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Mixed-Integer Benchmark Problems for Single-and Bi-Objective Optimization

Tea Tušar 1 Dimo Brockhoff 2, 3 Nikolaus Hansen 2, 3 
2 RANDOPT - Randomized Optimisation
CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique, Inria Saclay - Ile de France
Abstract : We introduce two suites of mixed-integer benchmark problems to be used for analyzing and comparing black-box optimization algorithms. They contain problems of diverse difficulties that are scalable in the number of decision variables. The bbob-mixint suite is designed by partially discretizing the established BBOB (Black-Box Optimization Benchmarking) problems. The bi-objective problems from the bbob-biobj-mixint suite are, on the other hand, constructed by using the bbob-mixint functions as their separate objectives. We explain the rationale behind our design decisions and show how to use the suites within the COCO (Comparing Continuous Optimizers) platform. Analyzing two chosen functions in more detail, we also provide some unexpected findings about their properties.
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Submitted on : Thursday, March 14, 2019 - 2:41:23 PM
Last modification on : Wednesday, November 30, 2022 - 3:58:04 AM
Long-term archiving on: : Saturday, June 15, 2019 - 7:35:33 PM


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  • HAL Id : hal-02067932, version 1


Tea Tušar, Dimo Brockhoff, Nikolaus Hansen. Mixed-Integer Benchmark Problems for Single-and Bi-Objective Optimization. GECCO 2019 -The Genetic and Evolutionary Computation Conference, Jul 2019, Prague, Czech Republic. ⟨hal-02067932⟩



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