Peptides play a key role in the development of drug candidates and diagnostic interventions, respectively. The design of peptides is cost-intensive and difficult in general for several well-known reasons. Multi-objective evolutionary algorithms (MOEAs) introduce adequate in silico methods for finding optimal peptides sequences which optimize several molecular properties. A mutation-specific fast non-dominated sorting GA (termed MSNSGA-II) was especially designed for this purpose.
In this work, an empirical study is presented about the performance of MSNSGA-II which is extended by optionally three different recombination operators. The main idea is to gain an insight into the significance of recombination for the performance of MSNSGA-II in general - and to improve the performance with these intuitive recombination methods for biochemical optimization. The benchmark test for this study is a three-dimensional optimization problem, using fitness functions provided by the BioJava library.