Search Results for "evolution linux"
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Showing 32 open source projects for "evolution linux"
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Biogenesis Color Mod
Mod for evolution simulation and artificial life program Biogenesis
You can download the Color Mod at the Biogenesis main site too (included in the main download now). You also find the sourcecode there. If you want to post a review, remember that this is only a modification, so comment on my changes, not on the simulation. Original project by Joan Queralt Molina: https://sourceforge.net/projects/biogenesis/ Includes Tyler Colemans "Features Mod": https://sourceforge.net/projects/biogenesisfeatu/ Biogenesis discussion... -
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Biogenesis is an artificial life program that simulates the processes involved in the evolution of organisms. It shows colored segment based organisms that mutate and evolve in a 2D environment. Biogenesis is based on Primordial Life.
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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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popt4jlib
Parallel Optimization Library for Java
popt4jlib is an open-source parallel optimization library for the Java programming language supporting both shared memory and distributed message passing models. Implements a number of meta-heuristic algorithms for Non-Linear Programming, including Genetic Algorithms, Differential Evolution, Evolutionary Algorithms, Simulated Annealing, Particle Swarm Optimization, Firefly Algorithm, Monte-Carlo Search, Local Search algorithms, Gradient-Descent-based algorithms, as well as some well-known... -
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DE-HEoC
DE-based Weight Optimisation for Heterogeneous Ensemble
We propose the use of Differential Evolution algorithm for the weight adjustment of base classifiers used in weighted voting heterogeneous ensemble of classifier. Average Matthews Correlation Coefficient (MCC) score, calculated over 10-fold cross-validation, has been used as the measure of quality of an ensemble. DE/rand/1/bin algorithm has been utilised to maximize the average MCC score calculated using 10-fold cross-validation on training dataset. The voting weights of base classifiers are... -
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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... -
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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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DJDarwin
A Genetic algorithm approach to creating beats.
The program puts beats through evolution - survival, mutations, reproduction. The purpose, in the words of Africa Bambaataa - Looking for the perfect beat. Using a genetic algorithmic framework, the user's taste defines a Beat fitness function - she decides which beats survive and breed, and which join the choir invisible. In addition, the user can easily define an automatic fitness function, put the program on 'autopilot' mode, and let it speed up evolution. Different types of... -
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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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The FAKE GAME tool uses natural evolution to evolve Data Mining models. It incorporates several preprocessing, optimization and visualization methods aimed to streamline the Knowledge Discovery process. Knowledge Extraction from data is being automated!
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Swarm Wars
Safety in numbers.
REPOSITORY MOVED TO GITHUB: https://github.com/happyjack27/SwarmWars video sample: http://youtu.be/s5mLNbdBQGY A game where you evolve & compete AI swarms. The organisms use swarm intelligence & ant colony optimization. The organisms can communicate through 3-color signaling as well as by laying beacons. They can attack and repair other organisms. They can select mates, and they can gather and distribute food and material. This behavior is controlled by a genetically evolved... -
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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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GPalta is a genetic programming toolbox that aims for simplicity and speed. It features tree-based genetic programming, optional strong typing, a GUI to control an evolution process (allows to save and continue later) and easy interoperability with Matlab
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Oyster is a distributed registry that exploits semantic web techniques in order to provide a solution for the management of ontology related metadata in distributed environments. It also provides services supporting ontology evolution.
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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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This is a program that can maintain a running conversation with a user. This is an evolution of an Eliza clone called Splotch. This version brings Splotch to the J2SE and MIDP platforms. So, if you like to SMS, get yourself a copy of Splotch!
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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 world of alife based on Physis, inspirated by Avida, written in java. Distributed research platform for evolution experiments.
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An implementation of the Compositional Pattern Producing Networks (CPPNs) and Neuro-Evolution of Augmenting Topologies (NEAT) proposed by Kenneth O. Stanley, based on the Genetic Art tool from Mattias Fagerlund.
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Neural networks evolution (genetic algorithm) written in Java. Now neural networks try to playing Go, Tic-Tac-Toe.
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ANJI (Another NEAT Java Implementation) Built on top of existing OpenSource projects, ANJI is an implementation of NEAT (Neuro-Evolution of Augmenting Topologies), an algorithm for evolving artificial neural networks.
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Kandid generates images using genetic algorithms. In a simulated evolution surprising images can be found calculated with Lisp expressions, Textures, Iterated Function Systems, Linear Cellular Automata, Voroni diagrams, Lindenmayer Systems and POVray
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Set of Java packages to apply Genetic Algorithms to any kind of problem. Various genetic operators are implemented and an example shows of how the system could be applied to digital circuit evolution.
