caa a22 4500 605470138 CHVBK 20210128100326.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s00500-014-1419-1 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1419-1 A novel optimization hardness indicator based on the relationship between optimization hardness and frequency features of real-parameter problems [Elektronische Daten] [Kun Li, Ming Li, Hao Chen] For evolutionary algorithms with the ability to self-adapt, linking the algorithmic operators and the problem features is one of the most interesting topics. One of the best ways to begin a study of this topic is to explore the relationship between the optimization hardness and the problem features. This paper attempts to interpret the relationship between optimization hardness and frequency features of real-parameter problems through a qualitative analysis based on an idealized model. Based on the results of a theoretically qualitative analysis, the effective high-frequency ratio (EHFR) is subsequently proposed to measure the optimization hardness of real-parameter problems. Finally, three aspects to the performance of EHFR are evaluated: stability, precision and ability to distinguish. Test results show that the EHFR is relevant not only for the results of theoretical analysis, but also for the other features related to the optimization hardness. Springer-Verlag Berlin Heidelberg, 2014 Problem difficulty nationallicence Fourier transform nationallicence Evolutionary computation nationallicence Memetic algorithm nationallicence Optimal feature factor nationallicence Li Kun College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China aut Li Ming College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China aut Chen Hao School of Information Engineering, Nanchang Hangkong University, Nanchang, Jiangxi, China aut Soft Computing Springer Berlin Heidelberg 19/8(2015-08-01), 2287-2303 1432-7643 19:8<2287 2015 19 500 https://doi.org/10.1007/s00500-014-1419-1 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1419-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Li Kun College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China aut NATIONALLICENCE 700 1- Li Ming College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China aut NATIONALLICENCE 700 1- Chen Hao School of Information Engineering, Nanchang Hangkong University, Nanchang, Jiangxi, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/8(2015-08-01), 2287-2303 1432-7643 19:8<2287 2015 19 500