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Author: David Ackley
Publisher: Springer Science & Business Media
ISBN: 1461319978
Category : Computers
Languages : en
Pages : 268
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Book Description
In the "black box function optimization" problem, a search strategy is required to find an extremal point of a function without knowing the structure of the function or the range of possible function values. Solving such problems efficiently requires two abilities. On the one hand, a strategy must be capable of learning while searching: It must gather global information about the space and concentrate the search in the most promising regions. On the other hand, a strategy must be capable of sustained exploration: If a search of the most promising region does not uncover a satisfactory point, the strategy must redirect its efforts into other regions of the space. This dissertation describes a connectionist learning machine that produces a search strategy called stochastic iterated genetic hillclimb ing (SIGH). Viewed over a short period of time, SIGH displays a coarse-to-fine searching strategy, like simulated annealing and genetic algorithms. However, in SIGH the convergence process is reversible. The connectionist implementation makes it possible to diverge the search after it has converged, and to recover coarse-grained informa tion about the space that was suppressed during convergence. The successful optimization of a complex function by SIGH usually in volves a series of such converge/diverge cycles.
Author: David Ackley
Publisher: Springer Science & Business Media
ISBN: 1461319978
Category : Computers
Languages : en
Pages : 268
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Book Description
In the "black box function optimization" problem, a search strategy is required to find an extremal point of a function without knowing the structure of the function or the range of possible function values. Solving such problems efficiently requires two abilities. On the one hand, a strategy must be capable of learning while searching: It must gather global information about the space and concentrate the search in the most promising regions. On the other hand, a strategy must be capable of sustained exploration: If a search of the most promising region does not uncover a satisfactory point, the strategy must redirect its efforts into other regions of the space. This dissertation describes a connectionist learning machine that produces a search strategy called stochastic iterated genetic hillclimb ing (SIGH). Viewed over a short period of time, SIGH displays a coarse-to-fine searching strategy, like simulated annealing and genetic algorithms. However, in SIGH the convergence process is reversible. The connectionist implementation makes it possible to diverge the search after it has converged, and to recover coarse-grained informa tion about the space that was suppressed during convergence. The successful optimization of a complex function by SIGH usually in volves a series of such converge/diverge cycles.
Author: Wladyslaw Skarbek
Publisher: Springer
ISBN: 3540446923
Category : Computers
Languages : en
Pages : 757
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Book Description
Computer analysis of images and patterns is a scienti c eld of longstanding tradition, with roots in the early years of the computer era when electronic brains inspired scientists. Moreover, the design of vision machines is a part of humanity’s dream of the arti cial person. I remember the 2nd CAIP, held in Wismar in 1987. Lectures were read in German, English and Russian, and proceedings were also only partially written in English. The conference took place under a di erent political system and proved that ideas are independent of political walls. A few years later the Berlin Wall collapsed, and Professors Sommer and Klette proposed a new formula for the CAIP: let it be held in Central and Eastern Europe every second year. There was a sense of solidarity with scienti c communities in those countries that found themselves in a state of transition to a new economy. A well-implemented idea resulted in a chain of successful events in Dresden (1991), Budapest (1993), Prague (1995), Kiel (1997), and Ljubljana (1999). This year the conference was welcomed at Warsaw. There are three invited lectures and about 90 contributions written by more than 200 authors from 27 countries. Besides Poland (60 authors), the largest representation comes from France (23), followed by England (16), Czech Republic (11), Spain (10), G- many (9), and Belarus (9). Regrettably, in spite of free registration fees and free accommodation for authors from former Soviet Union countries, we received only one accepted paper from Russia.
Author: Mukesh Patel
Publisher: MIT Press
ISBN: 9780262162012
Category : Computers
Languages : en
Pages : 510
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Book Description
This book explores a central issue in artificial intelligence, cognitive science, and artificial life: how to design information structures and processes that create and adapt intelligent agents through evolution and learning. Among the first uses of the computer was the development of programs to model perception, reasoning, learning, and evolution. Further developments resulted in computers and programs that exhibit aspects of intelligent behavior. The field of artificial intelligence is based on the premise that thought processes can be computationally modeled. Computational molecular biology brought a similar approach to the study of living systems. In both cases, hypotheses concerning the structure, function, and evolution of cognitive systems (natural as well as synthetic) take the form of computer programs that store, organize, manipulate, and use information. Systems whose information processing structures are fully programmed are difficult to design for all but the simplest applications. Real-world environments call for systems that are able to modify their behavior by changing their information processing structures. Cognitive and information structures and processes, embodied in living systems, display many effective designs for biological intelligent agents. They are also a source of ideas for designing artificial intelligent agents. This book explores a central issue in artificial intelligence, cognitive science, and artificial life: how to design information structures and processes that create and adapt intelligent agents through evolution and learning. The book is organized around four topics: the power of evolution to determine effective solutions to complex tasks, mechanisms to make evolutionary design scalable, the use of evolutionary search in conjunction with local learning algorithms, and the extension of evolutionary search in novel directions.
Author: Agoston E. Eiben
Publisher: Springer Science & Business Media
ISBN: 9783540650782
Category : Computers
Languages : en
Pages : 1076
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Book Description
This book constitutes the refereed proceedings of the 5th International Conference on Parallel Problem Solving from Nature, PPSN V, held in Amsterdam, The Netherlands, in September 1998. The 101 papers included in their revised form were carefully reviewed and selected from a total of 185 submissions. The book is divided into topical sections on convergence theory; fitness landscape and problem difficulty; noisy and non-stationary objective functions; multi-criteria and constrained optimization; representative issues; selection, operators, and evolution schemes; coevolution and learning; cellular automata, fuzzy systems, and neural networks; ant colonies, immune systems, and other paradigms; TSP, graphs, and satisfiability; scheduling, partitioning, and packing; design and telecommunications; and model estimations and layout problems.
Author: Richard K. Belew
Publisher: Routledge
ISBN: 0429971451
Category : Social Science
Languages : en
Pages : 552
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Book Description
This book is out of a workshop organized to address questions like these. The meeting was sponsored by the Santa Fe Institute and held at Sol y Sam- bra in Santa Fe, New Mexico, during July, 1993. It brought together a group of about 20 scientists from the disciplines of biology, psychology, and computer science, all studying interactions between the evolution of populations and individuals’ adaptations in those populations, and all of whom make some use of computational tools in their work.
Author: Eckart Zitzler
Publisher: Springer
ISBN: 3540447199
Category : Mathematics
Languages : en
Pages : 725
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Book Description
This book constitutes the refereed proceedings of the First International Conference on Multi-Criterion Optimization, EMO 2001, held in Zurich, Switzerland in March 2001. The 45 revised full papers presented were carefully reviewed and selected from a total of 87 submissions. Also included are two tutorial surveys and two invited papers. The book is organized in topical sections on algorithm improvements, performance assessment and comparison, constraint handling and problem decomposition, uncertainty and noise, hybrid and alternative methods, scheduling, and applications of multi-objective optimization in a variety of fields.
Author: Joaquim Filipe
Publisher: Springer
ISBN: 3642198023
Category : Business & Economics
Languages : en
Pages : 614
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Book Description
This book contains substantially extended and revised versions of the best papers from the 12th International Conference on Enterprise Information Systems (ICEIS 2010), held in Funchal, Madeira, Portugal, June 8-12, 2010. Two invited papers are presented together with 39 contributions, which were carefully reviewed and selected from 62 full papers presented at the conference (out of 448 submissions). They reflect state-of-the-art research work that is often driven by real-world applications, thus successfully relating the academic with the industrial community. The topics covered are: databases and information systems integration, artificial intelligence and decision support systems, information systems analysis and specification, software agents and internet computing, and human-computer interaction.
Author: Nathalie Gontier
Publisher: Springer
ISBN: 3319163450
Category : Science
Languages : en
Pages : 345
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Book Description
Written for non-experts, this volume introduces the mechanisms that underlie reticulate evolution. Chapters are either accompanied with glossaries that explain new terminology or timelines that position pioneering scholars and their major discoveries in their historical contexts. The contributing authors outline the history and original context of discovery of symbiosis, symbiogenesis, lateral gene transfer, hybridization or divergence with gene flow and infectious heredity. By applying key insights from the areas of molecular (phylo)genetics, microbiology, virology, ecology, systematics, immunology, epidemiology and computational science, they demonstrate how reticulate evolution impacts successful survival, fitness and speciation. Reticulate evolution brings forth a challenge to the standard Neo-Darwinian framework, which defines life as the outcome of bifurcation and ramification patterns brought forth by the vertical mechanism of natural selection. Reticulate evolution puts forward a pattern in the tree of life that is characterized by horizontal mergings and lineage crossings induced by symbiosis, symbiogenesis, lateral gene transfer, hybridization or divergence with gene flow and infective heredity, making the “tree of life” look more like a “web of life.” On an epistemological level, the various means by which hereditary material can be transferred horizontally challenges our classic notions of units and levels of evolution, fitness, modes of transmission, linearity, communities and biological individuality. The case studies presented examine topics including the origin of the eukaryotic cell and its organelles through symbiogenesis; the origin of algae through primary and secondary symbiosis and dinoflagellates through tertiary symbiosis; the superorganism and holobiont as units of evolution; how endosymbiosis induces speciation in multicellular life forms; transferrable and non-transferrable plasmids and how they symbiotically interact with their host; the means by which pro- and eukaryotic organisms transfer genes laterally (bacterial transformation, transduction and conjugation as well as transposons and other mobile genetic elements); hybridization and divergence with gene flow in sexually-reproducing individuals; current (human) microbiome and viriome studies that impact our knowledge concerning the evolution of organismal health and acquired immunity; and how symbiosis and symbiogenesis can be modelled in computational evolution.
Author: Marc Schoenauer
Publisher: Springer
ISBN: 3540453563
Category : Computers
Languages : en
Pages : 920
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Book Description
We are proud to introduce the proceedings of the Sixth International Conference on Parallel Problem Solving from Nature, PPSN VI, held in Paris, Prance, on 18-20 September 2000. PPSN VI was organized in association with the Genetic and Evolutionary Computing Conference (GECCO'2000) and the Congress on Evolutionary Computation (CEC'2000), reflecting the beneficial interaction between the conference activities in Europe and in the USA in the field of natural computation. Starting in 1990 in Dortmund, Germany (Proceedings, LNCS vol. 496, Sprin ger, 1991), this biannual meeting has been held in Brussels, Belgium (Procee dings, Elsevier, 1992), Jerusalem, Israel (Proceedings, LNCS vol. 866, Springer, 1994), Berlin, Germany (Proceedings, LNCS vol. 1141, Springer, 1996), and Amsterdam, The Netherlands (Proceedings, LNCS vol. 1498, Springer, 1998), where it was decided that Paris would be the location of the 2000 conference with Marc Schoenauer as the general chair. The scientific content of the PPSN conference focuses on problem solving pa radigms gleaned from a natural models. Characteristic for Natural Computing is the metaphorical use of concepts, principles and mechanisms underlying natural systems, such as evolutionary processes involving mutation, recombination, and selection in natural evolution, annealing or punctuated equilibrium processes of many-particle systems in physics, growth processes in nature and economics, collective intelligence in biology, DNA-based computing in molecular chemistry, and multi-cellular behavioral processes in neural and immune networks.
Author: Ryszard S. Michalski
Publisher: Morgan Kaufmann
ISBN: 9781558602519
Category : Computers
Languages : en
Pages : 798
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Book Description
Multistrategy learning is one of the newest and most promising research directions in the development of machine learning systems. The objectives of research in this area are to study trade-offs between different learning strategies and to develop learning systems that employ multiple types of inference or computational paradigms in a learning process. Multistrategy systems offer significant advantages over monostrategy systems. They are more flexible in the type of input they can learn from and the type of knowledge they can acquire. As a consequence, multistrategy systems have the potential to be applicable to a wide range of practical problems. This volume is the first book in this fast growing field. It contains a selection of contributions by leading researchers specializing in this area. See below for earlier volumes in the series.
