Note on Multi-objective optimization (why evolutionary)

The evolutionary algorithm (EA) mimics nature's evolutionary principles to drive its search towards an optimal solution. One of the most striking differences to classical search and optimization algorithms is that EAs use a population of solutions in each iteration, instead of a single solution. Since a population of solutions are processed in each iteration, the outcome of an EA is also a population of solutions. If an optimization problem has a single optimum, all EA population members can be expected to converge to that optimum solution. However, if an optimization problem has multiple optimal solutions, an EA can be used to capture multiple optimal solution in its final population.