TY - GEN
T1 - Leveraging conditional linkage models in gray-box optimization with the real-valued gene-pool optimal mixing evolutionary algorithm
AU - Bouter, Anton
AU - Maree, Stefanus C.
AU - Alderliesten, Tanja
AU - Bosman, Peter A. N.
PY - 2020/6/25
Y1 - 2020/6/25
N2 - 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.
AB - 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.
KW - GOMEA
KW - Gray-box optimization
KW - Linkage modeling
UR - http://www.scopus.com/inward/record.url?scp=85091753885&partnerID=8YFLogxK
U2 - https://doi.org/10.1145/3377930.3390225
DO - https://doi.org/10.1145/3377930.3390225
M3 - Conference contribution
T3 - GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference
SP - 603
EP - 611
BT - GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference
PB - Association for Computing Machinery
T2 - 2020 Genetic and Evolutionary Computation Conference, GECCO 2020
Y2 - 8 July 2020 through 12 July 2020
ER -