Search Results for "knapsack genetic algorithm" - Page 2
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Showing 55 open source projects for "knapsack genetic algorithm"
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This library is a lightweight implementation of genetic algorithm, contains the most popular types of chromosomes and the basic algorithms for selection, elitism, crossing and mutation.
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This easy to run and change program for developers, it contains several evolutionary based strategies allowing you try among some function samples
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PGAF provides a framework tuned, user-specific genetic algorithms by handling I/O, UI, and parallelism. It is designed for optimizing functions that take a "very long time" to evaluate.
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Shape is a molecular conformation prediction program. It uses a genetic algorithm to efficiently search the conformational space of a biomolecule and then clusters the results. It is very simple to use.
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Java API for implementing any kind of Genetic Algorithm and Genetic Programming applications quickly and easily. Contains a wide range of ready-to-use GA and GP algorithms and operators to be plugged-in or extended. Includes Tutorials and Examples.
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A 2D rigid physics simulator and set of genetic algorithm tools. Evolve cute little walking creatures.
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Primarily applied in experimental psychology, where it is used to create experiment schedules. It even can be applied to other planning-activities that have to met boundary constraints. Rando uses a genetic-algorithm to construct a fulfilled schedule.
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The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.
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Searches for adecuate design for feedforward backpropagation neural network, employing genetic algorithm as refining engine. The result topolgy may not be orthodox.
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A flexible programming library for evolutionary computation. Steady-state, generational and island model genetic algorithms are supported, using Darwinian, Lamarckian or Baldwinian evolution. Includes support for multiprocessor and distributed systems.
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gaDesignLib
Genetic algorithm for mechatronic product design
This C++/STL library provides you with the basic genetic algorithms needed for engineering design, such as simple GA and variable population. -
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DrPangloss is a python implementation of a three operator genetic algorithm, complete with a java swing GUI for running the GA and visualising performance, generation by generation
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NullAllEst is the implementation of a maximum likelihood algorithm to estimate the frequency of a null allele in microsatellite genetic data. A Markov Chain Monte Carlo simulation is used to solve the likelihood function.
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Molevolve is a Java library for running a Genetic Algorithm to model the 3-dimensional structures of peptide chains from amino-acid sequences. Client code can specify its own peptide chain model, fitness functions and GA operations. Requires JDK 1.5.
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IslandEv distributes a Genetic Algorithm (like <a href="/projects/jaga">JaGa</a>) across a network (see <a href="/projects/distrit">DistrIT</a>) using an island based coevolutionary model in which neighbouring islands swap migrating individuals every
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C++ genetic algorithms library (solving of the NP opnimisation problems). More GA strategies, more useability, more algorithm speed.
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Galileo is a library for developing custom distributed genetic algorithms developed in Python. It provides a robust set of objects that can be used directly or as the basis of derived objects. Its modularity makes it easy to extend the functionality. The
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MAGMA: Multiobjective Analyzer for Genetic Marker Acquisition A genetic algorithm for generating SNP tiling paths from a large SNP database based on the competing objectives of cost (number of SNPs) and coverage (haplotype blocks): Hubley R., Zitzler
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Java port and extension of MLC++ 2.0 by Kohavi et al. Currently contains ID3, C4.5, Naive (aka Simple) Bayes, and FSS and CHC (genetic algorithm) wrappers for feature selection. WEKA 3 interfaces are in development.
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A Genetic Algorithm Training System in Python. Gatspy provides the framework, the user provides the error (fitness) function. Gatspy will evolve a solution that attempts to minimize the error.
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aVolve is an evolutionary/genetic algorithm designed to evolve single-cell organisms in a micro ecosystem. It currently uses the JGAP Genetic algorithm, but does include a primitive genetic algorithm written in Python.
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R based genetic algorithm for optimization, variable selection and other machine learning and statistical analysis approaches.