Indicator-Based Versus Aspect-Based Selection in Multi- and Many-Objective Biochemical Optimization.

The identification of qualified peptides as ligands for diagnostic and therapeutic interventions requires the solution of multi- and many-objective biochemical optimization problems. A MOEA has been designed for molecular optimization with a combined indicator- and Pareto-based selection strategy that encounters common classification problems of the solutions' quality with the rise of the problem dimension. Therefore, a sophisticated selection strategy is presented in this work that selects the individuals for the succeeding generation related to two general aspects in biochemical optimization: the first aspect reflects the peptide quality and the second one the genetic dissimilarity among the peptides in a population. The search behavior of this aspect-based selection is compared to the traditional selection on generic 3- to 6-dimensional physiochemical optimization problems and the impact of the reference point in the aspect-based selection is investigated.