dc.description.abstract | Many objective optimization problems have turned out to be a considerable challenge for evolutionary
algorithms due to the difficulty of finding and visualizing high-dimensional Pareto frontiers. Fortunately,
however, the task can be simplified whenever an interaction with a human decision maker is possible. In-
stead of finding the entire Pareto frontier, the evolutionary search can be guided to the parts of the space
that are most relevant for the decision maker. In this paper, we propose an interactive method for solving
many objective optimization problems. Drawing on the recent developments in multiple criteria decision
making, we introduce an effective strategy for leveraging polyhedral preference cones within an evolu-
tionary algorithm. The approach is mathematically motivated and is applicable to situations, where the
user’s preferences can be assumed to follow an unknown quasi-concave and increasing utility function. In
addition to considering the preference cones as a tool for eliminating non-preferred solution candidates,
we also present how the the cones can be leveraged in approximating the directions of steepest ascent to
guide the subsequent search done by the evolutionary algorithm through a proposed merit function. To
evaluate the performance of the algorithm, we consider well known test problems as well as a practical
facility location problem. | en_US |