A new class of test functions for black box optimization is introduced. Affine OneMax (AOM) functions are defined as compositions of OneMax and invertible affine maps on bit vectors. The black box complexity of the class is upper bounded by a polynomial of large degree in the dimension. The proof relies on discrete Fourier analysis and the Kushilevitz-Mansour algorithm. Tunable complexity is achieved by expressing invertible linear maps as finite products of transvections. The black box complexity of sub-classes of AOM functions is studied. Finally, experimental results are given to illustrate the performance of search algorithms on AOM functions.
- Pub Date:
- June 2021
- Computer Science - Neural and Evolutionary Computing;
- Mathematics - Optimization and Control
- An extended two-page abstract of this work will appear in 2021 Genetic and Evolutionary Computation Conference Companion (GECCO '21 Companion)