Genetic Algorithms in 3 Minutes
By ai-depot | June 30, 2002
A clear introduction to genetic algorithms, concise and to the point. Covers biological evolution to algorithmic simulation, applications and motivations as well as future research. Dedicated to those of you that only have so much time on their hands… now there’s no excuse!
Written by Alex J. Champandard.
From Biology to Computing
Welcome to Genetic Algorithms in 3 Minutes, which surprisingly aims to teach you all about Genetic Algorithms in about 3 minutes. It does exactly what it says on the tin!
Evolution
According to Darwin’s theory of evolution, only the fittest individuals of a species will be capable of surviving, thereby earning a chance to reproduce. This contribution to the gene pool will potentially propagate through the population via breeding, allowing beneficial changes to become a standard.
This idea was borrowed from biology by John Holland, and applied to Artificial Intelligence. However, instead of dealing with mammels in their environment, this kind of artificial evolution deals with raw data. This allows computers to find good solutions to problems.
Problem Solving & Optimisation
To solve a problem, a solution needs to be encoded as a genotype - or sequence of genes. For humans, this can be understood as a chromosome. For computers, this is known as the representation, and getting it right is crucial for the evolution phase.
Before the evolution can start, you need a gene-pool, which contains many representations of solutions. Then things start to get interesting…
Tags: none
Category: tutorial |
