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genetic algorithm solver for mixed-integer or continuous-variable optimization, constrained or unconstrained

genetic algorithm solves smooth or nonsmooth optimization problems with any types of constraints, including integer constraints. it is a stochastic, population-based algorithm that searches randomly by mutation and crossover among population members.

functions

problem-based solution

	create values for optimization problem
	solve optimization problem or equation problem

solver

find minimum of function using genetic algorithm

options

	create optimization options
	reset options

live editor tasks

optimize

optimize or solve equations in the live editor

topics

problem-based genetic algorithm

basic example minimizing a function with multiple minima in the problem-based approach.
solve a nonlinear problem with nonlinear constraints and bounds using ga in the problem-based approach.
example showing how to use problem-based mixed-integer programming in ga, including how to choose from a finite list of values.
solve a nonlinear feasibility problem using the problem-based optimize live editor task and several solvers.
to set options in some contexts, map problem-based variables to solver-based using varindex.

genetic algorithm optimization basics

minimize rastrigin's function
presents an example of solving an optimization problem using the genetic algorithm.
shows how to write a fitness function including extra parameters or vectorization.
shows how to include constraints in your problem.
options and outputs
shows how to choose input options and output arguments.
effects of genetic algorithm options
example showing the effect of several options.
this example shows how setting the initial range can lead to a better solution.

common tuning options

examine the effects of setting the maxgenerations and maxstallgenerations options.
shows the importance of population diversity, and how to set it.
describes fitness scaling, and how it affects the progress of ga.
shows the effect of the mutation and crossover parameters in ga.
hybrid scheme in the genetic algorithm
shows the use of a hybrid function for improving a solution.
describes cases where hybrid functions are likely to provide greater accuracy or speed.

mixed integer optimization

solve mixed integer programming problems, where some variables must be integer-valued.
example showing how to use mixed-integer programming in ga, including how to choose from a finite list of values.

specialized tasks

shows how to continue optimizing ga from the final population.
shows how to reproduce results by resetting the random seed.
provides an example of running ga using a set of parameters to search for the most effective setting.
how to gain speed using vectorized function evaluations.
create custom plot function
shows how to create and use a custom plot function in ga.
custom output function for genetic algorithm
this example shows the use of a custom output function in ga.
custom data type optimization using the genetic algorithm
solve a traveling salesman problem using a custom data type.
optimize odes in parallel
save time by calling an expensive subroutine just once and computing an ode solution in parallel using patternsearch or ga.

genetic algorithm background

introduces the genetic algorithm.
explains some basic terminology for the genetic algorithm.
presents an overview of how the genetic algorithm works.
explains the augmented lagrangian genetic algorithm (alga) and penalty algorithm.
genetic algorithm options
explore the options for the genetic algorithm.