Abstract
Genetic algorithms (GA) are stochastic search techniques based on the 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 a Modified Genetic Algorithm (MGA) that is developed by making a marriage between the Simple Genetic Algorithm (SGA) and the 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 evaluation function, a selection function, 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 combinatorial optimization problems more accurately and quickly.
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