Analysis of selection noise in genetic algorithms

Abstract Selection is often considered as a fundamental force in the evolutionary process. Genetic drift, or selection noise, is an important characteristic of selection methods. It has a direct effect on the performance of genetic algorithms. In this paper, a brief review of methods to analyze genetic drift is given, and known estimations of selection noise of various selection schemes used in genetic algorithms are presented. After that, genetic drift of widely used proportional, ranking, and tournament selection schemes is thoroughly studied. To this end, two new measures for selection noise analysis are proposed, namely the noise takeover time and pure reproduction rate. Using these measures, the effect of population size, chromosome length, and selection scheme parameters on genetic drift is analyzed. Also, selection schemes known as being selection pressure equivalent are tested for selection noise equivalence. Both theoretical and experimental approaches are used for the analysis. The results obtained are presented in tabular form. Wherever possible, it is indicated whether the obtained results are identical or different from the results of previous studies. Since no comprehensive study of selection noise has been conducted previously, this indication concerns only some of the results. Although our results differ at some points from those presented earlier, they are consistent on both measures.

Authors

Institutions

Publication Details

Journal
Soft Computing
Published
2025-04-01
DOI
https://doi.org/10.1007/s00500-025-10652-6
Primary Topic
Evolutionary Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of selection noise in genetic algorithms

Nataliya M. Gulayeva, Joaquín Borrego‐Díaz, Fernando Sancho Caparrini
1 links
Soft Computing
Evolutionary Algorithms and Applications
article

Analysis of selection noise in genetic algorithms

Nataliya M. Gulayeva, Joaquín Borrego‐Díaz, Fernando Sancho Caparrini
article en

Abstract

Abstract Selection is often considered as a fundamental force in the evolutionary process. Genetic drift, or selection noise, is an important characteristic of selection methods. It has a direct effect on the performance of genetic algorithms. In this paper, a brief review of methods to analyze genetic drift is given, and known estimations of selection noise of various selection schemes used in genetic algorithms are presented. After that, genetic drift of widely used proportional, ranking, and tournament selection schemes is thoroughly studied. To this end, two new measures for selection noise analysis are proposed, namely the noise takeover time and pure reproduction rate. Using these measures, the effect of population size, chromosome length, and selection scheme parameters on genetic drift is analyzed. Also, selection schemes known as being selection pressure equivalent are tested for selection noise equivalence. Both theoretical and experimental approaches are used for the analysis. The results obtained are presented in tabular form. Wherever possible, it is indicated whether the obtained results are identical or different from the results of previous studies. Since no comprehensive study of selection noise has been conducted previously, this indication concerns only some of the results. Although our results differ at some points from those presented earlier, they are consistent on both measures.

Soft ComputingVol. 29(8)
National University of Kyiv-Mohyla Academy (UA), Universidad de Sevilla (ES)
Universidad de Sevilla, Agencia Estatal de Investigación
Openalex Percentile: Top 95%
Evolutionary Algorithms and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.