An Adaptive Bayesian Approach to SurrogateAssisted Evolutionary Multiobjective Optimization Abstract   Surrogate models have been widely used for solving computationally expensive multiobjective optimization problems (MOPs). The efficient global optimization (EGO) algorithm, a Bayesian approach to surrogateassisted optimization, has become very popular in surrogateassisted evolutionary optimization. In this paper, we propose an adaptive Bayesian approach to surrogateassisted evolutionary algorithm to solve expensive MOPs. The main idea is to tune the hyperparameter in the acquisition function according to the search dynamics to determine which candidate solutions are to be evaluated using the expensive real objective functions. In addition, the sampling selection criterion switches between an angle based distance and an anglepenalized distance over the course of optimization to achieve a better balance between exploration and exploitation. The performance of the proposed algorithm is examined on a set of benchmark problems and an airfoil design optimization problem using a maximum of 300 real fitness evaluations. Our experimental results show that the proposed algorithm is competitive compared to four popular multiobjective evolutionary algorithms.

Efficient Bayesian Optimization for Uncertainty Reduction Over Perceived Optima Locations Bayesian optimization (BO) is concerned with efficient optimization using probabilistic methods. Predictive entropy search (PES) is a popular and successful BO strategy to find a point that maximizes the information gained about the optima location of an unknown function. Since the PES analytical form is intractable, it requires approximations and is computationally expensive. These approximations may degrade PES performance in terms of accuracy and efficiency. In this paper, we propose an alternative scheme  predictive variance reduction search (PVRS) xe2x80x93 to find a point that maximally reduces the uncertainty at the perceived optima locations. The optimization converges to the true optimum when the uncertainty at all perceived optima locations is vanished. Our novel modification is beneficial in two ways. First, PVRS can be computed in closedform, unlike the approximations made in PES. Second, PVRS is simple and easy to implement. As a result, the proposed PVRS gains huge speed up for scalable BO whilst showing favorable optimization efficiency. Furthermore, we extend our PVRS framework for batch setting where we select multiple experiments for parallel evaluations at each iteration. Empirically, we demonstrate the effectiveness of the PVRS on both benchmark functions and realworld applications in standard and batch BO settings.