More efficient genetic algorithm for solving optimization problems

Genetic algorithms (GA) are stochastic search techniques based on mechanics of natural selection and natural genetics. By using genetic operators and cumulative information, genetic algorithms prune the search space and generate a set of plausible solutions. This paper describes an efficient genetic algorithm defined as modified genetic algorithms (MGA). The proposed algorithms is developed by hybridising simple genetic algorithms (SGA) with simulated annealing (SA). In this proposed algorithm, all the conventional genetic operators, such as, selection, reproduction, crossover, mutation, have been used. But they have been modified by a set of new functions such as a selection function 1, a selection function 2, a mutation function, etc., which utilizes the concept of successive descent as seen in simulated annealing. In this way, MGA can be implemented to solve various optimization problems more accurately and quickly.