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    Applied Mathematics and Computation addresses work at the interface between applied mathematics, numerical computation, and applications of systems – oriented ideas to the physical, biological, social, and behavioral sciences, and emphasizes papers of a computational nature focusing on new algorithms, their analysis and numerical results. In addition to presenting research papers, Applied Mathematics and Computation publishes review articles and single–topics issues. Please also visit the Electronic Service of Applied Mathematics and Computation at http://www.elsevier.com/locate/amc.
    5 years ago by @thorade
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    Algorithms, an international, peer-reviewed Open Access journal.
    5 years ago by @thorade
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    Presents original and review papers on all aspects of numerical algorithms Coverage includes new algorithms, theoretical results, implementation, numerical stability, complexity, parallel computing, subroutines and applications Also provides book reviews and announcements of scientific meetings The journal Numerical Algorithms presents original and review papers on all aspects of numerical algorithms: new algorithms, theoretical results, implementation, numerical stability, complexity, parallel computing, subroutines and applications. Papers on computer algebra related to obtaining numerical results also included. The journal offers high quality papers containing material not published elsewhere. The journal also provides book reviews and announcements of scientific meetings.
    5 years ago by @thorade
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    Netlib is a collection of mathematical software, papers, and databases.
    6 years ago by @thorade
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    A couple of posts ago, I talked about a simple monte carlo simulation for diffusion limited aggregation. In this post, I’m going to talk about another algorithm that, at its heart, is based on random numbers. Unlike DLA though, this algorithm isn’t about simulating a physical system. Instead, it is about a method for solving optimization problems that are generally poorly suited to traditional numerical optimization techniques. This post describes an application of a library implementing the GEP method posed by Cândida Ferreira nearly 10 years ago. I started messing with GEP shortly after the paper “Gene Expression Programming: A New Adaptive Algorithm for Solving Problems” was published in the journal Complex Systems. The paper sat in a pile for a while, and about two years ago I picked it up again and started to implement it as a Haskell library.
    7 years ago by @thorade
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    Evocosm is a set of classes that abstract the fundamental components of an evolutionary algorithm. I'll list the components here with a bit of introduction; you can review the details of the classes by downloading the code archives or by reviewing the online documentation (see the menu at the article's beginning for code and documentation links.) All class documentation was generated from source code comments using doxygen. These docs have not been thoroughly proofread, so they may contain a few typos and minor errors. Self-publishing has taught me the value of a good proofreader... ;} Evolutionary algorithms come in a variety of shapes and flavors, but at their core, they all share certain characteristics: populations that reproduce and mutate through a series of generations, producing future generations based on some measure of fitness. An amazing variety of algorithms can be built on that general framework, which leads me to construct a set of core classes as the basis for future applications.
    7 years ago by @thorade
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    EvA2 (an Evolutionary Algorithms framework, revised version 2) is a comprehensive heuristic optimization framework with emphasis on Evolutionary Algorithms implemented in Java. It is a revised version of the JavaEvA optimization toolbox, which has been developed as a resumption of the former EvA software package. EvA2 integrates several derivation free optimization methods, preferably population based, such as Evolution Strategies (ES), Genetic Algorithms (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), as well as classical techniques such as multi-start Hill Climbing or Simulated Annealing. Besides typical single-objective problems, multi-modal and multi-objective problem are handled directly by the EvA2 framework. Via the Java mechanism of Remote Method Invocation (RMI), the algorithms of EvA2 can be distributed over network nodes based on a client-server architecture. EvA2 aims at two groups of users. Firstly, the end user who does not know much about the theory of Evolutionary Algorithms, but wants to use Evolutionary Algorithms to solve an application problem. Secondly, the scientific user who wants to investigate the performance of different optimization algorithms or wants to compare the effect of alternative or specialized evolutionary or heuristic operators. The latter usually knows more about evolutionary algorithms or heuristic optimization and is able to extend EvA2 by adding specific optimization strategies or solution representations. EvA2 is being used as teaching aid in lecture tutorials, as a developing platform in student research projects and applied to numerous optimisation problems within active research and ongoing industrial cooperations.
    7 years ago by @thorade
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publications  43