Large-Scale Evolutionary Optimization via Multi-Task Random Grouping

Abstract

Evolutionary Algorithms (EA) are known to suffer from the curse of dimensionality resulting in poor performances when handling large-scale problems. Cooperative coevolution aims to overcome these issues in a divide and conquer approach by decomposing the original problem into several lower-dimensional sub-problems. For each sub-problem a chosen EA is applied for a defined number of function evaluations. This is repeated in a round robin like fashion until a terminating condition is met. A recently proposed area in the evolutionary computation field is the evolutionary multitask optimization (EMTO) framework. By jointly optimising several tasks, EMTO aims to exploit beneficial information across multiple tasks to improve the performance compared to optimising each task in isolation. In this paper, we consider a large-scale problem as a multi-task optimization problem by considering each sub-problem as an independent task. Applying an EMTO algorithm, knowledge transfer across sub-problems is carried out explicitly to improve the optimization of each sub-problem. We evaluate the effectiveness of our proposed algorithms empirically on a suite of separable and non-separable benchmark problems of varying dimensions.