Genetic Algorithms and Evolutionary Computing

Abstract

Evolutionary algorithms are inspired from the Nature’s ability to evolve. Evolutionary algorithms are a component of evolutionary computing in the field of Artificial Intelligence. They are inspired from the biological evolution of random population by employing various modifying operations on the basic pattern of the candidates of the population. In nature, evolution through such modification happens in such a way that the next population will consist of members that are comparatively more fit to survive in the given situation. In a case where the modification results in poorer candidates, they cannot survive, and hence they will be automatically deselected from the population. Only the fit candidates will survive in the population. That is why such an approach is called the survival of the fittest approach. A broad outline of the approach is given as follows.

• Generate initial population by selecting random individuals.

• Apply one or more evaluation functions for the candidates.

• Select fit (good quality) candidates and push them in the next generation directly, if they are up to the mark.

• Select some strong candidates and modify them to generate even stronger candidates and push them to the next generation.

• Repeat the procedure until the population evolves towards a solution.