Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks
CoRR(2024)
摘要
This study explores the influence of modules on the performance of modular
optimization frameworks for continuous single-objective black-box optimization.
There is an extensive variety of modules to choose from when designing
algorithm variants, however, there is a rather limited understanding of how
each module individually influences the algorithm performance and how the
modules interact with each other when combined. We use the functional ANOVA
(f-ANOVA) framework to quantify the influence of individual modules and module
combinations for two algorithms, the modular Covariance Matrix Adaptation
(modCMA) and the modular Differential Evolution (modDE). We analyze the
performance data from 324 modCMA and 576 modDE variants on the BBOB benchmark
collection, for two problem dimensions, and three computational budgets.
Noteworthy findings include the identification of important modules that
strongly influence the performance of modCMA, such as the weightsoption and mirrored modules for low dimensional problems, and
the base sampler for high dimensional problems. The large individual
influence of the lpsr module makes it very important for the
performance of modDE, regardless of the problem dimensionality and the
computational budget. When comparing modCMA and modDE, modDE undergoes a shift
from individual modules being more influential, to module combinations being
more influential, while modCMA follows the opposite pattern, with an increase
in problem dimensionality and computational budget.
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