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Global Optimization Toolbox

Genetic Algorithm Solver

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. It enables you to solve unconstrained, bound-constrained, and general optimization problems, and it does not require the functions to be differentiable or continuous.

The following table shows the standard genetic algorithm options provided by Global Optimization Toolbox.

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

Global Optimization Toolbox also lets you specify:

  • Population size
  • Number of elite children
  • Crossover fraction
  • Migration among subpopulations (using ring topology)
  • Bounds, linear, and nonlinear constraints for an optimization problem

You can customize these algorithm options by providing user-defined functions and represent the problem in a variety of data formats, for example by defining variables that are integers, mixed integers, categorical, or complex.

You can base the stopping criteria for the algorithm on time, stalling, fitness limit, or number of generations. And you can vectorize your fitness function to improve execution speed or execute the objective and constraint functions in parallel (using Parallel Computing Toolbox).

Optimal Component Selection Using the Mixed-Integer Genetic Algorithm 5:25
Use the mixed-integer genetic algorithm to solve an engineering design problem.

Next: Multiobjective Genetic Algorithm Solver

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