The impact of Quality Indicators on the rating of Multi-objective Evolutionary Algorithms

被引：30

作者：

Ravber, Miha ^{[1
]}

Mernik, Marjan ^{[1
]}

Crepinkek, Matej ^{[1
]}

机构：

[1] Univ Maribor, Fac Elect Engn & Comp Sci, Maribor, Slovenia

来源：

APPLIED SOFT COMPUTING | 2017年 / 55卷

关键词：

Multi-objective optimization; Evolutionary Algorithms; Quality Indicator; Performance assessment; Chess rating; BEE COLONY ALGORITHM; PERFORMANCE ASSESSMENT; OPTIMIZATION; DIVERSITY; SYSTEM;

D O I：

10.1016/j.asoc.2017.01.038

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Evaluating and comparing multi-objective optimizers is an important issue. But, when doing a comparison, it has to be noted that the results can be influenced highly by the selected Quality Indicator. Therefore, the impact of individual Quality Indicators on the ranking of Multi-objective Optimizers in the proposed method must be analyzed beforehand. In this paper the comparison of several different Quality Indicators with a method called Chess Rating System for Evolutionary Algorithms (CRS4EAs) was conducted in order to get a better insight on their characteristics and how they affect the ranking of Multi-objective Evolutionary Algorithms (MOEAs). Although it is expected that Quality Indicators with the same optimization goals would yield a similar ranking of MOEAs, it has been shown that results can be contradictory and significantly different. Consequently, revealing that claims about the superiority of one MOEA over another can be misleading. (c) 2017 Elsevier B.V. All rights reserved.

引用

页码：265 / 275

页数：11

共 50 条

[1] Survey on Performance Indicators for Multi-Objective Evolutionary Algorithms
Wang L.-P.
Ren Y.
Qiu Q.-C.
Qiu F.-Y.
Jisuanji Xuebao/Chinese Journal of Computers, 2021, 44 (08): : 1590 - 1619
[2] Ranking Multi-Objective Evolutionary Algorithms using a Chess Rating System with Quality Indicator Ensemble
Ravber, Miha
Mernik, Marjan
Crepinsek, Matej
2017 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2017, : 1503 - 1510
[3] Estimating the Quality of Initial Populations in Multi-Objective Evolutionary Algorithms
Benecke, Tobias
Mostaghim, Sanaz
PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022, 2022, : 324 - 327
[4] Unassisted thresholding based on multi-objective evolutionary algorithms
Hinojosa, Salvador
Avalos, Omar
Oliva, Diego
Cuevas, Erik
Pajares, Gonzalo
Zaldivar, Daniel
Galvez, Jorge
KNOWLEDGE-BASED SYSTEMS, 2018, 159 : 221 - 232
[5] Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey
Falcon-Cardona, Jesus Guillermo
Gomez, Raquel Hernandez
Coello, Carlos A. Coello
Tapia, Ma. Guadalupe Castillo
SWARM AND EVOLUTIONARY COMPUTATION, 2021, 67
[6] The Importance of Diversity in the Variable Space in the Design of Multi-Objective Evolutionary Algorithms
Segura, Carlos
Castillo, Joel Chacon
Schutze, Oliver
APPLIED SOFT COMPUTING, 2023, 136
[7] Genetic diversity as an objective in multi-objective evolutionary algorithms
Toffolo, A
Benini, E
EVOLUTIONARY COMPUTATION, 2003, 11 (02) : 151 - 167
[8] A Technique for the Optimization of the Parameters of Technical Indicators with Multi-Objective Evolutionary Algorithms
Bodas Sagi, Diego J.
Soltero, Francisco J.
Ignacio Hidalgo, J.
Fernandez, Pablo
Fernandez, F.
2012 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2012,
[9] Multi-Objective Optimization by Using Evolutionary Algorithms: The p-Optimality Criteria
Carreno Jara, Emiliano
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2014, 18 (02) : 167 - 179
[10] A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms
Wagner, Tobias
Trautmann, Heike
Marti, Luis
EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, 2011, 6576 : 16 - +

← 1 2 3 4 5 →