ABSTRACT
In this chapter, we will introduce the use of genetic algorithms for solving multiobjective programming (MOP) problems and the concrete contents of genetic algorithms (GAs).
In traditional optimization methods, gradients and derivatives are usually used to guide the search for an optimal solution. However, when the objective function is not differentiable or the dimensionality of the search space is quite large, these techniques usually perform poorly. GAs are now considered alternative methods to solve such optimization problems. GAs were pioneered by Holland (1975) and the concept is to mimic the natural evolution of a population by allowing solutions to reproduce, creating new solutions that then compete for survival in the next iteration. The fitness improves over generations and the best solution is finally achieved.
