The notion of experience has often been neglected within the domain of evolutionary computation while in machine learning a large variety of methods has emerged in the recent years under the umbrella of transfer learning. Notably, realizing experience-based methods suffers from a variety of conceptual key problems. The first one being in regards to what constitutes problem-similarity from an algorithm perspective and the second one being what constitutes the transferable experience by itself. Ideally, one would envision that a learning optimization algorithm could be expected to act similarly to a human-problem solver who tackles novel tasks initially without any preconceptions. Experience only comes into play until sufficient similarity to known problems is established. Our paper therefore has two aims. First, to outline existing related fields and methodologies and highlight their insufficiencies. Second, to make the case for experience-based optimization by a demonstration using a novel and statistics-based approach with a real-coded genetic algorithm as a case study. In this paper we do not claim to construct universal problem solvers, but instead propose that from an algorithm-specific-view, problem characteristics can be learned and harnessed to improve future performance of similarly-structured optimization tasks. © 2019 IEEE.
|Title of host publication
|2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - Dec 2019
Bibliographical noteThis research has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 766186. It was also supported by the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X386), Shenzhen Peacock Plan (Grant No. KQTD2016112514355531), and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008).
- evolutionary computation
- knowledge transfer
- machine learning.
- statistical learning
- stochastic optimization