The underlying ideas for genetic algorithms were inspired by Charles Darwin's notion of biological evolution through natural selection. The basic idea is to solve an optimization problem by evolving the best solution from an initial set of completely random guesses. The theoretical model provides the framework within which evolution takes place, and the individual parameters controlling it serve as the genetic building blocks.
Observations provide the selection pressure.
Parallel Genetic Algorithm Chart Initially, the parameter space is filled uniformly with trials consisting of randomly chosen values for each parameter, within a range based on the physics the parameter is supposed to describe. The model is evaluated for each trial, and the result is compared to the observed data and assigned a fitness inversely proportional to the variance. A new generation of trials is created by selecting from this population at random, weighted by fitness. In order to apply genetic operations to the new generation of trials, their characteristics must be encoded in some manner. The most straightforward way of encoding is to convert the numerical values of the parameters into a long string of numbers.