Genetic Algorithm and Direct Search Toolbox

What Is the Genetic Algorithm?

The genetic algorithm is a method for solving optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm selects individuals at random from the current population to be parents and uses them produce the children for the next generation. Over successive generations, the population "evolves" toward an optimal solution. You can apply the genetic algorithm to solve a variety of optimization problems that are not wellsuited for standard optimization algorithms, including problems in which the objective function is discontinuous, nondifferentiable, stochastic, or highly nonlinear.

The genetic algorithm uses three main types of rules at each step to create the next generation from the current population:

• Selection rules select the individuals, called parents, that contribute to the population at the next generation.
• Crossover rules combine two parents to form children for the next generation.
• Mutation rules apply random changes to individual parents to form children.

The genetic algorithm differs from a standard optimization algorithm in two main ways, as summarized in the following table.

Standard Algorithm	Genetic Algorithm
Generates a single point at each iteration. The sequence of points approaches an optimal solution.	Generates a population of points at each iteration. The population approaches an optimal solution.
Selects the next point in the sequence by a deterministic computation.	Selects the next population by computations that involve random choices.

Getting Started with the Genetic Algorithm

Using The Genetic Algorithm

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