Improving search efficiency via adaptive acquisition function selection in discrete black-box optimization

Discrete black-box optimization is hindered by an exponentially growing number of candidate solutions and costly evaluations. To address this, researchers have proposed an adaptive approach to selecting acquisition functions, which guide the search for optimal solutions. This method builds upon Bayesian Optimization of Combinatorial Structures (BOCS), a parametric technique that excels with limited data. By adaptively choosing acquisition functions, the search process becomes more efficient, allowing for the identification of promising solutions within a restricted number of trials¹. The implications of this research extend beyond discrete optimization, as it can inform strategies for navigating complex, high-stakes problem spaces. So what matters to practitioners is that this adaptive approach can be applied to various domains, potentially enhancing the efficiency and effectiveness of optimization efforts in fields like cybersecurity and quantum physics.