Non-linear Domain Adaptation in Transfer Evolutionary Optimization


The cognitive ability to learn with experience is a hallmark of intelligent systems. The emerging transfer optimization paradigm pursues such human-like problem-solving prowess by leveraging useful information from various source tasks to enhance optimization efficiency on a related target task. The occurrence of harmful negative transfer is a key concern in this setting, paving the way for recent probabilistic model-based transfer evolutionary algorithms that curb this phenomenon. However, in applications where the source and target domains, i.e., the features of their respective search spaces (e.g., dimensionality) and the distribution of good solutions in those spaces, do not match, narrow focus on curbing negative effects can lead to the conservative cancellation of knowledge transfer. Taking this cue, this paper presents a novel perspective on domain adaptation in the context of evolutionary optimization, inducing positive transfers even in scenarios of source-target domain mismatch. Our first contribution is to establish a probabilistic formulation of domain adaptation, by which source and/or target tasks can be mapped to a common solution representation space in which their discrepancy is reduced. Secondly, a domain adaptive transfer evolutionary algorithm is proposed, supporting both offline construction and online data-driven learning of non-linear mapping functions. The performance of the algorithm is experimentally verified, demonstrating superior convergence rate in comparison to state-of-the-art baselines on synthetic benchmarks and a practical case study in multi-location inventory planning. Our results thus shed light on a new research direction for optimization algorithms that improve their efficacy by learning from heterogeneous experiential priors.

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Source data comes from the multimodal Griewank function

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This work was funded by the Agency for Science, Technology and Research (A*STAR) of Singapore, under the Singapore Institute of Manufacturing Technology-Nayang Technological University Collaborative Research Programme in Complex Systems. This work was also supported in part by the A*STAR Cyber-Physical Production System (CPPS) – Towards Contextual and Intelligent Response Research Program, under the RIE2020 IAF-PP Grant A19C1a0018. Yew-Soon Ong acknowledges the support by Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU) that is funded by the Singapore Government through the Industry Alignment Fund ‐ Industry Collaboration Projects Grant.

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Lim, R., Gupta, A., Ong, YS. et al. Non-linear Domain Adaptation in Transfer Evolutionary Optimization. Cogn Comput (2021).

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  • Domain adaptation
  • Solution representation learning
  • Transfer evolutionary optimization
  • Memetic computation