TY - GEN
T1 - More is not Always Better
T2 - 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021
AU - Del Ser, Javier
AU - Osaba, Eneko
AU - Martinez, Aritz D.
AU - Bilbao, Miren Nekane
AU - Poyatos, Javier
AU - Molina, Daniel
AU - Herrera, Francisco
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Much controversy has been lately risen around the design and performance of modern bio-inspired optimization methods, in particular due to the alleged lack of algorithmic novelty in their definition with respect to traditional heuristic solvers. In this work we present a first attempt at shedding empirical evidence over this debate, for which results of a benchmark with unprecedented scales in terms of problems and algorithms are reported and discussed. Specifically, informed conclusions are held in what refers to the claimed superior performance of these bio-inspired solvers and their competitiveness when compared to competition-winning alternatives. Finally, we prove that the tailored selection of a subset of problems and techniques can unfairly bias the comparisons favoring any of such algorithms, ultimately arriving at illusory conclusions about their comparative performance.
AB - Much controversy has been lately risen around the design and performance of modern bio-inspired optimization methods, in particular due to the alleged lack of algorithmic novelty in their definition with respect to traditional heuristic solvers. In this work we present a first attempt at shedding empirical evidence over this debate, for which results of a benchmark with unprecedented scales in terms of problems and algorithms are reported and discussed. Specifically, informed conclusions are held in what refers to the claimed superior performance of these bio-inspired solvers and their competitiveness when compared to competition-winning alternatives. Finally, we prove that the tailored selection of a subset of problems and techniques can unfairly bias the comparisons favoring any of such algorithms, ultimately arriving at illusory conclusions about their comparative performance.
KW - Benchmarking
KW - Meta-heuristic Optimization
KW - Real-Parameter Optimization
UR - http://www.scopus.com/inward/record.url?scp=85125804403&partnerID=8YFLogxK
U2 - 10.1109/SSCI50451.2021.9660030
DO - 10.1109/SSCI50451.2021.9660030
M3 - Conference contribution
AN - SCOPUS:85125804403
T3 - 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
BT - 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 5 December 2021 through 7 December 2021
ER -