Search Results for "binary differential evolution" - Page 2
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Showing 32 open source projects for "binary differential evolution"
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Ofeli (as an acronym for Open, Fast and Efficient Level set Implementation) demonstrates how to operate an image segmentation algorithm of Y. Shi and W. C. Karl |1|, using a discrete approach for the approximation of level-set based curve evolution (implicit active contours). This is a novel (2005) and fast algorithm without the need of solving partial differential equations (PDE) while preserving the advantages of level set methods, such as the automatic handling of topological changes. Considerable speedups (×100) have been demonstrated as compared to PDE-based narrow band level-set implementations.
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The DetStoch package offers the user two different methods to compute chemical reactions: Deterministic and Stochastic approaches. It allows the user to play with an intuitive GUI framework and compute the evolution rates of the chemical reactions with the different methods available. The results are displayed in graphical plots of the species concentration over the time. The package is designed to solve any of the following reactions by either one of the two approaches...
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This is a GUI, Java implementation of the Ant Colony Optimisation/Particle Swarm Optimisation (PSO/ACO2) rule induction algorithm. This project was inspired by Ant-Miner, but handles continuous attributes using PSO or now Differential Evolution.
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C++ Differential Evolution Library
A C++ Differential Evolution optimization library
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aeon-framework
AEON is an Evolutionary Computation framework written in C++.
...AEON can be easily configured using text files to use different encodings and/or EAs on the same experiment. The framework currently supports standard EAs (Simple GA, Steady-State GA, etc.), Differential Evolution and a number of direct and indirect encodings (bitstring, real-valued, AGE, etc.). -
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Library for creation of artificial neural networks in C#. Library supports Hopfield nets, Kohonen nets, feedforward networks, several learning techniques (differential evolution, back propagation, quick propagation, resilient propagation etc.)
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VitoshaTrade is Distributed Artificial Neural Network trained by Differential Evolution for prediction of Forex. Project development is in Sofia, Bulgaria. Vitosha is a mountain massif, on the outskirts of Sofia, the capital of Bulgaria.
