ABSTRACT
The EA mimics nature’s evolutionary principles to drive its search towards an optimal solution. The EAs include genetic algorithm (GA), differential evolution (DE), evolutionary strategies (ES), evolutionary programming (EP), genetic programming (GP), etc. The GA is a search and optimization procedure that is motivated by the principles of natural genetics and natural selection. It gives near global population of optimal solutions. It works with a population of solutions and gives multiple optimal solutions in one simulation run. It has two distinct operations such as selection and search. It is flexible enough to be used in a wide variety of problem domains. It has three main operators such as reproduction, crossover and mutation which are playing important role in creating a new population of solutions. The reproduction or selection operator is used to make duplicates of good solutions and eliminate bad solutions in a population, while keeping the population size constant. The crossover and mutation operators are used to create new solutions [1, 2, 4, 5].
