GENETIC ALGORITHM & OPERATORS

The advent of electronic computer is a revolution in the field of science and technology. The applications of the electronic machine are not only limited to calculation rather it also motivated the scientist to implement biology and psychology with electronics. The natural systems are used as a guiding metaphor to invent such machines with artificial intelligence. The natural genetic system of species set a platform for the implementation of genetic algorithms to generate useful solutions to optimization and search problem. Genetic algorithm provides a step by step process for moving from one population of chromosomes to a new population using the natural selection with the phenomenon of “survival of the fittest”. The fitness of the genes changes with the population change. Different operators such as crossover, mutation and inversion play a vital role in the formation of new generation offspring. In this paper different techniques for genetic algorithm operators are discussed and a new crossover operator called ‘n bit right shift crossover’ and a new mutation technique called ‘complement mutation’ is proposed.