Elitist non-dominated sorting GA-II (NSGA-II) as a parameter-less multi-objective genetic algorithm

Genetic algorithms (GAs) are general-purpose heuristic search algorithms that mimic the evolutionary process in order to find the most fitting solutions. The algorithms were introduced by Holland in 1975. Since then, they have received growing interest due to their ability to discover good solutions quickly for complex searching and optimization problems. The traditional GAs have been converted to multi-objective GAs to solve multi-objective optimization problems successfully. However, GAs require parameter tunings (such as population size, mutation probabilities, crossover probabilities, selection rates) in order to achieve the desirable solutions. The task of tuning GA parameters has been proven to be far from trivial due to the complex interactions among the parameters. It takes trial and error experiments to obtain the optimal GA parameter settings for an arbitrary real-world problem, Many researchers have been trying to understand the interdependencies of GA parameters in order to determine their optimal settings. The objective of this research is to develop the elitist non-dominated sorting GA (NSGA-II) for multi-objective optimization as a parameter-less multiobjective GA. The research then evaluates and discusses the performance of the parameter-less NSGA-II against the original NSGA-II with optimal parameter settings using the experimental result for a test problem borrowed from the literature.