The recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA), with a lean, but sufficient, linkage model and an efficient variation operator, has been shown to be a robust and efficient methodology for solving single objective (SO) optimization problems with superior performance compared to classic genetic algorithms (GAs) and estimation-of-distribution algorithms (EDAs). In this paper, we bring the strengths of GOMEAs to the multi-objective (MO) optimization realm. To this end, we modify the linkage learning procedure and the variation operator of GOMEAs to better suit the need of finding the whole Pareto-optimal front rather than a single best solution. Based on state-of-the-art studies on MOEAs, we further pinpoint and incorporate two other essential components for a scalable MO optimizer. First, the use of an elitist archive is beneficial for keeping track of non-dominated solutions when the main population size is limited. Second, clustering can be crucial if different parts of the Pareto-optimal front need to be handled differently. By combining these elements, we construct a multi-objective GOMEA (MO-GOMEA). Experimental results on various MO optimization problems confirm the capability and scalability of our MO-GOMEA that compare favorably with those of the well-known GA NSGA-II and the more recently introduced EDA mohBOA.
Multi-objective optimization, Optimal Mixing, Linkage Tree Genetic Algorithm, Clustering
Energy (theme 4), Logistics (theme 3)
C. Igel
Computational Capacity Planning in Electricity Networks
Genetic and Evolutionary Computation Conference
Intelligent and autonomous systems

Luong, N.H, La Poutré, J.A, & Bosman, P.A.N. (2014). Multi-objective gene-pool optimal mixing evolutionary algorithms. In C Igel (Ed.), Proceedings of Genetic and Evolutionary Computation Conference 2014 (pp. 357–364). ACM. doi:10.1145/2576768.2598261