The practicality of Pareto-dominance in solving many-objective optimization problems becomes questionable due to its inability to factor the critical human decision-making (HDM) elements, including…
Many societal and industrial problem-solving tasks involving search, optimization, design, and management are conveniently decomposed into hierarchical subproblems. While this process allows a syst…
Innovization is a task of learning common relationships among some or all of the Pareto-optimal (PO) solutions in multi- and many-objective optimization problems. A recent study has shown that a ch…
Most evolutionary many-objective optimization (EMaO) algorithms start with a description of a number of the predefined set of reference points on a unit simplex. So far, most studies have used the …
Dominance move (DoM) is a binary quality indicator that can be used in multiobjective and many-objective optimization to compare two solution sets obtained from different simulations. The DoM indic…
This article concerns the study of mixed Pareto-lexicographic multiobjective optimization problems where the objectives must be partitioned in multiple priority levels (PLs). A PL is a group of obj…
Neural architecture search (NAS) has emerged as a promising avenue for automatically designing task-specific neural networks. Existing NAS approaches require one complete search for each deployment…
Convolutional neural networks (CNNs) are the backbones of deep learning paradigms for numerous vision tasks. Early advancements in CNN architectures are primarily driven by human expertise and by e…
To represent a many-objective Pareto-optimal front having four or more dimensions of the objective space, a large number of points are necessary. However, for choosing a single preferred point from…