Search Results for "artificial evolution"
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Showing 17 open source projects for "artificial evolution"
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DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
DeepCTR-Torch is an easy-to-use, Modular and Extendible package of deep-learning-based CTR models along with lots of core components layers that can be used to build your own custom model easily.It is compatible with PyTorch.You can use any complex model with model.fit() and model.predict(). With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical... -
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OculiX
Visual Automation IDE โ automate anything you see on screen
OculiX is the evolution of SikuliX, actively maintained with the full agreement of its original creator RaiMan. Automate any desktop application using image recognition (OpenCV) and OCR (Tesseract + PaddleOCR). No access to source code or DOM required โ if you can see it, you can automate it. Key features: - Guided step-by-step recorder with live code preview - Image recognition via OpenCV 4.10 - Dual OCR: Tesseract (built-in) + PaddleOCR (neural, high precision) - Local and remote... -
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scikit-opt
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing
scikit-opt is a Python library for solving optimization problems with evolutionary and swarm-intelligence algorithms. It includes genetic algorithms, particle swarm optimization, differential evolution, simulated annealing, ant colony optimization, immune algorithms, and artificial fish swarms. The package can address continuous objectives, constrained problems, and combinatorial tasks such as the traveling salesman problem. A consistent workflow lets users define an objective, configure an optimizer, run iterations, and inspect the best solution. ... -
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OpenDino
Open Source Java platform for Optimization, DoE, 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 other programs (solvers) - parallel execution of problems - distributed execution of problems via socket connection between computers Others: - data storage - data analyser and viewer -
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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.ย 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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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, ฮต-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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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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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 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 ะก++.
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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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This is a compiler which produces ANNs out of a simple, procedural programming language. Those programs can then be subject to genetic algorithms and neuro-evolution to enhance them automatically.
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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.
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Gnubert will be a system to use the power of many computers across the internet to solve problems using evolutionary techniques. Any user may define a problem and the system will attempt to solve it.
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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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