Microgenetic algorithms as generalized hill-climbing operators for GA optimization

In this paper, we investigate the potential of a microgenetic algorithm (MGA) [genetic algorithm (GA) with small population and short evolution] as a generalized hill-climbing operator. Combining a standard GA with the suggested MGA operator leads to a hybrid genetic scheme GA-MGA, with enhanced searching qualities. The main GA performs global search while the MGA explores a neighborhood of the current solution provided by the main GA, looking for better solutions. In contrast to conventional hill climbers that attempt independent steps along each axis, the MGA operator performs genetic local search, The major advantage of MGA is its ability to identify and follow narrow ridges of arbitrary direction leading to the global optimum, The proposed GA-MGA scheme is tested against 13 different schemes, including a simple GA and GAs with different hill-climbing operators. Experiments are conducted on a test set including eight constrained optimization problems with continuous variables, Extensive simulation results demonstrate the efficiency of the proposed GA-MGA scheme. For the same number of fitness evaluations, GA-MGA exhibited a significantly better performance in terms of solution accuracy, feasibility percentage of the attained solutions, and robustness.