Search Results for "evolutionary algorithm"
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Showing 64 open source projects for "evolutionary algorithm"
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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. -
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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. ... -
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MCScanX
MCScanX: Multiple Collinearity Scan toolkit X version
MCScanX is a toolkit for detecting gene synteny and collinearity, aiding in the evolutionary analysis of gene arrangements across multiple genomes. It extends the original MCScan algorithm by incorporating additional utilities for visualization and downstream analyses. -
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Evolutionary Computation Framework
C++ framework for application of any type of evolutionary computation.
ECF is a framework intended for application of any type of evolutionary computation (GA/GP, DE, Clonalg, ES, PSO, ABC, GAn, local search...). It offers simplicity for the end-user (parameterless usage, tutorial) and customization for experienced EC practicioners. -
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Our goal is to create a pervasive platform for application engineers. Therefore we've created a modular and open framework architecture. It supports the engineers in the usage of artificial intelligence, biological behaviours and developing distributed systems in C++, C# and other languages. The basic modules of our framework can be easily used to develop also common applications for Windows, Mac-OSX and Linux platforms. It provides a hugh pool of standard functionality abstracting the...
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SYSU-OpenEdgeAI
A public account displaying works from SYSU-OpenEdgeAI Club
SYSU-OpenEdgeAI Club is a community of geeks who have innovative ideas as well as a great passion for Edge AI technologies. All of our works are open source, and we sincerely welcome all the Edge AI lovers to join us to solve problems and learn! Until 2020-12-27, we have already upload three projects: 1. ov_carplate.zip is a car plate recognition project based on Zedboard Zynq-7000. 2. mnist_hls.zip is a realization of Lenet for MNIST handwritten digit recognition using Vivado hls... -
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Cuda Simulated Annealing GPU Route Plan
An Optimized GPU-Accelerated Route Planning of Multi-UAV Systems Using
An Optimized GPU-Accelerated Route Planning of Multi-UAV Systems Using Simulated Annealing Article CUDA CODE Usage of multiple unmanned aerial vehicles (UAV) in a certain mission makes flight route planning more complicated and slower. In order to obtain better performance, in the literature, most of the researchers propose using evolutionary algorithms and artificial intelligence approaches based on heuristics as optimization techniques. In addition to this, parallel programming approaches increase the computation performance. Therefore, this study focuses to discuss and solve the route planning problem for multi-UAV systems by using optimization techniques based on an evolutionary algorithm: simulated annealing. ... -
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Electronic Circuit Optimization
Optimer: a SPICE base electrical/ electronic circuit optimization tool
This project is dedicated to the optimization of (any) electrical and electronic circuits and components using evolutionary and heuristic algorithms incorporated with SPICE simulators (such as HSPICE, ngSPICE, etc.). We provide Optimer, which is a user graphical interface for circuit design and optimization. Website: https://www.circuitoptimization.com/ E-mail: contact@circuitoptimization.com/ -
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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 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. g. Gene, Chromosome, Genotype, Phenotype, Population and fitness Function. Jenetics allows you to minimize or maximize the given fitness function without tweaking it. ...
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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 network flow and other graph algorithms. A fast parallel implementation of the network simplex method, and some full-fledged parallel/distributed MIP solvers will be added in the next version. ... -
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MUSE
A Multi-algorithm Collaborative Structure-prediction Environment
MUSE is short for Multi-algorithm-collaborative Universal Structure-prediction Environment, which was developed for easy use in structure prediction of materials under ambient or extreme conditions, such as high pressure. It was written in Python and organically combined the multi algorithms including the evolutionary algorithm, the simulated annealing algorithm and the basin hopping algorithm to collaboratively search the global energy minimum of materials with the fixed stoichiometry. ... -
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Machine-Learning-Flappy-Bird
Machine Learning for Flappy Bird using Neural Network
Machine-Learning-Flappy-Bird is an educational machine learning project that demonstrates how an artificial intelligence agent can learn to play the Flappy Bird game using neural networks and evolutionary algorithms. The system simulates a population of birds that each possess their own neural network, which acts as a decision-making controller during gameplay. The neural network receives input features representing the bird’s position relative to the next obstacle and determines whether the bird should flap or remain idle. Over successive generations, a genetic algorithm evolves the neural networks by selecting high-performing agents and recombining their parameters to produce improved offspring. ... -
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Solid Python
A comprehensive gradient-free optimization framework written in Python
Solid is a Python framework for gradient-free optimization. It contains basic versions of many of the most common optimization algorithms that do not require the calculation of gradients, and allows for very rapid development using them. -
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AutoGrow
AutoGrow 3.1 is an evolutionary algorithm that optimizes candidate lig
IMPORTANT NOTE: The latest version of AutoGrow (v4) can be found at http://git.durrantlab.com/jdurrant/autogrow4 More information about the older version posted here on SourceForge can be found at http://autogrow.ucsd.edu AutoGrow 3.1 is an evolutionary algorithm that optimizes candidate ligands for predicted binding affinity and other druglike properties. Though no substitute for the medicinal chemist, AutoGrow attempts to introduce some chemical intuition into the automated optimization process. By carefully crafting chemically feasible druglike molecules, we hope that AutoGrow 3.1 will help supplement the chemist's efforts. -
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faif
C++ header only library with AI and bioinformatics algorithms
C++ header only library, small and fast; Naive Bayesian Classifier, Decision Tree Classifier (ID3), DNA/RNA nucleotide second structure predictor, timeseries management, timeseries prediction, generic Evolutionary Algorithm, generic Hill Climbing algorithm and others. -
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MicroGP
A multi-purpose extensible self-adaptive evolutionary algorithm
MicroGP (µGP, ugp) is a versatile optimizer able to outperform both human experts and conventional heuristics in finding the optimal solution of hard problems. It is an evolutionary algorithm since it mimics some principles of the Neo-Darwinian paradigm. ⚠️ A new version is available on https://github.com/squillero/microgp4 -
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MS-ML Studio
Free and Open-Source Tool for Mass Feature Extraction
Mass spectrometer is a useful tool for biochemistry research field analysis. It is used to identify microorganisms, protein, chemical compounds and complex diseases by blood analysis. There is many tools and libraries to handle different steps of mass spectrometry (MS) data analysis. However, the integration of all these tools lacks user friendly approaches. We developed a machine learning solution capable to handle all steps of MS data analysis. The ML-MS Tool is an free software that... -
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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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SharpNEAT is an optimised, neuro-evolution framework written in C#. Included is a front-end GUI, a number of example problem domains and some additional applications for viewing and simulating evolved networks.
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...We present a new algorithm for this problem based on the metaheuristic called Greedy Randomized Adaptive Search Procedure (GRASP) that has been routinely used to find solutions for combinatorial optimization problems. In essence, we implemented an iterative process in which each iteration receives a feasible solution whose neighborhood is investigated.
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Evolutionary Nursery
A simple genetic algorithm for numerical optimization
Evolutionary nursery is a result for my passion for developing genetic algorithms. I implemented this simple GA in 2008. This is especially intended for numerical optimization problems. -
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ESTReMo
An evolutionary simulator of transcription regulatory networks
ESTReMo is a genetic algorithm-based simulator to explore the evolution of transcription factors and their binding motifs on realistic genomic backgrounds. -
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evotopo
Evolving Realistic Topologies for Wireless Network Simulation
There is a need for realistic node topologies for simulating and testing wireless network protocols. The softare evoTopo is able to generate multiple different topologies based on a given reference example, typically from a real-world setup. Based on this an evolutionary algorithm evolves new topology files with equal parameters regarding node density, distribution homogeneity and network properties such as node degree distrbution. evoTopo can be used with any wireless network simulator that will accept a node position file of some form. The reference file must be given in .csv format listing the x- and y-coordinates of the nodes. evoTopo is written in Java and will run on Java 6 or higher. ... -
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Evochumps
Evolving recursive artificial neural networks in a simulation.
Evochups allows you to create a simple simulation environment where individuals called "chumps" display behavior according to their simulated brains. Each Chump has a recursive neural network which takes input from thier environment and controls the chump's behavior. It has a built-in evolutionary algorith to let the brains evolve conditoned to selective pressure. The program's interface allows you to manipulate all kinds of parameters of both the simulation, the genetic algorithm, and each particular RNN in real time.
