Search Results for "knapsack genetic algorithm" - Page 2
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Showing 226 open source projects for "knapsack genetic algorithm"
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GenCodeGenerator
C++ class to generate biologically plausible genetic codes
...By default, the class "GeneticCode" generates alternative genetic codes, with the reqirement of block structure, and, optionally, with the assumption of stereochemical or biosynthetic models (to impose the assumption of the adaptive model, simply filter the codes using the error_cost() function). See Appendix in Makukov & shCherbak (2017) for the description of the algorithm. The code requires Qt 5. -
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Swift AI
The Swift machine learning library
Swift AI is a high-performance deep learning library written entirely in Swift. We currently offer support for all Apple platforms, with Linux support coming soon. Swift AI includes a collection of common tools used for artificial intelligence and scientific applications. A flexible, fully-connected neural network with support for deep learning. Optimized specifically for Apple hardware, using advanced parallel processing techniques. We've created some example projects to demonstrate the... -
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GAKNN is a data mining software for gene annotation data. GAKNN is built with k- Nearest Neighbour algorithm optimized by the genetic algorithm. Gene annotation datasets saved under .csv or .arff formats with Gene Ontology or FunCat categorization can use GAKNN to predict gene functions.
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GA-tools
general genetic algorithms optimization fortran 95 routines
High level optimization routines in Fortran 95 for optimization problems using a genetic algorithm with elitism, steady-state-reproduction, dynamic operator scoring by merit, no-duplicates-in-population. Chromosome representation may be integer-array, real-array, permutation-array, character-array. Single objective and multi-objective maximization routines are present. Possible to incorporate own crossover and mutation operators exclusively or in addition to standard operators that are included by default. ... -
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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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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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tsp-problem-ga-aco-comparisson
Genetic Algorithm and Ant Colony to solve the TSP problem
This project compares the classical implementation of Genetic Algorithm and Ant Colony Optimization, to solve a TSP problem. It's possible to define the number of cities to visit , and also interactively create new cities to visit in a 2D spatial panel. A total distance is given for AG and ACO solution at end. -
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Sched-Resched
A C++ schedule generator based on Genetic Algorithm and Hill Climbing
Sched-Sched is a C++ schedule generator for software project staffing and rescheduling based on Genetic Algorithm (GA) and Hill Climbing (HC). It is implemented with GALib (http://lancet.mit.edu/ga/), an open-source toolkit of Genetic Algorithms in various platforms including Unix and Windows. The input and output files of the software are required as XML format. The input file includes tasks' and employees' information according to predefined file format. ... -
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Sched-SPM
A C++ schedule generator based on Genetic Algorithm and Hill Climbing
Sched-SPM is a C++ schedule generator for software project staffing and rescheduling based on Genetic Algorithm (GA) and Hill Climbing (HC). This preliminary tool is mainly for academic purpose. It is implemented with GALib (http://lancet.mit.edu/ga/), an open-source toolkit of Genetic Algorithms in various platforms including Unix and Windows. The input and output files of the software are required as XML format. The input file includes tasks' and employees' information according to predefined file format. ... -
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theoEA_Code
genetic algorithm for optimizing planar optical antennas
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GA-EoC
GeneticAlgorithm-based search for Heterogeneous Ensemble Combinations
...To enhance classification performances, we propose an ensemble of classifiers that combine the classification outputs of base classifiers using the simplest and largely used majority voting approach. Instead of creating the ensemble using all base classifiers, we have implemented a genetic algorithm (GA) to search for the best combination from heterogeneous base classifiers. The classification performances achieved by the proposed method method on the chosen datasets are promising. -
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REDHORSE
Software suite for the analysis of haploid hybrids using NGS
...We therefore designed a software suite called REDHORSE that takes genomic alignments as input, extracts meaningful markers and generates MSAs that are the inputs to existing RD algorithms. In addition, REDHORSE implements a custom RD algorithm that makes use of sequence information and genomic positions to accurately detect crossovers. REDHORSE is portable and platform independent suite that provides efficient analysis of genetic crosses based on NGS data. -
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This software searches the potential energy surface of small to medium size atomic systems for global minima using quantum ab initio techniques. It performs bond rotations and molecule translations and rotations on a Linux cluster with MPI.
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GeneticAlgorithms
Framework for using the Genetic Algorithms optimization heuristic.
GeneticAlgorithms is a simple and lightweight framework to implement an optimization heuristic following the Genetic Algorithms model. A genetic algorithm mimics the natural processes of evolution, selection and "survival of the fittest". This framework is intuitive and good integrated with Java 1.5 SDK and later. -
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BiRW
Bi-random Walks for Phenome-Genome Association Prediction
The availability of ontologies and systematic documentations of phenotypes and their genetic associations has enabled large-scale network-based global analyses of the association between the complete collection of phenotypes (phenome) and genes. BiRW is a package designed for analysis and prediction of the phenome-genome associations. BiRW package contains a program for analysis of circular bigraphs (CBGs) and the bi-random walk (BiRW) algorithm to capture the CBG patterns in the networks for unveiling human and mouse phenome-genome association. ... -
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Java CoreWars Evolver
A Java based, pMars compatible, CoreWars simulator with GA
A Java based, pMars compatible, CoreWars simulator with Genetic Algorithm warrior evolver functionality. Java 8 is needed. See [Wiki:Tutorial] -
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diggit
R package for DIGGIT algorithm
DIGGIT identify genetic variants associated with drivers of specific physiopathologic states. I computes the statistical association between the presence of genetic variants (CNV or SNP) and the activity of proteins driving physiologic or pathologic phenotype. It requires large gene expression and genetic variants datasets and depends on context-specific regulatory networks including transcriptional interactomes (usually reverse engineered by the ARACNe algorithm) and post-translational interactomes (usually generated by the MINDy algorithm). ... -
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Glycosylation Microviewer
An application create, simulate and adjust N-linked glycosylation.
...The model used in the application is based on two significant papers: 1.1997 Pablo Umana, James E.Bailey A Mathematical Model of N-linked Glycoform Biosynthesis. 2.2005 Frederick J.Krambeck, Michael J.Betenbaugh A Mathematical Model of N-linked Glycosylation. The application allow to simulate the network in dynamic form with various ODE solvers(Euler, Runge-Kutta etc). Genetic Algorithm is used to adjust the kinetic parameters of Michaelis-Menten equations in glycosylation reaction network. A dynamic plot is supplied to show the value of mole amout and mole fraction of various glycoform. Thanks to Alexey Balakin and Alessandro Presta. Alexey Balakin supply mathgl(https://sourceforge.net/projects/mathgl) that make the implementation of dynamic plot in this application easily. ... -
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Cyberphantom Alfa Colony
AI new tipe
This is an experimental project whose main objective is the creation of artificial intelligence (AI) new tipe. At the core of this idea is the use of special genetic algorithm that emulates the single-celled organism. The colony of such cybernetic organisms can give rise to a "reasonable" network that have a huge intellect and computing power. -
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gametes
Generate complex SNP models and heterogeneous datasets
Genetic Architecture Model Emulator for Testing and Evaluating Software (GAMETES) is an algorithm for the generation of complex single nucleotide polymorphism (SNP) models for simulated association studies. GAMETES is designed to generate epistatic models which we refer to as pure and strict, that constitute the worst-case in terms of detecting disease associations, since such associations may only be observed if all n-loci are included in the disease model. -
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quickGA-m
A quick (messy?) genetic algorithm to solve TSP
A quick genetic algorithm to solve TSP scenarios. Written in Matlab. Circa June 2012. -
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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.
