Genetic algorithms in economics: Difference between revisions

Revision as of 19:21, 22 December 2007

Revision as of 03:24, 16 January 2008

Line 1:

The [[genetic algorithm]] in [[economics]] is an [[algorithm]] used to [[Model (abstract)|model]] the [[learning behaviour]] of [[economic agents]]. The term "genetic algorithm" is often abbreviated as GA. The genetic algorithm is a particular class of [[evolutionary algorithm]] inspired by [[evolutionary biology]]. A genetic algorithm is defined as basic if it only contains a method for reproduction and experimentation. It is defined as augmented if it contains a selection operator as well.

Genetic algorithms are [[Implementation|implement]]ed as a [[computer simulation]] in which a [[population]] of [[abstract]] representations (called [[chromosome (genetic algorithm)|chromosomes]] or strings) of [[candidate solutions]] (called individuals, or agents) to an optimization problem evolves toward better [[Solution (business)|solution]]s. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), and modified (mutated or recombined) to form a new population. The new population is then used in the next iteration of the algorithm.

The genetic algorithm has increasingly been applied to economics over the last two decades. It has been used to characterize a variety of models including the [[cobweb model]], the [[overlapping generations model]], [[game theory]] and [[asset pricing]]