GNBG: A Generalized and Configurable Benchmark Generator for Continuous Numerical Optimization
CoRR(2023)
摘要
As optimization challenges continue to evolve, so too must our tools and
understanding. To effectively assess, validate, and compare optimization
algorithms, it is crucial to use a benchmark test suite that encompasses a
diverse range of problem instances with various characteristics. Traditional
benchmark suites often consist of numerous fixed test functions, making it
challenging to align these with specific research objectives, such as the
systematic evaluation of algorithms under controllable conditions. This paper
introduces the Generalized Numerical Benchmark Generator (GNBG) for
single-objective, box-constrained, continuous numerical optimization. Unlike
existing approaches that rely on multiple baseline functions and
transformations, GNBG utilizes a single, parametric, and configurable baseline
function. This design allows for control over various problem characteristics.
Researchers using GNBG can generate instances that cover a broad array of
morphological features, from unimodal to highly multimodal functions, various
local optima patterns, and symmetric to highly asymmetric structures. The
generated problems can also vary in separability, variable interaction
structures, dimensionality, conditioning, and basin shapes. These customizable
features enable the systematic evaluation and comparison of optimization
algorithms, allowing researchers to probe their strengths and weaknesses under
diverse and controllable conditions.
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