Genotype, phenotype, selection pressure, recombination, crossover, inversion and mutation are terms common to the jargon of genetic scientists. A "genetic algorithm" is a heuristic search algorithm which attempts to incorporate the ideas of evolutionary biology, as represented by the terms listed above, in a non-biological computational setting. In other words, a genetic algorithm is an algorithm, such as a computer program, which searches for, and generates, solutions to scientific problems by means of techniques that mirror population evolution. Since their introduction in the 1960's, genetic algorithms, have become widely used in economics, business, computer science, and many other fields. In statistical inference, a common problem is to search for the point at which a given function is maximized or minimized. For instance, in the method of maximum likelihood, one must locate the point at which a "likelihood" function is maximized. In this talk I will introduce genetic algorithms. Examples will be given of the maximizing or mimimizing of complicated functions over intricate domains, problems which are practically infeasible to solve using classical methods based on calculus.