Real world problems often present multiple, frequently conflicting, objectives.

The research for optimal solutions of multi-objective problems

can be achieved through means of genetic algorithms, which are inspired

by the natural process of evolution: an initial population of solutions is

randomly generated, then pairs of solutions are selected and combined in

order to create new solutions slightly different the initial solutions.

The fittest solutions are kept in the population and are used to generate

new solutions-Real world problems often present multiple, frequently conflicting, objectives.

The research for optimal solutions of multi-objective problems

can be achieved through means of genetic algorithms, which are inspired

by the natural process of evolution: an initial population of solutions is

randomly generated, then pairs of solutions are selected and combined in

order to create new solutions slightly different　the initial solutions.

The fittest solutions are kept in the population and are used to generate

new solutions