Genetic computer

Xem 1-20 trên 124 kết quả Genetic computer
  • Tham khảo sách 'genetic computer school final project: knowledge base system - book 1 - user guide', công nghệ thông tin, cơ sở dữ liệu phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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  • Tham khảo sách 'genetic computer school final project: knowledge base system - book 2 - system guide', công nghệ thông tin, tin học văn phòng phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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  • A web-based Knowledge Base System is an online Knowledge resource on the net. Knowledge Base System creates a huge archive of information on a website that help people gain knowledge. Knowledge Base System also is a communication tool that helps people share knowledge quickly and easily.

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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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  • 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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  • The purpose of The Computer Science Handbook is to provide a single comprehensive reference for com- puter scientists, software engineers, and IT professionals who wish to broaden or deepen their understand- ing in a particular subfield of computer science. Our goal is to provide the most current information in each of the following eleven subfields in a form that is accessible to students, faculty, and professionals in computer science

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  • Tham khảo sách 'an event service to support grid computational environments', kinh doanh - tiếp thị, tổ chức sự kiện phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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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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  • Table 62-1 Selected Databases Relevant for Genomics and Genetic Disorders Site URL Comment National Center Biotechnology Information (NCBI) for http://www.ncbi.nlm.nih.gov/ Molecular biology information, public databases, computational Site URL Comment biology. for Software analyzing genome data. Extensive links to other databases, genome resources, tutorials and National Human Genome http://www.genome.gov/ Web providing links Research Institute information about the human genome sequence, genomes of other organisms, and research genomic ...

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  • An ultimate goal of modern biology is to understand how the genetic blueprint of cells (genotype) determines the structure, function, and behavior of a living organism (phenotype). At the center of this scientific endeavor is characterizing the biochemical and cellular roles of proteins, the working molecules of the machinery of life. A key to understanding of functional proteins is the knowledge of their folded structures in a cell, as the structures provide the basis for studying proteins’ functions and functional mechanisms at the molecular level....

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  • A Field Guide to Genetic Programming Riccardo Poli Department of Computing and Electronic Systems University of Essex – UK rpoli@essex.ac.uk William B. Langdon Departments of Biological and Mathematical Sciences University of Essex – UK wlangdon@essex.ac.uk Nicholas F. McPhee Division of Science and Mathematics University of Minnesota, Morris – USA mcphee@morris.umn.edu with contributions by John R. Koza Stanford University – USA john@johnkoza.com March 2008

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  • This paper presents a genetic algorithm based approach to the automatic discovery of finitestate a u t o m a t a (FSAs) from positive data. FSAs are commonly used in computational phonology, but - given the limited learnability of FSAs from arbitrary language subsets - are usually constructed manually. The approach presented here offers a practical automatic method that helps reduce the cost of manual FSA construction.

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  • As you worked your way through high school, or otherwise worked to prepare yourself for college, you were probably unaware that an information explosion was taking place in the field of biology. This explosion, brought on by advances in biotechnology and communicated by faster, more powerful computers, has allowed scientists to gather data more quickly and disseminate data to colleagues in the global scientific community with the click of a mouse.

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  • Genetic Algorithms are a family of computational models inspired by evolution.

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  • These pages introduce some fundamentals of genetics algorithms. Pages are intended to be used for learning about genetics algorithms without any previous knowledge from this area. Only some knowledge of computer programming is assumed. You can find here several interactive Java applets demonstrating work of genetic algorithms.

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  • In recent years many successful machine learning applications have been developed, ranging from data mining programs that learn to detect fraudulent credit card transactions, to information filtering systems that learn user’s reading preferences, to autonomous vehicles that learn to drive on public highways. At the same time, machine learning techniques such as rule induction, neural networks, genetic learning, case-based reasoning, and analytic learning have been widely applied to real-world problems.

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  • Bioinformatics For Dummies is packed with valuable information that introduces you to this exciting new discipline. This easy-to-follow guide leads you step by step through every bioinformatics task that can be done over the Internet. Forget long equations, computer-geek gibberish, and installing bulky programs that slow down your computer. You’ll be amazed at all the things you can accomplish just by logging on and following these trusty directions. You get the tools you need to:

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  • Information Theory, Evolution, and the Origin of Life presents a timely introduction to the use of information theory and coding theory in molecular biology. The genetical information system, because it is linear and digital, resembles the algorithmic language of computers. George Gamow pointed out that the application of Shannon’s information theory breaks genetics and molecular biology out of the descriptive mode into the quantitative mode, and Dr. Yockey develops this theme, discussing how information theory and coding theory can be applied to molecular biology....

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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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