TY - GEN
T1 - Studying the Effect of Robustness Measures in Offline Parameter Tuning for Estimating the Performance of MOEA/D
AU - Pescador-Rojas, Miriam
AU - Pallez, Denis
AU - Castellanos, Carlos Ignacio Hernández
AU - Coello, Carlos A.Coello
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Offline parameter tuning (OPT) of multi-objective evolutionary algorithms (MOEAs) has the goal of finding an appropriate set of parameters for solving a large number of problems. According to the no free lunch theorem (NFL), no algorithm can have the best performance in all classes of optimization problems. However, it is possible to find an appropriate set of parameters of an algorithm for solving a particular class of problems. For that sake, we need to study how to estimate the aggregation quality function for an algorithmic configuration assessed on a set of optimization problems. In this paper, we study robustness measures for dealing with the parameter settings of stochastic algorithms. We focus on decomposition-based MOEAs and we propose to tune scalarizing functions for solving some classes of problems based on the Pareto front shapes using up to 7 objective functions. Based on our experimental results, we were able to derive interesting guidelines to evaluate the quality of algorithmic configurations using a combination of descriptive statistics.
AB - Offline parameter tuning (OPT) of multi-objective evolutionary algorithms (MOEAs) has the goal of finding an appropriate set of parameters for solving a large number of problems. According to the no free lunch theorem (NFL), no algorithm can have the best performance in all classes of optimization problems. However, it is possible to find an appropriate set of parameters of an algorithm for solving a particular class of problems. For that sake, we need to study how to estimate the aggregation quality function for an algorithmic configuration assessed on a set of optimization problems. In this paper, we study robustness measures for dealing with the parameter settings of stochastic algorithms. We focus on decomposition-based MOEAs and we propose to tune scalarizing functions for solving some classes of problems based on the Pareto front shapes using up to 7 objective functions. Based on our experimental results, we were able to derive interesting guidelines to evaluate the quality of algorithmic configurations using a combination of descriptive statistics.
KW - Offline parameter tuning
KW - multiobjective evolutionary algorithms
KW - robustness measures
KW - scalarizing functions
UR - http://www.scopus.com/inward/record.url?scp=85062795046&partnerID=8YFLogxK
U2 - 10.1109/SSCI.2018.8628704
DO - 10.1109/SSCI.2018.8628704
M3 - Contribución a la conferencia
AN - SCOPUS:85062795046
T3 - Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
SP - 204
EP - 211
BT - Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
A2 - Sundaram, Suresh
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
Y2 - 18 November 2018 through 21 November 2018
ER -