Sampling-based Pareto Optimization for Chance-constrained Monotone Submodular Problems
This work addresses optimization under uncertainty for researchers in evolutionary computation, but it is incremental as it builds on existing surrogate methods.
The paper tackled the problem of optimizing chance-constrained monotone submodular problems by comparing surrogate-based and sampling-based evaluation methods, and found that the proposed ASW-GSEMO algorithm with sampling outperformed other algorithms in experiments on maximum coverage problems.
Recently surrogate functions based on the tail inequalities were developed to evaluate the chance constraints in the context of evolutionary computation and several Pareto optimization algorithms using these surrogates were successfully applied in optimizing chance-constrained monotone submodular problems. However, the difference in performance between algorithms using the surrogates and those employing the direct sampling-based evaluation remains unclear. Within the paper, a sampling-based method is proposed to directly evaluate the chance constraint. Furthermore, to address the problems with more challenging settings, an enhanced GSEMO algorithm integrated with an adaptive sliding window, called ASW-GSEMO, is introduced. In the experiments, the ASW-GSEMO employing the sampling-based approach is tested on the chance-constrained version of the maximum coverage problem with different settings. Its results are compared with those from other algorithms using different surrogate functions. The experimental findings indicate that the ASW-GSEMO with the sampling-based evaluation approach outperforms other algorithms, highlighting that the performances of algorithms using different evaluation methods are comparable. Additionally, the behaviors of ASW-GSEMO are visualized to explain the distinctions between it and the algorithms utilizing the surrogate functions.