Multi-objective gene-pool optimal mixing evolutionary algorithms

Ngoc Hoang Luong, Han La Poutré, Peter A.N. Bosman
2014 Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO '14  
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 multiobjective (MO) optimization realm. To this
more » ... , we modify the linkage learning procedure and the variation operator of GOMEAs to better suit the need of finding the whole Paretooptimal 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.
doi:10.1145/2576768.2598261 dblp:conf/gecco/LuongPB14 fatcat:sfhoquvxinfdphrbstamen7laq