Optimal graph design using a knowledge-driven multi-objective evolutionary graph algorithm

Designing appropriate graphs is a problem frequently occurring in several common applications ranging from designing communication and transportation networks to discovering new drugs. More often than not the graphs to be designed need to satisfy multiple, sometimes conflicting, objectives e.g. total length, cost, complexity or other shape and property limitations. In this paper we present our approach to solving the multi-objective graph design problem and obtaining a set of multiple equivalent compromising solutions. Our method uses multi-objective evolutionary graphs, a graph-specific meta-heuristic optimization method that combines evolutionary algorithms with graph theory and local search techniques exploiting domain-specific knowledge. In the experimental section we present results obtained for the problem of designing molecules satisfying multiple pharmaceutically relevant objectives. The results suggest that the proposed method can provide a variety of valid solutions.