Quantifying selection pressure

Evert Haasdijk, Jacqueline Heinerman

Research output: Contribution to JournalArticleAcademicpeer-review


Selection is an essential component of any evolutionary system and analysing this fundamental force in evolution can provide relevant insights into the evolutionary development of a population. The 1990s and early 2000s saw a substantial number of publications that investigated selection pressure through methods such as takeover time and Markov chain analysis. Over the last decade, however, interest in the analysis of selection in evolutionary computing has waned. The established methods for analysis of selection pressure provide little insight when selection is based on more than comparison-of-fitness values. This can, for instance, be the case in coevolutionary systems, when measures unrelated to fitness affect the selection process (e.g., niching) or in systems that lack a crisply defined objective function. This article proposes two metrics that holistically consider the statistics of the evolutionary process to quantify selection pressure in evolutionary systems and so can be applied where traditionally used methods fall short. Themetrics are based on a statistical analysis of the relation between reproductive success and a quantifiable trait: one method builds on an estimate of the probability that this relation is random; the other uses a correlation measure. These metrics provide convenient tools to analyse selection pressure and so allow researchers to better understand this crucial component of evolutionary systems. Both metrics are straightforward to implement and can be used in post-hoc analyses as well as during the evolutionary process, for example, to inform parameter control mechanisms. Anumber of case studies and a critical analysis show that the proposed metrics provide relevant and reliable measures of selection pressure.

Original languageEnglish
Pages (from-to)213-235
Number of pages23
JournalEvolutionary computation
Issue number2
Early online date1 Jun 2018
Publication statusPublished - Jun 2018


The work presented here was funded through the European Union’s Horizon 2020 research and innovation program under grant agreement No. 640891 (DREAM).

FundersFunder number
Horizon 2020 Framework Programme
Seventh Framework Programme640891


    • Evolutionary algorithms
    • Probability of selection
    • Selection pressure


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