%0 Generic
%A Del Ser, Javier
%A Osaba, Eneko
%A Martinez, Aritz D.
%A Bilbao, Miren Nekane
%A Poyatos, Javier
%A Molina, Daniel
%A Herrera, Francisco
%T More is not Always Better: 												Insights from a Massive Comparison of Meta-heuristic Algorithms over Real-Parameter Optimization Problems
%J 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
%D 2021
%U https://hdl.handle.net/11556/2172
%X 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.
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