Genetic Algorithm Solver

Product Description

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.

Step	Genetic Algorithm Option
Creation	Uniform, feasible
Fitness scaling	Rank-based, proportional, top (truncation), shift linear
Selection	Roulette, stochastic uniform selection (SUS), tournament, uniform, remainder
Crossover	Arithmetic, heuristic, intermediate, scattered, single-point, two-point
Mutation	Adaptive feasible, Gaussian, uniform
Plotting	Best 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).

Output that shows solutions reached when using only the genetic algorithm (right, bar chart) and when using the genetic algorithm with a gradient-based solver from Optimization Toolbox (final point in Optimization Tool, left). Combining algorithms can produce more accurate results while reducing the number of function evaluations required by the genetic algorithm alone.