Multi-objective evolutionary algorithms (MOEAs)are often criticized for their high-computational costs. Thisbecomes especially relevant in simulation-based optimizationwhere the objectives lack a closed form and are expensive toevaluate. Over the years, meta-modeling or surrogate modelingtechniques have been used to build inexpensive approximationsof the objective functions which reduce the overall number offunction evaluations (simulations). Some recent studies however,have pointed out that accurate models of the objective functionsmay not be required at all since evolutionary algorithms onlyrely on the relative ranking of candidate solutions. Extendingthis notion to MOEAs, algorithms which can ‘learn’ Paretodominancerelations can be used to compare candidate solutionsunder multiple objectives. With this goal in mind, in thispaper, we study the performance of ten different off-the-shelfclassification algorithms for learning Pareto-dominance relationsin the ZDT test suite of benchmark problems. We considerprediction accuracy and training time as performance measureswith respect to dimensionality and skewness of the training data.Being a preliminary study, this paper does not include results ofintegrating the classifiers into the search process of MOEAs.
['Sunith Bandaru', 'Amos H. C. Ng', 'Kalyanmoy Deb']
On the Performance of Classification Algorithms for Learning Pareto-Dominance Relations