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Evolutionary Computing
This book constitutes the refereed post-workshop proceedings of the AISB International Workshop on Evolutionary Computing, held in Manchester, UK, in April 1997. The 22 strictly reviewed and revised full papers presented were selected for inclusion in the book after two rounds of refereeing. The papers are organized in sections on evolutionary approaches to issues in biology and economics, problem structure and finite landscapes, evolutionary machine learning and classifier systems, evolutionary scheduling, and more techniques and applications of evolutionary algorithms.
Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
This book constitutes the refereed proceedings of the International Workshop on Engineering Stochastic Local Search Algorithms 2009, held in Brussels, Belgium, September 3-5, 2009. The 7 revised full papers presented together with 10 short papers were carefully reviewed and selected from more than 27 submissions. The topics include e. g. the use of run time distributions to evaluate and compare, high- performance local search for task scheduling with human, running time analysis of ACO Systems for shortest path problems, the explorative behavior of MAX-MIN ant system and improved robustness through population variance and colony optimization.
Foundations of Genetic Programming
This is one of the only books to provide a complete and coherent review of the theory of genetic programming (GP). In doing so, it provides a coherent consolidation of recent work on the theoretical foundations of GP. A concise introduction to GP and genetic algorithms (GA) is followed by a discussion of fitness landscapes and other theoretical approaches to natural and artificial evolution. Having surveyed early approaches to GP theory it presents new exact schema analysis, showing that it applies to GP as well as to the simpler GAs. New results on the potentially infinite number of possible programs are followed by two chapters applying these new techniques.
Advances in Molecular Bioinformatics
Molecular bioinformatics as a newly emerging interdisciplinary research area, comprises the development and application of algorithms for the purpose of analysis, interpretation and prediction of data and for the design of experiments in the biosciences. The heterogeneous collection of original research presented in this volume illustrates the use of the wide and diverse range of algorithmic techniques. The application of algorithms from computer sciences, including artificial intelligence, machine learning, genetic programming, evolutionary algorithms and neural nets to molecular biologyespecially DNA and RNA sequence analysis and protein engineering is broadly examined. Both algorithmic and biological background problems are explained for the benefit of an interdisciplinary audience.
Handbook of Metaheuristics
The third edition of this handbook is designed to provide a broad coverage of the concepts, implementations, and applications in metaheuristics. The book’s chapters serve as stand-alone presentations giving both the necessary underpinnings as well as practical guides for implementation. The nature of metaheuristics invites an analyst to modify basic methods in response to problem characteristics, past experiences, and personal preferences, and the chapters in this handbook are designed to facilitate this process as well. This new edition has been fully revised and features new chapters on swarm intelligence and automated design of metaheuristics from flexible algorithm frameworks. The auth...
Parallel Problem Solving from Nature – PPSN XVI
This two-volume set LNCS 12269 and LNCS 12270 constitutes the refereed proceedings of the 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, held in Leiden, The Netherlands, in September 2020. The 99 revised full papers were carefully reviewed and selected from 268 submissions. The topics cover classical subjects such as automated algorithm selection and configuration; Bayesian- and surrogate-assisted optimization; benchmarking and performance measures; combinatorial optimization; connection between nature-inspired optimization and artificial intelligence; genetic and evolutionary algorithms; genetic programming; landscape analysis; multiobjective optimization; real-world applications; reinforcement learning; and theoretical aspects of nature-inspired optimization.
Genetic Algorithms in Optimisation, Simulation and Modelling
This book examines the implementation and applications of genetic algorithms (GA) to the domain of AI.In recent years the trend towards, real world applications is fgaining ground especially in GA. The general purpose nature of GA is examined from an interdiciplinary point of view. Despite the differences that may exist in between representations across domain problems the commonality of in the design of GA is upheld. This work provides an overview of the current developments in Europe a section is devoted to the progrmamming of Parallel Genetic Algorithms (including GAME) and a section on Optimisation and Complex Modelling. Readers: researchers in AI, mathematics and computing.
Operations Research in Production Planning and Control
This proceedings volume contains selected and refereed contributions that were presented at the conference on "Recent Developments and New Perspectives of Operations Research in the Area of Production Planning and Control" in Hagen/Germany, 25. - 26. June 1992. This conference was organized with the cooperation of the FernuniversiHit Hagen and was jointly hosted by the "Deutsche Gesellschaft fur Operations Research (DGOR)" and the "Manufacturing Special Interest Group of the Operations Research Society of America (ORSA-SIGMA)". For the organization of the conference we received generous financial support from the sponsors listed at the end of this volume. We wish to express our appreciation ...
Introduction to Genetic Algorithms
This book offers a basic introduction to genetic algorithms. It provides a detailed explanation of genetic algorithm concepts and examines numerous genetic algorithm optimization problems. In addition, the book presents implementation of optimization problems using C and C++ as well as simulated solutions for genetic algorithm problems using MATLAB 7.0. It also includes application case studies on genetic algorithms in emerging fields.
Evolutionary Computation in Combinatorial Optimization
This book constitutes the refereed proceedings of the 15th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2015, held in Copenhagen, Denmark, in April 2015, co-located with the Evo*2015 events EuroGP, EvoMUSART and EvoApplications. The 19 revised full papers presented were carefully reviewed and selected from 46 submissions. The papers cover methodology, applications and theoretical studies. The methods included evolutionary and memetic (hybrid) algorithms, iterated local search, variable neighbourhood search, ant colony optimization, artificial immune systems, hyper-heuristics and other adaptive approaches. The applications include both traditional domains, such as graph coloring, knapsack, vehicle routing, job-shop scheduling, the p-median and the orienteering problems; and new(er) domains such as designing deep recurrent neural networks, detecting network community structure, lock scheduling of ships, cloud resource management, the fire-fighter problem and AI planning. The theoretical studies involved approximation ratio, runtime and black-box complexity analyses.
