Comparison of genetic algorithms for experimental multi-objective optimization on the example of medium design for cyanobacteria

In this work, two different genetic algorithms were applied to improve culture media composition for the autotrophic cyanobacteria Synechococcus PCC 7942. Biomass yield and conversion of the asymmetric reduction of 2',3',4',5',6'-pentafluoroacetophenone were considered as simultaneous objectives, resulting in a multi-objective optimization problem. Even when similar performances of both algorithms were observed, it could be shown that a novel strength pareto approach was able to achieve remarkable results with a reduced number of experiments (160 instead of 320). Handling a high number of media components (13), their concentrations were adjusted, delivering high improvements in comparison to the standard BG 11 culture media. The quality of the Synechococcus biocatalyst could be increased up to fivefold compared to the initial state of the optimization.