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]]. Specifically, it has been used as a model to represent learning, rather than as a means for fitting a model.
== Design ==
The genetic algorithm generally consists of a population of n agents with m strings. These strings are often initially randomly generated but are then updated every g periods. Each string is assigned a fitness value through a defined method which is used as a measure of performance. The strings are updated through a series of operators. The basic genetic algorithm generally consists of three unique operators: the reproduction operator, which attempts to imitate successful agents and the two experimentation operators, crossover and mutation, which are implemented to bring diversity into the system. The augmented genetic algorithm includes an election operator, which adds a selection criteria.
=== Reproduction ===
The first operator, reproduction, works by attempting to imitate. In general, it selects another agent to observe its fitness value. If its fitness value is greater than its own, then it elects to adopt the other agent's string. Otherwise, it preserves it own. These strings are then placed into an offspring pool to undergo the mutation operators, crossover and mutation. Most functions are [[stochastic]] and designed so that a small proportion of less fit solutions are selected. This helps keep the diversity of the population large, preventing premature convergence on poor solutions. Popular and well-studied selection methods include [[fitness proportionate selection|roulette wheel selection]] and [[tournament selection]].
These processes ultimately result in the offspring pool of strings that is different from the initial parent pool. The election operator then works by comparing the fitness of the parent strings to the potential fitness of the offspring pool. If the offspring string has a higher fitness value, it will replace the parent string in the population. Otherwise, the parent string will stay. Generally the average fitness will have increased by this procedure for the population, since only the best strings are selected.
