Leveraging conditional linkage models in gray-box optimization with the real-valued gene-pool optimal mixing evolutionary algorithm

Genetic and Evolutionary Computation Conference (GECCO), pp. 603–611

Abstract

Often, real-world problems are of the gray-box type. It has been shown that the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) is in principle capable of exploiting such a Gray-Box Optimization (GBO) setting using linkage models that capture dependencies between problem variables, resulting in excellent performance and scalability on both benchmark and real-world problems that allow for partial evaluations. However, linkage models proposed for RV-GOMEA so far cannot explicitly capture overlapping dependencies. Consequently, performance degrades if such dependencies exist. In this paper, we therefore introduce various ways of using conditional linkage models in RV-GOMEA. Their use is compared to that of non-conditional models, and to VkD-CMA, whose performance is among the state of the art, on various benchmark problems with overlapping dependencies. We find that RV-GOMEA with conditional linkage models achieves the best scalability on most problems, with conditional models leading to similar or better performance than non-conditional models. We conclude that the introduction of conditional linkage models to RV-GOMEA is an important contribution, as it expands the set of problems for which optimization in a GBO setting results in substantially improved performance and scalability. In future work, conditional linkage models may prove to benefit the optimization of real-world problems.

Authors 4

1. Anton Bouter
   Affiliation as printed

   Informatica Amsterdam, The Netherlands

2. Stefanus C. Maree

   University of Amsterdam

   Affiliation as printed

   University of Amsterdam, Amsterdam, The Netherlands

3. Tanja Alderliesten Aachen

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center, Leiden, The Netherlands

4. Peter A. N. Bosman

   Delft University of Technology

   Affiliation as printed

   Delft University of Technology, Delft, The Netherlands