الصفحة 18
الصفحة 18
Evolvable systems : From biology to hardware ; 6th International Conference, ICES 2005, Sitges, Spain, September 12-14, 2005, Proceedings
The flying machines proposed by Leonardo da Vinci in the fifteenth century, the se- reproducing automata theory proposed by John von Neumann in the middle of the twentieth century and the current possibility of designing electronic and mechanical systems using evolutionary principles are all examples of the efforts made by humans to explore the mechanisms present in biological systems that permit them to tackle complex tasks. These initiatives have recently given rise to the emergent field of b- inspired systems and evolvable hardware. The inaugural workshop, Towards Evolvable Hardware, took place in Lausanne in October 1995, followed by the successive events of the International Conference on Evolvable Systems: From Biology to Hardware, held in Tsukuba (Japan) in October 1996, in Lausanne (Switzerland) in September 1998, in Edinburgh (UK) in April 2000, in Tokyo (Japan) in October 2001, and in Trondheim (Norway) in March 2003. Following the success of these past events the sixth international conference was aimed at presenting the latest developments in the field, bringing together researchers who use biologically inspired concepts to implement real systems in artificial intelligence, artificial life, robotics, VLSI design, and related domains. The sixth conference consolidated this biennial event as a reference meeting for the community involved in bio-inspired systems research. All the papers received were reviewed by at least three independent reviewers, thus guaranteeing a high-quality bundle for ICES 2005.
Evolvable Machines : Theory & Practice
Methods for the artificial evolution of active components, such as programs and hardware, are rapidly developing branches of adaptive computation and adaptive engineering. "Evolvable Machines" reports innovative and significant progress in automatic and evolutionary methodology applied to machine design. This book presents theoretical as well as practical chapters concentrating on Evolvable Robots, Evolvable Hardware Synthesis, as well as Evolvable Design.
Evolutionary Synthesis of Pattern Recognition Systems
Evolutionary Synthesis of Pattern Recognition Systems presents novel effective approaches based on evolutionary computational techniques, such as genetic programming (GP), linear genetic programming (LGP), coevolutionary genetic programming (CGP) and genetic algorithms (GA) to automate the synthesis and analysis of object detection and recognition systems. The book’s concepts, principles, and methodologies will enable readers to automatically build robust and flexible systems—in a systematic manner—that can provide human-competitive performance and reduce the cost of designing and maintaining these systems. Its content covers all key aspects of object recognition: object detection, feature selection, feature discovery, object recognition, domain knowledge. Basic knowledge of programming and data structures, and some calculus, is presupposed.ing the book’s novel ideas
Evolutionary Swarm Robotics : Evolving Self-Organising Behaviours in Groups of Autonomous Robots
In this book the use of ER techniques for the design of self-organising group behaviours, for both simulated and real robots is introduced. This research has a twofold value. From an engineering perspective, an automatic methodology for synthesising complex behaviours in a robotic system is described.
Evolutionary Stasis and Change in the Dominican Republic Neogene
In practice, however, science is no less susceptible to fads, culture shifts, and pendulum swings than any other realm of human endeavor. This is an especially important feature of science to keep in mind in the present climate of shrinking government funding (at least in prop- tion to the demand) and the resulting susceptibility of individual scientists and entire disciplines to being influenced by the changing priorities of funding agencies (even if, as such agencies maintain, those priorities come ultimately “from the c- munity”). The present volume is in several important respects a testimonial to both the threats and opportunities that such scientific culture swings pose, both for the individual researcher and a wider field. When scientific research in the Dominican Republic Neogene began more than a century ago, paleontology was an essentially descriptive discipline, focused mainly on finding, describing, and documenting the taxa represented in the fossil record, and (especially in invertebrate paleontology) on using these taxa for bi- tratigraphic correlation.
Evolutionary Scheduling
Evolutionary scheduling is a vital research domain at the interface of two important sciences - artificial intelligence and operational research. Scheduling problems are generally complex, large scale, constrained, and multi-objective in nature, and classical operational research techniques are often inadequate at solving them effectively. With the advent of computation intelligence, there is renewed interest in solving scheduling problems using evolutionary computational techniques. These techniques, which include genetic algorithms, genetic programming, evolutionary strategies, memetic algorithms, particle swarm optimization, ant colony systems, etc, are derived from biologically inspired concepts and are well-suited to solve scheduling problems since they are highly scalable and flexible in terms of handling constraints and multiple objectives. This edited book gives an overview of many of the current developments in the large and growing field of evolutionary scheduling, and demonstrates the applicability of evolutionary computational techniques to solve scheduling problems, not only to small-scale test problems, but also fully-fledged real-world problems.
Evolutionary Multiobjective Optimization : Theoretical Advances and Applications
Evolutionary Multiobjective Optimization is a rare collection of the latest state-of-the-art theoretical research, design challenges and applications in the field of multiobjective optimization paradigms using evolutionary algorithms. It includes two introductory chapters giving all the fundamental definitions, several complex test functions and a practical problem involving the multiobjective optimization of space structures under static and seismic loading conditions used to illustrate the various multiobjective optimization concepts.
Evolutionary Multi-Criterion Optimization ; 4th International Conference, EMO 2007, Matsushima, Japan, March 5-8, 2007, Proceedings
Multicriterion optimization refers to problems with two or more objectives (normally in conflict with each other) which must be simultaneously satisfied. Evolutionary algorithms have been used for solving multicriterion optimization problems for over two decades, gaining an increasing attention from industry. This book included four keynote speakers: Hirotaka Nakayama on aspiration level methods, Kay Chen Tan on large and computationally intensive real-world MO optimization problems, Carlos Fonseca on decision making, and Gary B. Lamont on design of large-scale network centric systems.
Evolutionary Multi-Criterion Optimization ; 3rd International Conference, EMO 2005, Guanajuato, Mexico, March 9-11, 2005, Proceedings
Constitutes the refereed proceedings of the Third International Conference on Evolutionary Multi-Criterion Optimization, EMO 2005, held in Guanajuato, Mexico, in March 2005.
Evolutionary Microeconomics
Classical microeconomics is intended to explain how a price system is able to coordinate the economic agents. But even if it can be extended to incomplete information and externalities, it remains grounded on very heroic assumptions. Agents are endowed with a very strong rationality, equilibrium is stated without a concrete process to achieve it, market is the unique institution considered. Evolutionary microeconomics is aimed at bypassing these limitations by considering a dynamic approach, however not biologically oriented.
Evolutionary Intelligence : An introduction to theory and applications with Matlab
This book gives a good introduction to evolutionary computation for those who are first entering the field and are looking for insight into the underlying mechanisms behind them. Emphasizing the scientific and machine learning applications of genetic algorithms instead of applications to optimization and engineering, the book could serve well in an actual course on adaptive algorithms.
Evolutionary Genomics : Statistical and Computational Methods
This book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results.
Evolutionary Equations : Picard's Theorem for Partial Differential Equations, and Applications
This book provides a solution theory for time-dependent partial differential equations, which classically have not been accessible by a unified method. Instead of using sophisticated techniques and methods, the approach is elementary in the sense that only Hilbert space methods and some basic theory of complex analysis are required. Nevertheless, key properties of solutions can be recovered in an elegant manner.
Evolutionary Epistemology, Language and Culture : A Non-Adaptationist, Systems Theoretical Approach
Today we know that natural selection and evolution are far from synonymous and that they do not explain isomorphic phenomena in the world. ‘Taking Darwin seriously’ is the way to go, but today the time has come to take alternative and complementary theories that developed after the Modern Synthesis, equally seriously, and, furthermore, to examine how language and culture can merit from these diverse disciplines.As this volume will make clear, a specific inter- and transdisciplinary approach is one of the next crucial steps that needs to be taken, if we ever want to unravel the secrets of phenomena such as language and culture.
Evolutionary Computer Music
The evolutionary computation approach to music is an exciting new development for composers and musicologists alike. For composers, it provides an innovative and natural means for generating musical ideas from a specifiable set of primitive components and processes. For musicologists, these techniques are used to model the cultural transmission and change of a population's body of musical ideas over time. In both cases, musical evolution can be guided by a variety of constraints and tendencies built into the system, such as realistic psychological factors that influence the way music is expressed, experienced, learned, stored, modified, and passed on among individuals. This book discusses not only the applications of evolutionary computation to music, but also the tools needed to create and study such systems. These tools are drawn in part from research into the origins and evolution of biological organisms, ecologies, and cultural systems on the one hand, and from computer simulation methodologies on the other. They can be combined to create surrogate artificial worlds populated by interacting simulated organisms in which complex musical experiments can be performed that would otherwise be impossible.
Evolutionary computation, machine learning and data mining in bioinformatics ; 6th European Conference, EvoBIO 2008, Naples, Italy, March 26-28, 2008. Proceedings
The feld of bioinformatics has two main objectives: the creation and main- nance of biological databases, and the discovery of knowledge from life sciences data in order to unravel the mysteries of biological function, leading to new drugs and therapies for human disease. Life sciences data come in the form of biological sequences, structures, pathways, or literature. One major aspect of discovering biological knowledge is to search, predict, or model specifc infortioninagivendatasetinorderto generate new in teresting knowledge.Computer science methods such as evolutionary computation, machine learning, and data mining all have a great deal to ofer the feld of bioinformatics.
Evolutionary computation, machine learning and data mining in bioinformatics ; 5th European Conference, EvoBIO 2007, Valencia, Spain, April 11-13, 2007, Proceedings
This book Covers brings together experts in computer science with experts in bioinformatics and the biological sciences. It presents contributions on fundamental and theoretical issues along with papers dealing with different applications areas.
Evolutionary Computation in Practice
This book is loaded with examples in which computer scientists and engineers have used evolutionary computation—programs that mimic natural evolution—to solve real problems. They aren’t abstract, mathematically intensive papers, but accounts of solving important problems, including tips from the authors on how to avoid common pitfalls, maximize the effectiveness and efficiency of the search process, and many other practical suggestions.
Evolutionary Computation in Dynamic and Uncertain Environments
This book provides a compilation on the state-of-the-art and recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The motivation for this book arises from the fact that some degree of uncertainty in characterizing any realistic engineering systems is inevitable. Representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums, are presented. "Evolutionary Computation in Dynamic and Uncertain Environments" is a valuable reference for scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, natural computing and evolutionary computation.
Evolutionary Computation in Data Mining
This carefully edited book reflects and advances the state of the art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms. It emphasizes the utility of different evolutionary computing tools to various facets of knowledge discovery from databases, ranging from theoretical analysis to real-life applications. "Evolutionary Computation in Data Mining" provides a balanced mixture of theory, algorithms and applications in a cohesive manner, and demonstrates how the different tools of evolutionary computation can be used for solving real-life problems in data mining and bioinformatics.
