The results show that the proposed initialization methods (Quasi_LHS) and Quasi-based Stratified improved to some extent the quality of final results of the experiments.", For our research, a set of well known multi objective optimization problems were used in order to evaluate our initial population strategies using NSGAII algorithm. Furthermore, we propose a modified version of Stratified sampling method that uses Quasi-Random numbers to represent the intervals. We also propose a modified version of Latin Hypercube sampling method called (Quasi_LHS) that uses Quasi random numbers as a backbone in its body. In this work, a set of initialization methods were examined such as, Latin hypercube sampling (LHS), Quasi-Random sampling and stratified sampling. The common method is to generate the initial population randomly.
Latin hypercube sampling followed optimization how to#
Unfortunately, little research sheds light at how to generate the initial population. The results show that the proposed initialization methods (Quasi_LHS) and Quasi-based Stratified improved to some extent the quality of final results of the experiments.Ībstract = "Evolutionary algorithms are the most widely used meta-heuristics for solving multi objective optimization problems, and since all of these algorithms are population based, such as NSGAII, there are a set of factors that affect the final outcomes of these algorithms such as selection criteria, crossover, mutation and fitness evaluation. Evolutionary algorithms are the most widely used meta-heuristics for solving multi objective optimization problems, and since all of these algorithms are population based, such as NSGAII, there are a set of factors that affect the final outcomes of these algorithms such as selection criteria, crossover, mutation and fitness evaluation.