Search Results for "tsp genetic algorithm" - Page 3
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Showing 236 open source projects for "tsp genetic algorithm"
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Genetic Algorithms Engine - Blackjack
A genetic algortihm engine that evolves blackjack basic strategy.
This project is a genetic algorithm engine able to be reused for other projects with minimal additional programming. The genetic algorithm engine currently plays many blackjack hands for the fitness function and produces a result similar to blackjack basic strategy. To see it in action, download the zip file and run either: GABlackjack_Demo.exe (quick) or GABlackjack_Long.exe (slow, but it achieves better results). -
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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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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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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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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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FamSeq
Variant calling on the basis of pedigree information
...FamSeq accommodates de novo mutations and can perform variant calling at chromosome X. To accommodate variations in data complexity, FamSeq consists of three distinct implementations of the Mendelian genetic model: the Bayesian network algorithm, Elston-Stewart algorithm and Markov chain Monte Carlo algorithm. To make the software efficient and applicable to large families, we parallelized the Bayesian network algorithm that copes with pedigrees with inbreeding loops without losing calculation precision on an NVIDIA® graphics processing unit. -
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SimpleFB_GA
a simple genetic algorithm written in FreeBasic
a FreeBasic-written Genetic Algorithm code is here deployed -
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HfT GA RCPSP
Genetic Algorithm for the RCPSP
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NEAT Visualizer SFML
A NEAT Implementation and Visualization System
Evolves neural networks using the Neuro-Evolution of Augmenting Topologies (NEAT) technique. A separate visualization system uses another genetic algorithm to evolve images of the otherwise dimensionless networks so their structure can be observed. -
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VariantMaster
Extract causative variants for monogenic and sporadic genetic diseases
...To improve the identification of the variants from HTS, we developed VariantMaster, an original program that accurately and efficiently extracts causative variants in familial and sporadic genetic diseases. The algorithm takes into account predicted variants (SNPs and indels) in affected individuals or tumor samples and utilizes the row (BAM) data to robustly estimate the conditional probability of segregation in a family, as well as the probability of it being de novo or somatic. In familial cases, various modes of inheritance are considered: X-linked, autosomal dominant, and recessive (homozygosity or compound heterozygosity). ... -
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niGA
Heterogenous Multiprocessor Scheduling Using Genetic Algorithms
Implementation of task scheduling using Genetics algorithm for heterogeneous parallel programming -
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TSPLib.Net
TSPLib wrapper for .Net
...This project provides .Net library wrapping up library files loading code, implements distance functions and all the TSPLib features. If you'd like to test your graph tour searching algorithm on a TSP lib will save you hours of reading in a data and adjusting your code to ensure that result are comparable. Delivering as a dll with .Net wrapper and a zip archive with all the original data files from TSPLIB95. This is still BETA, if something is not working or you need some extra features then just let me know. ... -
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Bin Packing with Genectic Algorithm
Bin Packing problem solved using Genectic Algorithm
This project contains a solution for a Bin Packing problem solved using Genectic Algorithms. The code in the project was created as a solution for a problem in a combinatorial optimization class at the Univeridade Federal do Rio Grande do Sul (UFRGS - Brasil) in 2007. -
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OBSOLETE s-ga
genetic algorithm
Obsolete. Moved to the new Project 'sgena' -
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Evochumps
Evolving recursive artificial neural networks in a simulation.
...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. -
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PyGAO
Genetic Algorithm Optimization for Python
A simple interface for performing genetic algorithm optimization for numerical problems. I am starting with a stripped-down version, where a solution can be described using a single vector of float numbers. Eventually, I will expand to more generic data structures and add multiple-species search options. For the time being, I have no plans of developing a GUI. For now, this is strictly a computational module. -
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ABM-Calibration-SensitivityAnalysis
Codes and Data for Calibration and Sensitivity Analysis of ABM
...<http://jasss.soc.surrey.ac.uk/xx/x/x.html> Methods/Techniques used are: a. Parameter fitting: 1. Full Factorial Design 2. Simple Random Sampling 3. Latin Hypercube Sampling 4. Quasi-Newton Method 5. Simulated Annealing 6. Genetic Algorithm 7. Approximate Bayesian Computation b. Sensitivity Analysis: 1. Local SA 2. Morris Screening 3. DoE 4. Partial (Rank) Correlation Coefficient 5. Standardised (Rank) Regression Coefficient 6. Sobol' 7. eFAST 8. FANOVA Decomposition Have also a look on our other projects: http://www.uni-goettingen.de/de/315075.html -
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ALCHEMY is a genotype calling algorithm for Affymetrix and Illumina products which is not based on clustering methods. Features include explicit handling of reduced heterozygosity due to inbreeding and accurate results with small sample sizes
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Model Based Test Generator
Model Based Test Generator from UML
The purpose of this project is creating test cases from uml statemachine and Class diagram with OCL constraint. We use forward search strategy to creating abstract test cases and using genetic algorithm for creating test data. -
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NNBrain
A free, open source collection of neural network based AI agents.
NNBrain is a free and open source collection of artificial intelligence agents. These agents have applications in video games, research, business, and more. The included agents function in both discrete and continuous action and state spaces. -
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Tetris AI
A try to demonstrate an AI module based on doubled genetic algorithm.
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OpenAIL
Open Artificial Intelligence Library
[Open Artificial Intelligence Library]. This library main goal is to provide a tool box to those who want to use algorithm such as neural network or genetics Algorithm and all algorithms that are commonly within the Artificial Intelligence field. -
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AEvolution
Artificial life game
Artificial life game using a genetic algorithm in a simple 2D world. -
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Reactor Breeder
A Genetic Algorithm for Reactors in StarMade
This software uses a genetic algorithm to "evolve" reactor designs for Schema's space-simulation game, Star-Made (http://star-made.org/). One of the more unique aspects of the game is that ship power management is not as simple as filling a cube with power generation blocks. This mechanism leads to difficulties in getting maximal power generation out of such reactors. -
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GAET
Genenitc Algorithm : Educational Tool
The project consists of several parts : A simple framework dedicated to quick implementation of problem resolution algorithms using genetic algorithms ; Some problem resolution algorithms using genetic algorithms ; A graphical interface dedicated to the testing and learning of genetical algorithms.
