Evolutionary computing

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  • This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. Concerning evolutionbased optimization methods, this book presents diverse versions of genetic algorithms, genetic programming, and performance studies and analyses, as well as several particle swarm optimizers and hybrid approaches using neural networks and artificial immunological systems for multi-objective optimization....

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  • Evolutionary computation has been widely used in computer science for decades. Even though it started as far back as the 1960s with simulated evolution, the subject is still evolving. During this time, new metaheuristic optimization approaches, like evolutionary algorithms, genetic algorithms, swarm intelligence, etc., were being developed and new fields of usage in artificial intelligence, machine learning, combinatorial and numerical optimization, etc., were being explored.

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  • With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field.

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  • Man has learned much from studies of natural systems, using what has been learned to develop new algorithmic models to solve complex problems. This book presents an introduction to some of these technological paradigms, under the umbrella of compu- tational intelligence (CI).

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  • Physicists pretend not only to know everything, but also to know everything bet- ter. This applies in particular to computational statistical physicists like US. Thus many of our colleagues have applied their computer simulation techniques to fields outside of physics, and have published sometimes in biological, economic or sociological journals, and publication flow in the opposite direction has also started.

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  • From the standpoint of the average user, today’s computer networks are extremely primitive compared to other networks. While the national power, transportation, and telecommunications networks have evolved to their present state of sophistication and ease of use, computer networks are at an early stage in their evolutionary process. Eventually, users will be unaware that they are using any computer but the one on their desk, because it will have the capability to reach out across the national network and obtain whatever computational resources that are necessary...

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  • This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics.

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  • ADO.NET is the data access component of Microsoft’s new .NET Framework. Microsoft bills ADO.NET as “an evolutionary improvement” over previous versions of ADO, a claim that has been hotly debated since its announcement. It is certainly true that the ADO.NET object model bears very little relationship to earlier versions of ADO.

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  • Microsoft Analysis Services is server-based platform for on-line analytical processing (OLAP) and Data Mining Services provided with Microsoft SQL Server 2005. This tool is not new, but it has been completely rewritten from the ground up. While Analysis Services 2000 was more of an evolutionary release building on the same OLAP architecture that Microsoft (MS) introduced with SQL 7, the new version of SSAS is a fundamental change, built on Unified Dimensional Model (UDM). Microsoft released SSAS in November 2005, and it ships as an add-on to SQL Server 2005 just like its predecessors....

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  • This book is not organized in a traditional chapter format. Instead I have chosen to include example programs that exhaustively illustrate the important points of C in an evolutionary manner. By working through these programs you can teach yourself C. I assume you already know how to program and are familiar with standard algorithms.

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  • Immediately after the first drafts of the human genome sequence were reported almost a decade ago, the importance of genomics and functional genomics studies became well recognized across the broad disciplines of biological sciences research.

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  • Genetic programming (GP) is a branch of Evolutionary Computing that aims the automatic discovery of programs to solve a given problem. Since its appearance, in the earliest nineties, GP has become one of the most promising paradigms for solving problems in the artificial intelligence field, producing a number of human-competitive results and even patentable new inventions. And, as other areas in Computer Science, GP continues evolving quickly, with new ideas, techniques and applications being constantly proposed....

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  • What does the next generation network look like? Most network planners have an IP-based answer to that question. The better question is this—what does the next generation network look like at specific points in time: one year, three years, seven years from now, and so on into the future? The answer to that question is fuzzy because migration to the next generation network is an evolutionary process.

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  • In the chapter limit properties of genetic algorithms and theproblem of their classification are elaborated. Recently one can observe an increasing interest in properties of genetic algorithms modelled by Markov chains (Vose, Rowe). However, the known results are mainly limited to existence theorems. They say that there exists a limit distribution for a Markov chain describing a simple genetic algorithm. In the chapter we perform the next step on this way and present a formula for this limit distribution for a Markov chain.

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  • Genetic Algorithms (GAs) are global optimization techniques used in many real-life applications. They are one of several techniques in the family of Evolutionary Algorithms – algorithms that search for solutions to optimization problems by “evolving” better and better solutions. A Genetic Algorithm starts with a population of possible solutions for the desired application. The best ones are selected to become parents and then, using genetic operators like crossover and mutation, offspring are generated....

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  • Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations.

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  • This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented.

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  • What does the wired portion of a next generation wireless network look like? The better question is this—what does the wired portion of a next generation wireless network look like at specific points in time: one year, three years, seven years from now, and so on into the future? That question is hard to answer because migration to next generation networks is an evolutionary process.

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  • This second edition presents new chapters on (a) the utilization of mutants as highresolution nanosensors of short-living protein structures and protein nanophysics (Chap. 11) and (b) the recently developed method of evolutionary computer programming (Chap. 12), respectively. In the latter method, computer programs evolve themselves towards a higher performance. In contrast to simple selflearning programs, the code of the evolved program differs significantly from that of the original "wild-type" program.

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  • Studies in Computational Intelligence, Volume 70 Editor-in-chief Prof. Janusz Kacprzyk Systems Research Institute Polish Academy of Sciences ul. Newelska 6 01-447 Warsaw Poland E-mail: kacprzyk@ibspan.waw.pl Further volumes of this series can be found on our homepage: springer.com Vol. 49. Keshav P. Dahal, Kay Chen Tan, Peter I. Cowling (Eds.) Evolutionary Scheduling, 2007 ISBN 978-3-540-48582-7 Vol. 50. Nadia Nedjah, Leandro dos Santos Coelho, Luiza de Macedo Mourelle (Eds.) Mobile Robots: The Evolutionary Approach, 2007 ISBN 978-3-540-49719-6 Vol. 51.

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