ABSTRACT
This chapter will deal with some of the most widely used and successful evolutionary algorithms, which are inspired from the evolution of natural evolution and organisms. They are mostly used in discrete problem optimization but can also be used in multiobjective continuous problems as well. The main advantages of using these algorithms are its combination generation, learning capability, and using the learnt experience for a generation of better solutions. There are various evolutionary algorithm like genetic algorithms (GA), genetic programming approaches, and evolutionary strategies [1-4]. There are various modied versions of these algorithms, which have been introduced when certain deciencies were found in the original ones. In addition, there were times when it was found that the modied versions were far better in performing than the traditional algorithm itself. We will gradually get into the details of the algorithm and will illustrate with examples so that the readers can visualize the operations and can use them in their problems for optimization and solution generation. However, the explanations provided are just one-sided visualizations, and there can be several other observations and techniques of using the algorithm for the various problems and applications [5,6].
