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Evolutionary algorithms (EAs) is now a mature problem-solving family of heuristics that has found its way into many important real-life problems and into leading-edge scientific research. Spatially structured EAs have different properties than standard, mixing EAs. By virtue of the structured disposition of the population members they bring about new dynamical features that can be harnessed to solve difficult problems faster and more efficiently. This book describes the state of the art in spatially structured EAs by using graph concepts as a unifying theme. The models, their analysis, and their empirical behavior are presented in detail. Moreover, there is new material on non-standard networked population structures such as small-world networks. The book should be of interest to advanced undergraduate and graduate students working in evolutionary computation, machine learning, and optimization. It should also be useful to researchers and professionals working in fields where the topological structures of populations and their evolution plays a role.
This book constitutes the refereed proceedings of the 9th Congress of the Italian Association for Artificial Intelligence, AI*IA 2005, held in Milan, Italy in September 2005. The 46 revised full papers presented together with 16 revised short papers were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on either theoretical research with results and proposals, improvements and consolidations, or on applications as there are systems and prototypes, case studies and proposals. Within this classification some of the main classical topics of AI are presented (agents, knowledge representation, machine learning, planning, robotics, natural language, etc.), but here the focus is on the ability of AI computational approaches to face challenging problems and to propose innovative solutions.
This book constitutes the refereed proceedings of the 15th Australian Joint Conference on Artificial Intelligence, AI 2002, held in Canberra, Australia in December 2002. The 62 revised full papers and 12 posters presented were carefully reviewed and selected from 117 submissions. The papers are organized in topical sections on natural language and information retrieval, knowledge representation and reasoning, deduction, learning theory, agents, intelligent systems. Bayesian reasoning and classification, evolutionary algorithms, neural networks, reinforcement learning, constraints and scheduling, neural network applications, satisfiability reasoning, machine learning applications, fuzzy reasoning, and case-based reasoning.
This book constitutes the refereed proceedings of the 6th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2006, held in Budapest, Hungary in April 2006. The 24 revised full papers presented were carefully reviewed and selected from 77 submissions. The papers include coverage of evolutionary algorithms as well as various other metaheuristics, like scatter search, tabu search, and memetic algorithms.
This book constitutes the refereed proceedings of the 25th International Conference on Applications of Evolutionary Computation, EvoApplications 2022, held as part of Evo*2022, in April 2022, co-located with the Evo*2022 events EuroGP, EvoCOP, and EvoMUSART. The 46 revised full papers presented in this book were carefully reviewed and selected from 67 submissions.
This book is about the fabulous sixties and what racing - and life in general -were like in those days. It spans the entire career of Remo Venturi, five-time Italian champion and twice runner-up in the world championship, along with the many anecdotes involved.
This book constitutes the refereed proceedings of the 7th International Conference on Cellular Automata for Research and Industry, ACRI 2006. The book presents 53 revised full papers and 19 revised poster papers together with 6 invited lectures. Topical sections include CA theory and implementation, computational theory, population dynamics, physical modeling, urban, environmental and social modeling, traffic and boolean networks, multi-agents and robotics, as well as crowds and cellular automata, and more.
The growing presence of biologically-inspired processing has caused significant changes in data retrieval. With the ubiquity of these technologies, more effective and streamlined data processing techniques are available. Bio-Inspired Computing for Information Retrieval Applications is a key resource on the latest advances and research regarding current techniques that have evolved from biologically-inspired processes and its application to a variety of problems. Highlighting multidisciplinary studies on data processing, swarm-based clustering, and evolutionary computation, this publication is an ideal reference source for researchers, academics, professionals, students, and practitioners.