Genetic algorithm


Dr. Nur Arina Bazilah Aziz

Genetic Algorithms (GAs) were developed by Prof. John Holland and his students at the University of Michigan during the 1960s and 1970s. Essentially, they are a method of “breeding” computer programs and solutions to optimization or search problems by means of simulated evolution. Processes loosely based on natural selection, crossover, and mutation are repeatedly applied to a population of binary strings that represent potential solutions. Over time, the number of above-average individuals increases, and highly-fit building blocks are combined from several fit individuals to find good solutions to the problem at hand.

The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that are 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 to 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 well suited for standard optimization algorithms, including problems in which the objective function is discontinuous, nondifferentiable, stochastic, or highly nonlinear. The genetic algorithm can address problems of mixed-integer programming, where some components are restricted to be integer-valued.