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Multiple Variable Traveling Salesmen Problem Genetic Algorithm

Matlab

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It finds a (near) optimal solution to a variation of the M-TSP (that has a variable number of salesmen) by setting up a GA to search for the shortest route (least distance needed for the salesmen to travel to each city exactly once and return to their starting locations). Each salesman ...

Fixed Start Open Multiple Traveling Salesmen Problem Genetic Algorithm

Matlab

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Finds a (near) optimal solution to a variation of the "open" M-TSP by setting up a GA to search for the shortest route (least distance needed for each salesman to travel from the start location to unique individual cities without returning to the starting location).Each salesman starts at the first point, ...

Fixed Endpoints Open Multiple Traveling Salesmen Problem Genetic Algorithm

Matlab

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The script finds a (near) optimal solution to a variation of the "open" M-TSP by setting up a GA to search for the shortest route (least distance needed for each salesman to travel from the start location to unique individual cities and finally to the end location).Each salesman starts at the ...

Fixed Start End Point Multiple Traveling Salesmen Problem Genetic Algorithm

Matlab

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Finds a (near) optimal solution to a variation of the M-TSP by setting up a GA to search for the shortest route (least distance needed for each salesman to travel from the start location to individual cities and back to the original starting place).Each salesman starts at the first point, and ...

Fixed Start Open Traveling Salesman Problem Genetic Algorithm

Matlab

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The algorithm computes the optimal route from a fixed predetermined starting point to all other cities without returning to the starting point. Requirements: · MATLAB 7.6 or higher ...

Open Traveling Salesman Problem Genetic Algorithm

Matlab

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The difference to the classic TSP is that the salesman doesn't travel in loop across the circuit. Requirements: · MATLAB 7.6 or higher ...

GODLIKE

Matlab

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... optimization problems using relatively basic implementations of a genetic algorithm, differential evolution, particle swarm optimization and adaptive simulated annealing algorithms.The script is primarily intended to increase robustness, not efficiency as it usually requires more function evaluations than any of the algorithms separately. Requirements: · MATLAB 7.7 or higher ...

Fityk

... interface), variouse optimization methods (standard Marquardt least-square algorithm, Genetic Algorithms, Nelder-Mead simplex), equality constraints, modelling error of x coordinate of points (eg. zero-shift of instrument), handling series of datasets, automation of common tasks with scripts, and more. Requirements: · GTK+ · wxGTK ...

Charlemagne

Python

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Charlemagne is a genetic programming application which aims to be highly configurable and applicable to a broad range of problems.It is written in Python and Lisp and to some degree is extensible in both languages. It features built-in input-output mapping support, but also provides the ability to define complex fitness ...

Open BEAGLE

... any kind of EC, with support for tree-based genetic programming; bit string, integer-valued vector, and real-valued vector genetic algorithms; and evolution strategy.The Open BEAGLE architecture follows ... Constrained GP tree operators with support for strongly-typed genetic programming - Three illustrative examples (Symbolic Regression, ...

SciPy

Python

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... linear algebra, Fourier transforms, signal processing, image, processing, genetic algorithms, ODE solvers, special functions and more. ...

athenaCL

Python

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... automata based, markov based, wave-form, fractional noise (1/f), genetic, Xenakis sieve, linear and exponential break-point segments, masks, and various other algorithmic models. ParameterObjects can be embedded in other ParameterObjects to provide powerful dynamic and masked value generation. Textures can be combined and edited, and tuned with algorithmic Temperament objects.Texture Clones ...

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