Genetic algorithms

Genetic algorithms are exploratory procedures that are often able to locate near optimal solutions to complex problems. To do this, a genetic algorithm maintains a set of trial solutions, and forces them to evolve towards an acceptable solution. First, a representation for possible solutions must be developed. Then, starting with an initial random population and employing survival-of-the-fittest and exploiting old knowledge in the gene pool, each generation´s ability to solve the problem should improve. This is achieved through a four-step process involving evaluation, reproduction, recombination, and mutation. As an application the author developed a genetic algorithm to train a product neural network for predicting the optimum transistor width in a CMOS switch, given the operating conditions and desired conductance.<>