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Genetic Algorithm and Direct Search Toolbox™ 2.3

Product Description

Genetic Algorithm Tools

The genetic algorithm solves optimization problems by mimicking the principles of biological evolution, repeatedly modifying a population of individual points using rules modeled on gene combinations in biological reproduction. Due to its random nature, the genetic algorithm improves your chances of finding a global solution.

The Genetic Algorithm and Direct Search Toolbox provides the following standard algorithm options.

StepAlgorithm Option
CreationUniform
Fitness scalingRank-based, proportional, top (truncation), linear scaling, shift
SelectionRoulette, stochastic uniform selecĀ­tion (SUS), tournament, uniform
CrossoverArithmetic, heuristic, interĀ­mediate, scattered, single-point, two-point
MutationAdaptive feasible, Gaussian, uniform
PlottingBest fitness, best individual, distance among individuals, expectation of individuals, range, diversity of population, selection index, stopping conditions


The toolbox also lets you specify:

  • Constraints for your optimization problem
  • Population size
  • Number of elite children
  • Crossover fraction
  • Migration among subpopulations (using ring topology)

You can customize these algorithm options by providing user-defined functions and represent your problem in a variety of data formats, for example by using variables that are either mixed-integer or complex.

You can base the stopping criteria for the algorithm on time, stalling, fitness limit, or number of generations. Finally, you can vectorize your fitness function to improve execution speed.

M-file of an optimization created using the automatic code generation feature in the Genetic Algorithm and Direct Search Toolbox. You can export your optimization from the GUI as commented code that can be called from the command line and used to automate routines and preserve your work. Click on image to see enlarged view.
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Genetic Algorithms & New Optimization Methods in MATLAB

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Using MATLAB to Develop and Deploy Financial Models