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This handbook offers a comprehensive treatise on Grammatical Evolution (GE), a grammar-based Evolutionary Algorithm that employs a function to map binary strings into higher-level structures such as programs. GE's simplicity and modular nature make it a very flexible tool. Since its introduction almost twenty years ago, researchers have applied it to a vast range of problem domains, including financial modelling, parallel programming and genetics. Similarly, much work has been conducted to exploit and understand the nature of its mapping scheme, triggering additional research on everything from different grammars to alternative mappers to initialization. The book first introduces GE to the novice, providing a thorough description of GE along with historical key advances. Two sections follow, each composed of chapters from international leading researchers in the field. The first section concentrates on analysis of GE and its operation, giving valuable insight into set up and deployment. The second section consists of seven chapters describing radically different applications of GE. The contributions in this volume are beneficial to both novices and experts alike, as they detail the results and researcher experiences of applying GE to large scale and difficult problems. Topics include: • Grammar design • Bias in GE • Mapping in GE • Theory of disruption in GE · Structured GE · Geometric semantic GE · GE and semantics · Multi- and Many-core heterogeneous parallel GE · Comparing methods to creating constants in GE · Financial modelling with GE · Synthesis of parallel programs on multi-cores · Design, architecture and engineering with GE · Computational creativity and GE · GE in the prediction of glucose for diabetes · GE approaches to bioinformatics and system genomics · GE with coevolutionary algorithms in cybersecurity · Evolving behaviour trees with GE for platform games · Business analytics and GE for the prediction of patient recruitment in multicentre clinical trials.

Evolutionary programming (Computer science) --- Automatic programming (Computer science) --- Automatic program construction (Computer science) --- Computer programming --- Artificial intelligence. --- Artificial Intelligence. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers

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This handbook offers a comprehensive treatise on Grammatical Evolution (GE), a grammar-based Evolutionary Algorithm that employs a function to map binary strings into higher-level structures such as programs. GE's simplicity and modular nature make it a very flexible tool. Since its introduction almost twenty years ago, researchers have applied it to a vast range of problem domains, including financial modelling, parallel programming and genetics. Similarly, much work has been conducted to exploit and understand the nature of its mapping scheme, triggering additional research on everything from different grammars to alternative mappers to initialization. The book first introduces GE to the novice, providing a thorough description of GE along with historical key advances. Two sections follow, each composed of chapters from international leading researchers in the field. The first section concentrates on analysis of GE and its operation, giving valuable insight into set up and deployment. The second section consists of seven chapters describing radically different applications of GE. The contributions in this volume are beneficial to both novices and experts alike, as they detail the results and researcher experiences of applying GE to large scale and difficult problems. Topics include: • Grammar design • Bias in GE • Mapping in GE • Theory of disruption in GE · Structured GE · Geometric semantic GE · GE and semantics · Multi- and Many-core heterogeneous parallel GE · Comparing methods to creating constants in GE · Financial modelling with GE · Synthesis of parallel programs on multi-cores · Design, architecture and engineering with GE · Computational creativity and GE · GE in the prediction of glucose for diabetes · GE approaches to bioinformatics and system genomics · GE with coevolutionary algorithms in cybersecurity · Evolving behaviour trees with GE for platform games · Business analytics and GE for the prediction of patient recruitment in multicentre clinical trials.

Artificial intelligence. Robotics. Simulation. Graphics --- bio-informatica --- genomics --- creativiteit --- KI (kunstmatige intelligentie) --- AI (artificiële intelligentie)

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Information Technology --- Computer Science (Hardware & Networks)

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This contributed volume, written by leading international researchers, reviews the latest developments of genetic programming (GP) and its key applications in solving current real world problems, such as energy conversion and management, financial analysis, engineering modeling and design, and software engineering, to name a few. Inspired by natural evolution, the use of GP has expanded significantly in the last decade in almost every area of science and engineering. Exploring applications in a variety of fields, the information in this volume can help optimize computer programs throughout the sciences. Taking a hands-on approach, this book provides an invaluable reference to practitioners, providing the necessary details required for a successful application of GP and its branches to challenging problems ranging from drought prediction to trading volatility. It also demonstrates the evolution of GP through major developments in GP studies and applications. It is suitable for advanced students who wish to use relevant book chapters as a basis to pursue further research in these areas, as well as experienced practitioners looking to apply GP to new areas. The book also offers valuable supplementary material for design courses and computation in engineering.

Artificial intelligence. Robotics. Simulation. Graphics --- neuronale netwerken --- fuzzy logic --- cybernetica --- KI (kunstmatige intelligentie) --- AI (artificiële intelligentie)

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In this volume we present the accepted contributions to the Sixth European Conference on Genetic Programming (EuroGP 2003) which took place at the University of Essex, UK on 14-16 April 2003. EuroGP is now a well-established conference and, without any doubt, the most important international event - voted to Genetic Programming occurring in Europe. The proceedings have all been published by Springer-Verlag in the LNCS series. EuroGP began as an - ternational workshop in Paris, France in 1998 (14–15 April, LNCS 1391). Sub- quently the workshop was held in G¨ oteborg, Sweden in 1999 (26–27 May, LNCS 1598) and then EuroGP became an annual conference: in 2000 in Edinburgh, UK (15–16 April, LNCS 1802), in 2001 in Lake Como, Italy (18–19 April, LNCS 2038) and in 2002 in Kinsale, Ireland (3–5 April, LNCS 2278). From the outset, there have always been specialized workshops, co-located with EuroGP, focusing on applications of evolutionary algorithms (LNCS 1468, 1596, 1803, 2037, and 2279). This year was no exception and EvoWorkshops 2003, incorporating Evo- BIO, EvoCOP, EvoIASP, EvoMUSART, EvoSTIM and EvoROB, took place at the University of Essex (LNCS 2611). Genetic Programming (GP) is that part of Evolutionary Computation which solves particular complex problems or tasks by evolving and adapting popu- tions of computer programs, using Darwinian evolution and Mendelian genetics as a source of inspiration.

Genetic programming (Computer science) --- Computer Science --- Engineering & Applied Sciences --- Computer science. --- Computer programming. --- Computers. --- Algorithms. --- Artificial intelligence. --- Pattern recognition. --- Bioinformatics. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Programming Techniques. --- Computation by Abstract Devices. --- Algorithm Analysis and Problem Complexity. --- Pattern Recognition. --- Computer programming --- Genetic algorithms --- Computer software. --- Optical pattern recognition. --- Artificial Intelligence. --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Software, Computer --- Computer systems --- Informatics --- Science --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Data processing --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Algorism --- Algebra --- Arithmetic --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Cybernetics --- Calculators --- Cyberspace --- Computers --- Electronic computer programming --- Electronic digital computers --- Programming (Electronic computers) --- Coding theory --- Foundations --- Programming