Modified enhanced steady state genetic algorithm for Scheduling & Optimization problems

Abstract

Scheduling & Optimization problems are iterative in nature. To find a ideal solution to which is a complex task. These types of problems may be effectively solved and optimal solutions which may be close to the ideal solution may be derived with the help of evolutionary algorithms like the Genetic Algorithm. This paper introduces a new variant of genetic algorithm called Modified Enhanced Steady State Genetic Algorithm (MESSGA) which uses Fuzzy Logic on crossover probability, mutation probability and insertion, for better convergence time. The results of this paper are studied on a common scheduling problem faced by all universities to assign externals for viva-vose or examination to other colleges under its jurisdiction.