Towards Lifelong Learning in Optimisation Algorithms

Abstract

Standard approaches to developing optimisation algorithms tend to involve selecting an algorithm and tuning it to work well on a large set of problem instances from the domain of interest. Once deployed, the algorithm remains static, failing to improve despite being exposed to a wealth of further example instances. Furthermore, if the characteristics of the instances being solved shift over time, the tuned algorithm is likely to perform poorly. To counter this, we propose the lifelong learning optimiser, which autonomously and continually refines its optimisation algorithm(s) to improve with experience, and generates novel algorithms if performance drops. The approach combines genetic programming with an autonomous management method inspired by the operation of the natural immune system.