Search Results for "evolution linux"
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Showing 21 open source projects for "evolution linux"
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PlatEMO
Evolutionary multi-objective optimization platform
Evolutionary multi-objective optimization platform. PlatEMO consists of a number of MATLAB functions without using any other libraries. Any machines able to run MATLAB can use PlatEMO regardless of the operating system. PlatEMO includes more than ninety existing popular MOEAs, including genetic algorithm, differential evolution, particle swarm optimization, memetic algorithm, estimation of distribution algorithm, and surrogate model-based algorithm. Most of them are representative algorithms... -
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Evolutionary Algorithm
Evolutionary Algorithm using Python
Evolutionary Algorithm is an educational Python project that demonstrates evolutionary computation techniques such as genetic algorithms, evolution strategies, and neuroevolution in a clear and accessible way. Rather than being a single monolithic library, this repository provides a series of self-contained examples showing how different population-based search methods solve optimization problems and adapt candidate solutions over generations. Users can explore basic genetic algorithm... -
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Evolutionary.jl
Evolutionary & genetic algorithms for Julia
A Julia package for evolutionary & genetic algorithms. The package can be installed with the Julia package manager. -
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The ASCO project aims to bring circuit optimization capabilities to existing SPICE simulators using a high-performance parallel differential evolution (DE) optimization algorithm. It supports Eldo, HSPICE, LTspice, Spectre, and Qucs.
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OpenDino
Open Source Java platform for Optimization, DoE, and Learning.
OpenDino is an open source Java platform for optimization, design of experiment and learning. It provides a graphical user interface (GUI) and a platform which simplifies integration of new algorithms as "Modules". Implemented Modules Evolutionary Algorithms: - CMA-ES - (1+1)-ES - Differential Evolution Deterministic optimization algorithm: - SIMPLEX Learning: - a simple Artificial Neural Net Optimization problems: - test functions - interface for executing... -
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The source code has been migrated and is now hosted on Github: https://github.com/jenetics/jenetics Jenetics is an advanced Genetic Algorithm, Evolutionary Algorithm and Genetic Programming library, respectively, written in modern day Java. It is designed with a clear separation of the several algorithm concepts, e. g. Gene, Chromosome, Genotype, Phenotype, Population and fitness Function. Jenetics allows you to minimize or maximize the given fitness function without tweaking it. In...
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Opt4J
Modular Java framework for meta-heuristic optimization
Opt4J is an open source Java-based framework for evolutionary computation. It contains a set of (multi-objective) optimization algorithms such as evolutionary algorithms (including SPEA2 and NSGA2), differential evolution, particle swarm optimization, and simulated annealing. The benchmarks that are included comprise ZDT, DTLZ, WFG, and the knapsack problem. The goal of Opt4J is to simplify the evolutionary optimization of user-defined problems as well as the implementation of arbitrary... -
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S-Match
S-Match is a semantic matching framework.
S-Match is a semantic matching framework. S-Match takes any two tree like structures (such as database schemas, classifications, lightweight ontologies) and returns a set of correspondences between those tree nodes which semantically correspond to one another. S-Match contains implementations of the semantic matching, minimal semantic matching and structure preserving semantic matching algorithms. S-Match applies as a solution in many fields, including: information integration,... -
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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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A self-contained, fully configurable Java "game" to simulate multi-species evolution. Design species by optionally specifying every attribute; modify any or all environmental settings; let them loose to eat, fight, procreate, die, and Evolve!
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The Mars Rover Simulator project is based on the evolutionary robotics paradigm where an artificial agent acquires its skills through the process of artificial evolution. This simulator can be useful to evolve neural network controllers for the rover
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This sofware is designed to help different chess clubs to set up their own rating system. It uses USCF approximation formulas for rating calculations. Working example at newarkcc.vfutbole.ru
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This is implementation of parallel genetic algorithm with "ring" insular topology. Algorithm provides a dynamic choice of genetic operators in the evolution of. The library supports the 26 genetic operators. This is cross-platform GA written in С++.
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"Blue Planet" is a research project simulating the behaviour and darwinian evolution of unicellular lifeforms, each controlled by its own genetic program. Moreover, "Blue Planet Inhabitants" are suited for swarm intelligence and swarm research.
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Simulation of plants, prey, and predators, using sense->think->action paradigm with neural net processing. Reproduction and mutation implemented.
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The Gene Expression Programming Framework in Java. It separates the process of evolution from the process of interpretation of the chromosome, allowing the use of various schemes in the chromosome.
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The Distributed Genetic Programming Framework is a scalable Java genetic programming environment. It comes with an optional specialization for evolving assembler-syntax algorithms. The evolution can be performed in parallel in any computer network.
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A library for Cartesian Genetic Programming (CGP.) This project includes a simple Evolution Strategy library that can be used for evolution.
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The Airs GPT project experiments with Steady State Genetic Programming create agents that play tag. It emphasises co-evolution through competition, and provides the user with some tools to help them visualize what is taking place.
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biojazz
In silico evolution of biochemical networks.
In silico evolution of biochemical networks using a genetic algorithm combined with a rule-based biochemical network model. -
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GENet
A genetic algorithm framework for artificial neural networks.
A genetic algorithm framework to allow the evolution of synapse weights and topologies of artificial neural networks.
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