Performance Metric Ensemble for Multiobjective Evolutionary Algorithms

• Published in: IEEE transactions on evolutionary computation Vol. 18; no. 1; pp. 131 - 144
• Main Authors: Yen, Gary G., Zhenan He
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.02.2014; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Approximation algorithms; Approximation methods; Benchmark testing; Benchmarking; Combinatorics; Combinatorics. Ordered structures; Computer science; control theory; systems; Data processing. List processing. Character string processing; Double elimination design; ensemble method; Evolutionary algorithms; evolutionary algorithms (EAs); Exact sciences and technology; Graph theory; Heuristic; Heuristic methods; Information retrieval. Graph; Mathematics; Measurement uncertainty; Memory organisation. Data processing; Optimization; Performance measurement; performance metrics; Ranking; Sciences and techniques of general use; Sociology; Software; Statistics; Theoretical computing; Data analysis; Statistical analysis; Hierarchical classification; Evolutionary algorithm; Performance characteristic; Multiobjective programming; Graph theory; Aggregate model; Experimental result; evolutionary algorithms (EAs); Supervised learning; performance metrics; Heuristic method; ensemble method; Metric; Tournament; Double elimination design; Mathematical programming
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2013.2240687

Evolutionary algorithms have been successfully exploited to solve multiobjective optimization problems. In the literature, a heuristic approach is often taken. For a chosen benchmark problem with specific problem characteristics, the performance of multiobjective evolutionary algorithms (MOEAs) is evaluated via some heuristic chosen performance metrics. The conclusion is then drawn based on statistical findings given the preferable choices of performance metrics. The conclusion, if any, is often indecisive and reveals no insight pertaining to which specific problem characteristics the underlying MOEA could perform the best. In this paper, we introduce an ensemble method to compare MOEAs by combining a number of performance metrics using double elimination tournament selection. The double elimination design allows characteristically poor performance of a quality algorithm to still be able to win it all. Experimental results show that the proposed metric ensemble can provide a more comprehensive comparison among various MOEAs than what could be obtained from a single performance metric alone. The end result is a ranking order among all chosen MOEAs, but not quantifiable measures pertaining to the underlying MOEAs.