Exploiting Comparative Studies Using Criteria: Generating Knowledge from an Analyst's Perspective

In this work the use of qualitative preferences for classifying and selecting MOEAs is introduced. The classical notions of the Analyst and the so called Prescriptive Analysis are introduced explicitly in EMO, identifying some difficulties in exploiting the results of the comparative studies performed by the current fashion. A methodology is developed that allows the analyst to translate DM's general preferences as well as quantitative benchmarking results into a practical tool for the comparison of MOEAs, facilitating the selection of the proper method and/or parameters for the MCDM problem at hand. A comparative experimentation is performed using well known state of the art functions, allowing drawing clear conclusions about the utility of the proposed methodology. The results are useful for research, practitioners and analysts involved in benchmarking, comparative studies and prescriptive analysis for EMO.

[1]  Carlos A. Coello Coello,et al.  The Micro Genetic Algorithm 2: Towards Online Adaptation in Evolutionary Multiobjective Optimization , 2003, EMO.

[2]  Shinn-Ying Ho,et al.  A Novel Multi-objective Orthogonal Simulated Annealing Algorithm for Solving Multi-objective Optimization Problems with a Large Number of Parameters , 2004, GECCO.

[3]  Sergio Barba-Romero,et al.  Decisiones multicriterio: fundamentos teóricos y utilización práctica , 1997 .

[4]  Gabriela Ochoa,et al.  Setting The Mutation Rate: Scope And Limitations Of The 1/L Heuristic , 2002, GECCO.

[5]  Bernard Roy,et al.  Decision-Aiding Today: What Should We Expect? , 1999 .

[6]  David Greiner,et al.  Safety Systems Optimum Design by Multicriteria Evolutionary Algorithms , 2003, EMO.

[7]  M. Hansen,et al.  Evaluating the quality of approximations to the non-dominated set , 1998 .

[8]  Tomoyuki Hiroyasu,et al.  Multi-objective RectangularPacking Problem and Its Applications , 2003, EMO.

[9]  Marco Laumanns,et al.  Mutation Control and Convergence in Evolutionary Multi-Object Optimization , 2001 .

[10]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[11]  Gabriel Winter,et al.  Enhancing A Multiobjective Evolutionary Algorithm Through Flexible Evolution , 2004 .

[12]  Petros Koumoutsakos,et al.  Self-Adaptation for Multi-objective Evolutionary Algorithms , 2003, EMO.

[13]  D. Corne,et al.  On Metrics for Comparing Non Dominated Sets , 2001 .

[14]  Hisao Ishibuchi,et al.  An Empirical Study on the Effect of Mating Restriction on the Search Ability of EMO Algorithms , 2003, EMO.

[15]  Joshua D. Knowles,et al.  Some multiobjective optimizers are better than others , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[16]  Shapour Azarm,et al.  Minimal Sets of Quality Metrics , 2003, EMO.

[17]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[18]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.

[19]  David W. Corne,et al.  No Free Lunch and Free Leftovers Theorems for Multiobjective Optimisation Problems , 2003, EMO.

[20]  Martin J. Oates,et al.  PESA-II: region-based selection in evolutionary multiobjective optimization , 2001 .

[21]  Joshua D. Knowles,et al.  On metrics for comparing nondominated sets , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[22]  Dirk Thierens,et al.  The balance between proximity and diversity in multiobjective evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..

[23]  Milan Zeleny,et al.  Multiple Criteria Decision Making (MCDM) , 2004 .

[24]  Jeffrey D. Horn,et al.  F1.12: Multicriteria Decision Making and Evolutionary Computation , 1996 .

[25]  Tomoyuki Hiroyasu,et al.  Multi-objective rectangular packing problem and its applications , 2003 .

[26]  Marco Laumanns,et al.  On the Effects of Archiving, Elitism, and Density Based Selection in Evolutionary Multi-objective Optimization , 2001, EMO.

[27]  Kalyanmoy Deb,et al.  Optimal Operating Conditions for Overhead Crane Maneuvering Using Multi-objective Evolutionary Algorithms , 2004, GECCO.

[28]  Aïda Valls Mateu.  CLUSDM: a multiple criteria decision making method for heterogeneous data sets , 2003 .

[29]  A. Zell,et al.  MOCS: Multi-objective Clustering Selection Evolutionary Algorithm , 2002, GECCO.

[30]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[31]  Xin Yao,et al.  Performance Scaling of Multi-objective Evolutionary Algorithms , 2003, EMO.

[32]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..