@misc { , title = {Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn}, 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 (1) autonomously and continually refines its optimisation algorithm(s) so that they improve with experience and (2) generates novel algorithms if performance drops in reaction to unforeseen data.}, doi = {10.1007/978-3-030-79092-9\_9}, isbn = {978-3-030-79091-2}, pages = {187-203}, publicationstatus = {Published}, publisher = {Springer}, url = {http://researchrepository.napier.ac.uk/Output/2846706}, keyword = {Optimisation and learning, Centre for Algorithms, Visualisation and Evolving Systems, AI and Technologies, Lifelong learning, Optimisation, Artificial immune systems}, year = {2024}, author = {Hart, Emma} editor = {Smith, Alice E.} }