Introduction: Artificial Intelligence
Intelligence can be defined as the capability of a system to adapt its behavior to an ever-changing environment. The form or appearance of a system is irrelevant to it is intelligence (Negnevitsky, 2005). However, we need to form our everyday experience of knowledge: we know that evidences of intelligent behavior are easily observed in humans. But we are products of evolution, and thus by modeling the process of evolution, we might expect to create intelligent behavior. Evolutionary computation simulates evolution on a computer. The result of such a simulation is a serie of optimization algorithms, usually based on a simple set of rules. Optimization iteratively improves the quality of solutions until an optimal, or at least feasible, solution is found.
The evolutionary approach to learning machine is based on computational models of natural selection and genetics. We call them evolutionary computation, an umbrella term that combines genetic algorithms, evolution strategies and genetic programming. All these techniques simulate evolution by using the processes of selection, crossover and mutation. Since the process of evolution is emulated on a computer, evolution will be seen as a process leading to the maintenance or an increase of a population’s ability to survive and reproduce in a specific environment; this ability is called evolutionary fitness.
All simulate natural evolutions, generally by creating a population of individuals, evaluating their fitness, generating a new population of individuals, evaluating their fitness, generating a new population through genetic operations, and repeating this process a number of times (Negnevitsky, 2005).
