APOGA: An Adaptive Population Pool Size based Genetic Algorithm

In Genetic Algorithm, the population size is an important parameter which directly influences the ability to search an optimum solution in the search space. Many researchers have revealed that having a large number of population leads to the accuracy of getting an optimal solution. But having a large population size will not be a good idea in case where the search space is small. Hence, optimal size for population pool has been determined, but the size is kept fixed. Despite the pool size is optimal, the fixed size population leads to time complexity and make the search more complex by increasing the number of generation to converge. So, the population pool size needs to be dynamically varying through the entire GA evolution of new solutions. This paper proposes an adaptive population pool based genetic algorithm, termed as APOGA, in which the population pool size either grown or shrunk at every iteration based on the performance status of the algorithm. The proposed algorithm is implemented and the performance is compared with standard genetic algorithm while solving benchmark test function with varying size of solution space. The experimental results show that APOGA outperforms standard GA for all the solution spaces.