Genetic Algorithms

Genetic algorithms (GA) are exploratory search and optimisation methods that are based on a Darwinian-type survival of the fittest strategy with reproduction, where stronger individuals in the population have a higher chance of creating offspring. Each individual in the population represents a potential solution to the problem. The individuals are represented in the GA by means of string similar to the way genetic information is coded in organisms as chromosomes (Holland, 1975). Unlike other optimisation techniques, GA does not require mathematical descriptions of the optimisation problem, but instead relies on a cost function, in order to assess the fitness of a particular solution to the problem in question (Goldberg, 1989). The GA then iteratively creates new populations from the old by ranking the strings and interbreeding the fittest to create new strings, which are (hopefully) closer to the optimum solution to the problem in question. So in each generation, the GA creates a set of strings from the bits and pieces of the previous strings, occasionally adding random new data to keep the population from stagnating. The end result is a search strategy that is tailored for vast, complex, multimodal search spaces. A genetic adaptive plan then can be defined as a quadruple as { } Ω Φ Π Σ = Λ , , , N (1)