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Linear Genetic Programming
Linear Genetic Programming presents a variant of Genetic Programming that evolves imperative computer programs as linear sequences of instructions, in contrast to the more traditional functional expressions or syntax trees. Typical GP phenomena, such as non-effective code, neutral variations, and code growth are investigated from the perspective of linear GP. This book serves as a reference for researchers; it includes sufficient introductory material for students and newcomers to the field.
Flexible Pattern Matching in Strings
Presents recently developed algorithms for searching for simple, multiple and extended strings, regular expressions, exact and approximate matches.
Genetic Programming
Thisvolumerecordstheproceedingsofthe?fthEuropeanconferenceonGenetic Programming(EuroGP2002)whichtookplaceinKinsale,IrelandonApril3–5, 2002, continuing an established tradition of yearly meetings among the most prominent researchers on Genetic Programming in Europe and beyond; their proceedings have always been published in the LNCS series by Springer-Verlag. EuroGP began life in Paris in 1998 as an international workshop (April 14– 15, LNCS 1391); a second workshop took place in G ̈ oteborg in 1999 (May 26– 27, LNCS 1598). Its ?rst appearance as a conference was in the year 2000 in Edinburgh (April 15–16, LNCS 1802), followed by last year’s conference held at Lake Como (April 18â€...
Evolutionary and Swarm Intelligence Algorithms
This book is a delight for academics, researchers and professionals working in evolutionary and swarm computing, computational intelligence, machine learning and engineering design, as well as search and optimization in general. It provides an introduction to the design and development of a number of popular and recent swarm and evolutionary algorithms with a focus on their applications in engineering problems in diverse domains. The topics discussed include particle swarm optimization, the artificial bee colony algorithm, Spider Monkey optimization algorithm, genetic algorithms, constrained multi-objective evolutionary algorithms, genetic programming, and evolutionary fuzzy systems. A friendly and informative treatment of the topics makes this book an ideal reference for beginners and those with experience alike.
Genetic Programming
In this volume we present the accepted contributions for the 7th European C- ference on Genetic Programming (EuroGP 2004). The conference took place on 5-7 April 2004 in Portugal at the University of Coimbra, in the Department of Mathematics in Pra ̧ ca Dom Dinis, located on the hill above the old town. EuroGP is a well-established conference and the sole one exclusively de- ted to Genetic Programming. Previous proceedings have all been published by Springer-Verlag in the LNCS series. EuroGP began as an international wor- hop in Paris, France in 1998 (14-15 April, LNCS 1391). Subsequently the wor- hop was held in G ̈ oteborg, Sweden in 1999 (26-27 May, LNCS 1598) and then EuroGP became an ...
Genetic Algorithms and Machine Learning for Programmers
Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to machine learning. Discover machine learning algorithms using a handful of self-contained recipes. Create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, and cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection mathods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters.
Genetic Programming
July 28-31, 1996 A· Stanford University Genetic programming is a domain-independent method for automatic programming that evolves computer programs that solve, or approximately solve, problems. Starting with a primordial ooze of thousands of randomly created computer programs composed of functions and terminals appropriate to a problem, a population of programs is progressively evolved over many generations using the Darwinian principle of survival of the fittest, a sexual recombination operation, and occasional mutation. These proceedings of the first Genetic Programming Conference present the most recent research in the field of genetic programming as well as recent research results in th...
The Emerging Domain of Cooperating Objects
This book provides a classification of current and future applications for the domain of Cooperating Objects. The book has been created with a very strong participation of the industry and taking into account current research trends and industrial roadmaps
Genetic Programming Theory and Practice VIII
The contributions in this volume are written by the foremost international researchers and practitioners in the GP arena. They examine the similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application. Topics include: FINCH: A System for Evolving Java, Practical Autoconstructive Evolution, The Rubik Cube and GP Temporal Sequence Learning, Ensemble classifiers: AdaBoost and Orthogonal Evolution of Teams, Self-modifying Cartesian GP, Abstract Expression Grammar Symbolic Regression, Age-Fitness Pareto Optimization, Scalable Symbolic Regression by Continuous Evolution, Symbolic Density Models, GP Transforms in Linear Regression Situations, Protein Interactions in a Computational Evolution System, Composition of Music and Financial Strategies via GP, and Evolutionary Art Using Summed Multi-Objective Ranks. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results in GP .
Genetic Programming Theory and Practice III
Genetic Programming Theory and Practice III provides both researchers and industry professionals with the most recent developments in GP theory and practice by exploring the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). The contributions developed from a third workshop at the University of Michigan's Center for the Study of Complex Systems, where leading international genetic programming theorists from major universities and active practitioners from leading industries and businesses meet to examine and challenge how GP theory informs practice and how GP practice impacts GP theory. Applications are from a wide range of domains, including chemical process control, informatics, and circuit design, to name a few.
