## 1 Introduction  
Portfolio optimization is the art of asset allocation to achieve the purpose of maximizing investment returns while matching investors' risk tolerance. As an essential part of practical portfolio management, it has become an attracting issue in both academia and industry during the past few decades. In 1952, [PERSON] proposed the Mean-Variance (MV) portfolio optimization model [1], which is regarded as the cornerstone of modern portfolio theory (MPT). Although the MV model has drawn much attention since its introduction, several obvious shortcomings are exposed gradually with the application of the model. The MV model describes the return and the risk of the portfolio by the mean and the variance of the asset returns, respectively [2; 3], which means that the forecasting of asset returns will strongly influence portfolio performance. In practice, such forecasts are mainly based on their historical behavior, which may lead to considerable estimation errors. To address this problem, an enormous amount of attempts have been made. The Black-Litterman (BL) model [4; 5] is one of these attempts, which takes views on certain assets into consideration based on a Bayesian approach. Specifically, experts or investors give their subjective opinions on certain assets, and the BL model integrates them with the historical return data to create the future asset returns. Compared with the MV model, it is more stable in the process of forecasting asset returns and covariances, which helps to extend profit advantages and avoid risks, especially in volatile markets [6]. In the past decades, many researchers devoted themselves to the extension of BL model [7; 8; 9; 10; 11], in which novel methods to generate views is one of the most promising directions. With the development of artificial intelligence techniques, machine learning algorithms began to be employed for views generating [12; 13; 14; 15] to replace the views given directly through expert experience. However, there is almost no study that places emphasis on the uncertainty introduced by the relevant predictive model itself. BL portfolio construction is on the basis of the classic return-risk optimization, which is a typical multi-objective optimization problem (MOP). For these kinds of problems, evolutionary multi-objective optimization (EMO) algorithms such as MOEA/D [16] and NSGA-II [17] have fully demonstrated their niche [18]. However, EMO algorithms usually contain random operations in the process of the population initialization and evolution to maintain diversity, which leads to non-reproducibility, and lack a theoretical guarantee of global convergence. From the perspective of practice, multi-objective optimization is expected to be deterministic and theoretically convergent. Inspired by Lipschitzian global optimization [19; 20], [PERSON] et al. [21] introduced the DIviding RECTangles (DIRECT) algorithm, which does not require the Lipshitz constant and is more tractable in higher dimensions [22]. Due to the advantages of being easy to implement and having fewer hyperparameters (only one), some researchers explored extensions of DIRECT to make it possible to handle MOPs. However, most of them usually have a high computational complexity. Therefore, some algorithmic changes to the existing algorithms are somehow more practical. This paper attempts to address a BL portfolio optimization problem in which particular attention is paid to the interesting issue of how to generate investor preferences systematically and precisely. With this goal, a novel random forest-based BL model is developed, and an approach to handle the model mentioned above in a short time, while maintaining high accuracy, is also proposed. The main contribution of this paper could be summarized as follows:  
* We develop a new random forest-based BL portfolio optimization model in which a novel method for generating investor views on the basis of random forests is adopted.