moPGA: Towards a New Generation of Multi-objective Genetic Algorithms

This paper describes a multi-objective parameter-less genetic algorithm (moPGA), which combines several recent developments including efficient non-dominated sorting, linkage learning, isin-Dominance, building-block mutation and convergence detection. Additionally, a novel method of clustering in the objective space using an isin-Pareto Set is introduced. Comparisons with well-known multi-objective GAs on scalable benchmark problems indicate that the algorithm scales well with problem size in terms of number of function evaluations and quality of solutions found. moPGA was built for easy usage and hence, in addition to the problem function and encoding, there are only two required user defined parameters; (1) the maximum running time or generations and (2) the precision of the desired solutions (isin).