Data-driven evolutionary multi-task optimization for problems with complex solution spaces.

Smoothing rugged and rough fitness landscapes by machine learning (ML) models is a feasible approach to improve the global optimization performance of evolutionary algorithms (EA). However, systematic studies on the smoothing performance of ML models are rare. Moreover, existing model management methods have a relatively low efficiency and can cause the error propagation problem. In this paper, we empirically evaluate and compare the performance of common ML models in smoothing high-dimensional fitness landscapes. Based on the experimental results, we design a novel framework called data-driven multi-task optimization (DDMTO) to enhance EAs' search abilities in complex solution spaces with ML smoothing models. The proposed framework firstly smooths the fitness landscape by a ML model. Then, it models the original fitness landscape optimization and the smoothed fitness landscape optimization as a two-task optimization problem and solves them with an evolutionary multi-task optimizer (EMTO). Coordinated by EMTO, the easy task can help optimize the difficult task through the proposed knowledge transfer operator, and the error propagation problem can be solved by the proposed knowledge transfer control scheme. To prove the effectiveness of the proposed methodology, we design several example algorithms by embedding different EAs and ML models into the DDMTO framework and compare their optimization performance on high-dimensional continuous benchmark functions with rugged and rough fitness landscapes and a real-world optimization problem. Experimental results show that, by embedding an EA into the DDMTO framework with an appropriate smoothing model, its exploration ability and global optimization performance in complex solution spaces can be significantly enhanced without increasing the total computational cost. [ABSTRACT FROM AUTHOR]