Understand Network Intrusion Detection Rules with Genetic Programming.- Soft Computing Techniques for Internet Backbone Traffic Anomaly Detection.- Testing Detector Parameterization Using Evolutionary Exploit Generation.- Ant Routing with Distributed Geographical Localization of Knowledge in Ad-Hoc Networks.- Discrete Particle Swarm Optimization for Multiple Destination Routing Problems.- EvoENVIRONMENT Contributions.- Combining Back-Propagation and Genetic Algorithms to Train Neural Networks for Ambient Temperature Modeling in Italy.- Estimating the Concentration of Nitrates in Water Samples Using PSO and VNS Approaches.- Optimal Irrigation Scheduling with Evolutionary Algorithms.- Adaptive Land-Use Management in Dynamic Ecological System.- EvoFIN Contributions.- Evolutionary Money Management.- Prediction of Interday Stock Prices Using Developmental and Linear Genetic Programming.- An Introduction to Natural Computing in Finance.- Evolutionary Approaches for Estimating a Coupled Markov Chain Model for Credit Portfolio Risk Management.- Knowledge Patterns in Evolutionary Decision Support Systems for Financial Time Series Analysis.- Predicting Turning Points in Financial Markets with Fuzzy-Evolutionary and Neuro-Evolutionary Modeling.- Comparison of Multi-agent Co-operative Co-evolutionary and Evolutionary Algorithms for Multi-objective Portfolio Optimization.- Dynamic High Frequency Trading: A Neuro-Evolutionary Approach.- EvoGAMES Contributions.- Decay of Invincible Clusters of Cooperators in the Evolutionary Prisoner's Dilemma Game.- Evolutionary Equilibria Detection in Non-cooperative Games.- Coevolution of Competing Agent Species in a Game-Like Environment.- Simulation Minus One Makes a Game.- Evolving Simple Art-Based Games.- Swarming for Games: Immersion in Complex Systems.- Fitness Diversity Parallel Evolution Algorithms in the Turtle Race Game.- Evolving Strategies for Non-player Characters in Unsteady Environments.- Grid Coevolution for Adaptive Simulations: Application to the Building of Opening Books in the Game of Go.- Evolving Teams of Cooperating Agents for Real-Time Strategy Game.- EvoHOT Contributions.- Design Optimization of Radio Frequency Discrete Tuning Varactors.- An Evolutionary Path Planner for Multiple Robot Arms.- Evolutionary Optimization of Number of Gates in PLA Circuits Implemented in VLSI Circuits.- Particle Swarm Optimisation as a Hardware-Oriented Meta-heuristic for Image Analysis.- EvoIASP Contributions.- A Novel GP Approach to Synthesize Vegetation Indices for Soil Erosion Assessment.- Flies Open a Door to SLAM.- Genetic Image Network for Image Classification.- Multiple Network CGP for the Classification of Mammograms.- Evolving Local Descriptor Operators through Genetic Programming.- Evolutionary Optimization for Plasmon-Assisted Lithography.- An Improved Multi-objective Technique for Fuzzy Clustering with Application to IRS Image Segmentation.- EvoINTERACTION Contributions.- Interactive Evolutionary Evaluation through Spatial Partitioning of Fitness Zones.- Fractal Evolver: Interactive Evolutionary Design of Fractals with Grid Computing.- Humorized Computational Intelligence towards User-Adapted Systems with a Sense of Humor.- Innovative Chance Discovery - Extracting Customers' Innovative Concept.- EvoMUSART Contributions.- Evolving Approximate Image Filters.- On the Role of Temporary Storage in Interactive Evolution.- Habitat: Engineering in a Simulated Audible Ecosystem.- The Evolution of Evolutionary Software: Intelligent Rhythm Generation in Kinetic Engine.- Filterscape: Energy Recycling in a Creative Ecosystem.- Evolved Ricochet Compositions.- Life's What You Make: Niche Construction and Evolutionary Art.- Global Expectation-Violation as Fitness Function in Evolutionary Composition.- Composing Using Heterogeneous Cellular Automata.- On the Socialization of Evolutionary Art.- An Evolutionary Music Composer Algorithm for Bass Harmonization.- Generation of Pop-Rock Chord Sequences Using Genetic Algorithms and Variable Neighborhood Search.- Elevated Pitch: Automated Grammatical Evolution of Short Compositions.- A GA-Based Control Strategy to Create Music with a Chaotic System.- Teaching Evolutionary Design Systems by Extending "Context Free".- Artificial Nature: Immersive World Making.- Evolving Indirectly Represented Melodies with Corpus-Based Fitness Evaluation.- Hearing Thinking.- EvoNUM Contributions.- Memetic Variation Local Search vs. Life-Time Learning in Electrical Impedance Tomography.- Estimating HMM Parameters Using Particle Swarm Optimisation.- Modeling Pheromone Dispensers Using Genetic Programming.- NK Landscapes Difficulty and Negative Slope Coefficient: How Sampling Influences the Results.- On the Parallel Speed-Up of Estimation of Multivariate Normal Algorithm and Evolution Strategies.- Adaptability of Algorithms for Real-Valued Optimization.- A Stigmergy-Based Algorithm for Continuous Optimization Tested on Real-Life-Like Environment.- Stochastic Local Search Techniques with Unimodal Continuous Distributions: A Survey.- Evolutionary Optimization Guided by Entropy-Based Discretization.- EvoSTOC Contributions.- The Influence of Population and Memory Sizes on the Evolutionary Algorithm's Performance for Dynamic Environments.- Differential Evolution with Noise Analyzer.- An Immune System Based Genetic Algorithm Using Permutation-Based Dualism for Dynamic Traveling Salesman Problems.- Dynamic Time-Linkage Problems Revisited.- The Dynamic Knapsack Problem Revisited: A New Benchmark Problem for Dynamic Combinatorial Optimisation.- Impact of Frequency and Severity on Non-Stationary Optimization Problems.- A Critical Look at Dynamic Multi-dimensional Knapsack Problem Generation.- EvoTRANSLOG Contributions.- Evolutionary Freight Transportation Planning.- An Effective Evolutionary Algorithm for the Cumulative Capacitated Vehicle Routing Problem.- A Corridor Method-Based Algorithm for the Pre-marshalling Problem.- Comparison of Metaheuristic Approaches for Multi-objective Simulation-Based Optimization in Supply Chain Inventory Management.- Heuristic Algorithm for Coordination in Public Transport under Disruptions.- Optimal Co-evolutionary Strategies for the Competitive Maritime Network Design Problem.
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The year 2009 celebrates the bicentenary of Darwin's birth and the 150th - niversary of the publication of his seminal work, On the Origin of Species.If this makes 2009 a special year for the research community working in biology and evolution, the ?eld of evolutionary computation (EC) also shares the same excitement. EC techniques are e?cient, nature-inspired planning and optimi- tion methods based on the principles of natural evolution and genetics. Due to their e?ciency and simple underlying principles, these methods can be used in the context of problem solving, optimization, and machine learning. A large and ever-increasing number of researchers and professionals make use of EC te- niques in various application domains. ThisvolumepresentsacarefulselectionofrelevantECapplicationscombi ned with a thorough examination of the techniques used in EC. The papers in the volume illustrate the current state of the art in the application of EC and can help and inspire researchers and professionals to develop e?cient EC methods for design and problem solving.
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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.
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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.
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