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Genetic algorithms
MathWorks - Genetic Algorithm and Direct Search Toolbox
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The Genetic Algorithm and Direct Search Toolbox extends the optimization capabilities in MATLAB and the Optimization Toolbox with tools for using the genetic and direct search algorithms.
Submitted Jan 23, 2004
Updated Mar 08, 2007
by mohammad
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| Free Online MathWorks Webinar: Genetic Algorithms & New Optimization Methods in MATLAB
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The Genetic Algorithm and Direct Search Toolbox provides both graphical user interfaces and command line tools for applying these nontraditional optimization techniques, which augment the traditional methods found in the Optimization Toolbox. This webinar is appropriate for those unfamiliar with MATLAB, as well as current MATLAB users interested in optimization.
Submitted Oct 18, 2004
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| GEATbx
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Welcome to the main page of the Genetic and Evolutionary Algorithm Toolbox (GEATbx) for use with MATLAB. The GEATbx is the most comprehensive implementation of Evolutionary Algorithms in
MATLAB. A broad range of operators is fully integrated into one environment constituting a powerful optimization tool applicable to a wide range of problems.
Submitted Jul 10, 1999
Updated Mar 10, 2004
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| Solver Platform SDK - Genetic Algorithm in MATLAB
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The Solver Platform SDK gives MATLAB users access to a powerful hybrid Evolutionary/Classical Solver that uses genetic algorithm methods such as mutation, crossover, selection and constraint repair, but also uses deterministic, gradient-free direct search methods, classical gradient-based quasi-Newton methods, and even the Simplex method for linear subsets of the constraints. This Solver handles optimization problems with hundreds of constraints, which are typically beyond the capabilities of genetic and evolutionary algorithms alone.
Submitted Mar 26, 2006
by Daniel Fylstra
Updated Mar 27, 2006
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| Lazy Learning Toolbox
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Lazy learning (Aha, 1997) is a memory-based technique that postpones all the computation until an explicit request for a prediction is received. The request is fulfilled by interpolating locally the examples considered relevant according to a distance measure. Each prediction requires therefore a local modeling procedure that can be seen as composed of a structural and of a parametric identification.
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