Improved Performance of Replacement Strategies in GA
Genetic algorithm is blind global search technique based on population and exploiting objective function. Genetic algorithm works on set of individual, not on single solution. After applying reproduction and mutation operator's replacement strategy is executed. This paper discusses various replacement strategies to help in selecting suitable replacement class (generational and steady state), which the authors apply over the basic steps i.e. selection, crossover, mutation. Steady state replacement helps in enhancing the performance of genetic algorithm as it propitiates useful diversity.