REVIEW 3 cited by
The Evolutionary Computation Methods No One Should Use
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
The center-bias (or zero-bias) operator has recently been identified as one of the problems plaguing the benchmarking of evolutionary computation methods. This operator lets the methods that utilize it easily optimize functions that have their respective optima in the center of the feasible set. In this paper, we describe a simple procedure that can be used to identify methods that incorporate a center-bias operator and use it to investigate 90 evolutionary computation methods that were published between 1987 and 2022. We show that more than half (47 out of the 90) of the considered methods have the center-bias problem. We also show that the center-bias is a relatively new phenomenon (with the first identified method being from 2012), but its inclusion has become extremely prevalent in the last few years. Lastly, we briefly discuss the possible root causes of this issue.
Forward citations
Cited by 3 Pith papers
-
Benchmarking global optimization techniques for unmanned aerial vehicle path planning
A new set of 56 UAV path-planning benchmark instances is introduced and shown via landscape analysis to differ from standard suites, with CEC-winning evolutionary methods performing best.
-
Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows
An evolutionary loop of foundation-model agents could automate the full optimization pipeline, but the paper's evidence only covers two isolated components.
-
The Paradox of Success in Evolutionary and Bioinspired Optimization: Revisiting Critical Issues, Key Studies, and Methodological Pathways
A survey argues that bioinspired optimization suffers from a 'paradox of success' in which many metaphor-based algorithms lack real novelty, and lays out methodological remedies.
Discussion (0). Continue with ORCID to comment.