Genetic algorithms in economics: Difference between revisions

Revision as of 23:28, 28 February 2007

Revision as of 16:19, 20 March 2007

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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 Geneticgenetic Algorithmalgorithm 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]]

== Design ==