Search Results for "grammatical evolution"
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Showing 5 open source projects for "grammatical evolution"
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MOEA Framework
A Free and Open Source Java Framework for Multiobjective Optimization
The MOEA Framework is a free and open source Java library for developing and experimenting with multiobjective evolutionary algorithms (MOEAs) and other general-purpose multiobjective optimization algorithms. The MOEA Framework supports genetic algorithms, differential evolution, particle swarm optimization, genetic programming, grammatical evolution, and more. A number of algorithms are provided out-of-the-box, including NSGA-II, NSGA-III, ε-MOEA, GDE3 and MOEA/D. In addition, the MOEA Framework provides the tools necessary to rapidly design, develop, execute and statistically test optimization algorithms. -
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Grammatical Optimization
A java based framework for grammatical evolution.
This project is a result for my curiosity for how grammatical evolution (GE) works. Eventually, I made this wrapper for GE that should work with any numerical optimization algorithm. So the idea behind GE is that it takes production rules for computer programs using a context free grammar in Backus Naur form. The production rules can be used to evolve computer programs by running an algorithm such as a genetic algorithm or a particle swarm optimizer in the background. -
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EurekaOptima is a framework for optimization containing the implementation of several algorithms like Genetic Algorithm (GA), Clonal Selection Algorithm (CLONALG), Grammatical Evolution (GE), Differential Evolution (DE), and Evolution Strategy (ES).
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EpochX is an open source genetic programming framework, specifically for analysing the properties of evolutionary automatic programming. It supports 3 popular representations - Strongly-Typed GP, Context-Free Grammar GP and Grammatical Evolution.
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This project provides a set of Python tools for creating various kinds of neural networks, which can also be powered by genetic algorithms using grammatical evolution. MLP, backpropagation, recurrent, sparse, and skip-layer networks are supported.
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