Population diversity of non-elitist evolutionary algorithms in the exploration phase

Jarosław Arabas , Karol Opara

Abstract

The paper discusses the genetic diversity of real-coded populations processed by an evolutionary algorithm (EA). Diversity is expressed as a variance or a covariance matrix of individuals contained in the population, in one-or multidimensional cases, respectively. We focus on the exploration stage of the optimization, therefore the fitness function is modeled as noise. We prove that the expected value of genetic diversity achieves a level proportional to the mutation covariance matrix. The proportionality coefficient depends solely on the EA parameters. Formulas are derived to predict the diversity for fitness proportionate, tournament and truncation selection, with and without arithmetic crossover and with Gaussian mutation. Experimental validation of the multidimensional case shows that prediction accuracy is satisfactory in a broad spectrum of settings of EA parameters.
Author Jarosław Arabas (FEIT / IN)
Jarosław Arabas,,
- The Institute of Computer Science
, Karol Opara (SRI)
Karol Opara,,
- Systems Research Institute
Journal seriesIEEE Transactions On Evolutionary Computation, ISSN 1089-778X, (A 50 pkt)
Issue year2019
Pages1-1
Publication size in sheets0.3
Keywords in Englishevolutionary algorithm , population diversity , noise fitness model.
ASJC Classification1703 Computational Theory and Mathematics; 2614 Theoretical Computer Science; 1712 Software
DOIDOI:10.1109/TEVC.2019.2917275
URL http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8716593&isnumber=4358751
Languageen angielski
Score (nominal)50
ScoreMinisterial score = 50.0, 04-09-2019, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2017 = 4.724; WoS Impact Factor: 2017 = 8.124 (2) - 2017=8.481 (5)
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