Archiving With Guaranteed Convergence And Diversity In Multi-objective Optimization
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Evolutionary algorithms for automated drug design towards target molecule properties
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Preferences and their application in evolutionary multiobjectiveoptimization
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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This paper is motivated by problem scenarios in automated drug design. It discusses a modeling approach for design optimization problems with many criteria that can be partitioned into objectives and fuzzy constraints. The purpose of this remodeling is to transform the original criteria such that, when using them in an evolutionary search method, a good view on the trade-off between the different objectives and the satisfaction of constraints is obtained. Instead of reducing a many objective problem to a single-objective problem, it is proposed to reduce it to a multi-objective optimization problem with a low number of objectives, for which the visualization of the Pareto front is still possible and the size of a high-resolution approximation set is affordable. For design problems where it is reasonable to combine certain objectives and/or constraints into logical groups by means of desirability indexes, this method will yield good trade-off results with reduced computational effort. The proposed methodology is evaluated in a case-study on automated drug design where we aim to find molecular structures that could serve as estrogen receptor antagonists.