@inproceedings{Deb2006e, abstract = {Evolutionary multi-objective optimization (EMO) methodologies have been amply applied to find a representative set of Pareto-optimal solutions in the past decade and beyond. Although there are advantages of knowing the range of each objective for Pareto-optimality and the shape of the Pareto-optimal frontier itself in a problem for an adequate decision-making, the task of choosing a single preferred Pareto-optimal solution is also an important task which has received a lukewarm attention so far. In this paper, we combine one such preference-based strategy with an EMO methodology and demonstrate how, instead of one solution, a preferred set solutions near the reference points can be found parallely. We propose a modified EMO procedure based on the elitist non-dominated sorting GA 'or NSGA-II. On two-objective to 10-objective optimization problems, the modified NSGA-II approach shows its efficacy in: finding an adequate set of Pareto-optimal points. Such procedures will provide the decision-maker with a set of solutions near her/his preference so that a better and a more reliable decision can be made. Copyright 2006 ACM.}, author = {Deb, Kalyanmoy and Sundar, J.}, booktitle = {GECCO 2006 - Genetic and Evolutionary Computation Conference}, doi = {10.1145/1143997.1144112}, isbn = {1595931864}, keywords = {Decision making,Multi-objective optimization,Points,Preference-based optimization,Reference}, pages = {635--642}, publisher = {Association for Computing Machinery (ACM)}, title = {{Reference point based multi-objective optimization using evolutionary algorithms}}, volume = {1}, year = {2006} }