Search Results for "genetic algorithm in matlab"
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Showing 8 open source projects for "genetic algorithm in matlab"
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The Operator Splitting QP Solver
The Operator Splitting QP Solver
OSQP uses a specialized ADMM-based first-order method with custom sparse linear algebra routines that exploit structure in problem data. The algorithm is absolutely division-free after the setup and it requires no assumptions on problem data (the problem only needs to be convex). It just works. OSQP has an easy interface to generate customized embeddable C code with no memory manager required. OSQP supports many interfaces including C/C++, Fortran, Matlab, Python, R, Julia, Rust. -
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LWPR
Locally Weighted Projection Regression (LWPR)
Locally Weighted Projection Regression (LWPR) is a fully incremental, online algorithm for non-linear function approximation in high dimensional spaces, capable of handling redundant and irrelevant input dimensions. At its core, it uses locally linear models, spanned by a small number of univariate regressions in selected directions in input space. A locally weighted variant of Partial Least Squares (PLS) is employed for doing the dimensionality reduction. Please cite: [1] Sethu... -
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cCNN
A fast implementation of LeCun's convolutional neural network
Code of this library is partialy based on myCNN MATLAB class written by Nikolay Chemurin. -
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Developed as a final year project at Islamic University of Technology (IUT) during 2010 - 2011 academic year. This code is a simple implementation of real coded genetic algorithm for numerical optimization. Code is written in C/C++.
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This project aims to create an application to solve the job shop schedule problem using genetic algorithm on the IBM Cell BE processor. This is useful especially using the power of Cell for the large scale job shop schedule problem.
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The goal of the Rats and Mazes project is to build a simple genetic algorithm to find general solutions to random non-cyclic single path mazes. I believe that the solutions will tend towards either the right/left-hand rule.
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This is a very simple implementation of the genetic algorithm framework presented by John Holland, based on Goldberg book Genetic Algorithms in Search, Optimization and Machine Learning.
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