biostudies-literatureUnknown13Diot J<h4>Introduction</h4>Advances in genotyping technologies have provided breeders with access to the genotypic values of several thousand genetic markers in their breeding materials. Combined with phenotypic data, this information facilitates genomic selection. Although genomic selection can benefit breeders, it does not guarantee efficient genetic improvement. Indeed, multiple components of breeding schemes may affect the efficiency of genetic improvement and controlling all components may not be possible. In this study, we propose a new application of Bayesian optimisation for optimizing breeding schemes under specific constraints using computer simulation.<h4>Methods</h4>Breeding schemes are simulated according to nine different parameters. Five of those parameters are considered constraiFrontiers in plant science1050198https://www.ebi.ac.uk/biostudies/studies/S-EPMC9875003biostudies-literatureBayesian optimisation for breeding schemes.PMC9875003Diot JIwata HfalseBayesian optimisation for breeding schemes.<h4>Introduction</h4>Advances in genotyping technologies have provided breeders with access to the genotypic values of several thousand genetic markers in their breeding materials. Combined with phenotypic data, this information facilitates genomic selection. Although genomic selection can benefit breeders, it does not guarantee efficient genetic improvement. Indeed, multiple components of breeding schemes may affect the efficiency of genetic improvement and controlling all components may not be possible. In this study, we propose a new application of Bayesian optimisation for optimizing breeding schemes under specific constraints using computer simulation.<h4>Methods</h4>Breeding schemes are simulated according to nine different parameters. Five of those parameters are considered constrai2022-01-01T00:00:00Z20222025-04-22T03:33:19.255Z2025-04-05T20:46:17.901ZS-EPMC98750033671477610.3389/fpls.2022.1050198