DOI

The importance and potential of Gray-Box Optimization (GBO) with evolutionary algorithms is becoming increasingly clear lately both for benchmark and real-world problems. We consider the GBO setting where partial evaluations are possible, meaning that sub-functions of the evaluation function are known and can be exploited to improve optimization efficiency. In this paper, we show that the efficiency of GBO can be greatly improved through large-scale parallelism, exploiting the fact that each evaluation function requires the calculation of a number of independent sub-functions. This is especially interesting for real-world problems where often the majority of the computational effort is spent on the evaluation function. Moreover, we show how the best parallelization technique largely depends on factors including the number of sub-functions and their required computation time, revealing that for different parts of the optimization the best parallelization technique should be selected based on these factors. As an illustration, we show how large-scale parallelization can be applied to optimization of high-dose-rate brachytherapy treatment plans for prostate cancer. We find that use of a modern Graphics Processing Unit (GPU) was the most efficient parallelization technique in all realistic scenarios, leading to substantial speed-ups up to a factor of 73.
Original languageEnglish
Title of host publicationGECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages1199-1206
Number of pages8
ISBN (Electronic)978-1-4503-5618-3
DOIs
Publication statusPublished - 1 Jul 2018
EventGECCO 2018: Genetic and Evolutionary Computation Conference - Kyoto, Japan
Duration: 15 Jul 201819 Jul 2018

Conference

ConferenceGECCO 2018
CountryJapan
CityKyoto
Period15/07/1819/07/18

    Research areas

  • Gray-box optimization, parallel, CUDA, GPU, GOMEA

ID: 46986094