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High Performance Computing
I wish to welcome all of you to the International Symposium on High Perf- mance Computing 2002 (ISHPC2002) and to Kansai Science City, which is not farfromtheancientcapitalsofJapan:NaraandKyoto.ISHPC2002isthefourth in the ISHPC series, which consists, to date, of ISHPC ’97 (Fukuoka, November 1997), ISHPC ’99 (Kyoto, May 1999), and ISHPC2000 (Tokyo, October 2000). The success of these symposia indicates the importance of this area and the strong interest of the research community. With all of the recent drastic changes in HPC technology trends, HPC has had and will continue to have a signi?cant impact on computer science and technology. I am pleased to serve as General Chair at a time whe...
Information Processing with Evolutionary Algorithms
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
The Design of Innovation
7 69 6 A DESIGN APPROACH TO PROBLEM DIFFICULTY 71 1 Design and Problem Difficulty 71 2 Three Misconceptions 72 3 Hard Problems Exist 76 4 The 3-Way Decomposition and Its Core 77 The Core of Intra-BB Difficulty: Deception 5 77 6 The Core of Inter-BB Difficulty: Scaling 83 7 The Core of Extra-BB Difficulty: Noise 88 Crosstalk: All Roads Lead to the Core 8 89 9 From Multimodality to Hierarchy 93 10 Summary 100 7 ENSURING BUILDING BLOCK SUPPLY 101 1 Past Work 101 2 Facetwise Supply Model I: One BB 102 Facetwise Supply Model II: Partition Success 103 3 4 Population Size for BB Supply 104 Summary 5 106 8 ENSURING BUILDING BLOCK GROWTH 109 1 The Schema Theorem: BB Growth Bound 109 2 Schema Growth S...
Practical Handbook of Genetic Algorithms
Practical Handbook of Genetic Algorithms, Volume 3: Complex Coding Systems contains computer-code examples for the development of genetic algorithm systems - compiling them from an array of practitioners in the field. Each contribution of this singular resource includes: unique code segments documentation descripti
Advances in Machine Learning and Cybernetics
This book constitutes the thoroughly refereed post-proceedings of the 4th International Conference on Machine Learning and Cybernetics, ICMLC 2005, held in Guangzhou, China in August 2005. The 114 revised full papers of this volume are organized in topical sections on agents and distributed artificial intelligence, control, data mining and knowledge discovery, fuzzy information processing, learning and reasoning, machine learning applications, neural networks and statistical learning methods, pattern recognition, vision and image processing.
Artificial Immune Systems
Arti?cial Immune Systems have come of age. They are no longer an obscure computersciencetechnique,workedonbyacoupleoffarsightedresearchgroups. Today, researchers across the globe are working on new computer algorithms inspired by the workings of the immune system. This vigorous ?eld of research investigates how immunobiology can assist our technology, and along the way is beginning to help biologists understand their unique problems. AIS is now old enough to understand its roots, its context in the research community, and its exciting future. It has grown too big to be con?ned to s- cial sessions in evolutionary computation conferences. AIS researchers are now forming their own community and...
Recent Advances in Memetic Algorithms
Memetic algorithms are evolutionary algorithms that apply a local search process to refine solutions to hard problems. Memetic algorithms are the subject of intense scientific research and have been successfully applied to a multitude of real-world problems ranging from the construction of optimal university exam timetables, to the prediction of protein structures and the optimal design of space-craft trajectories. This monograph presents a rich state-of-the-art gallery of works on memetic algorithms. Recent Advances in Memetic Algorithms is the first book that focuses on this technology as the central topical matter. This book gives a coherent, integrated view on both good practice examples and new trends including a concise and self-contained introduction to memetic algorithms. It is a necessary read for postgraduate students and researchers interested in recent advances in search and optimization technologies based on memetic algorithms, but can also be used as complement to undergraduate textbooks on artificial intelligence.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2001[
This book constitutes the refereed proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2001, held in Utrecht, The Netherlands, in October 2001. The 122 revised papers and 136 posters presented were carefully reviewed and selected from a total of 338 submissions. The book offers topical sections on image-guided surgery; shape analysis, segmentation, computer-aided diagnosis; registration; simulation, planning and modeling; visualization; quantitative image analysis; medical robotics and devices; visualization and augmented reality; and time series analysis.
Genetic and Evolutionary Computation — GECCO 2004
The two volume set LNCS 3102/3103 constitutes the refereed proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2004, held in Seattle, WA, USA, in June 2004. The 230 revised full papers and 104 poster papers presented were carefully reviewed and selected from 460 submissions. The papers are organized in topical sections on artificial life, adaptive behavior, agents, and ant colony optimization; artificial immune systems, biological applications; coevolution; evolutionary robotics; evolution strategies and evolutionary programming; evolvable hardware; genetic algorithms; genetic programming; learning classifier systems; real world applications; and search-based software engineering.
Parameter Setting in Evolutionary Algorithms
One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.
