2 research outputs found
Transfer learning for gaussian process assisted evolutionary bi-objective optimization for objectives with different evaluation times
Wang X, Jin Y, Schmitt S, Olhofer M, Coello Coello CA. Transfer learning for gaussian process assisted evolutionary bi-objective optimization for objectives with different evaluation times. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO '20). New York, NY: ACM; 2020: 587-594.Despite the success of evolutionary algorithms (EAs) for solving multi-objective problems, most of them are based on the assumption that all objectives can be evaluated within the same period of time. However, in many real-world applications, such an assumption is unrealistic since different objectives must be evaluated using different computer simulations or physical experiments with various time complexities. To address this issue, a surrogate assisted evolutionary algorithm along with a parameter-based transfer learning (T-SAEA) is proposed in this work. While the surrogate for the cheap objective can be updated on sufficient training data, the surrogate for the expensive one is updated by either the training data set or a transfer learning approach. To find out the transferable knowledge, a filter-based feature selection algorithm is used to capture the pivotal features of each objective, and then use the common important features as a carrier for knowledge transfer between the cheap and expensive objectives. Then, the corresponding parameters in the surrogate models are adaptively shared to enhance the quality of the surrogate models. The experimental results demonstrate that the proposed algorithm outperforms the compared algorithms on the bi-objective optimization problems whose objectives have a large difference in computational complexities
Higher-order Knowledge Transfer for Dynamic Community Detection with Great Changes
Network structure evolves with time in the real world, and the discovery of
changing communities in dynamic networks is an important research topic that
poses challenging tasks. Most existing methods assume that no significant
change in the network occurs; namely, the difference between adjacent snapshots
is slight. However, great change exists in the real world usually. The great
change in the network will result in the community detection algorithms are
difficulty obtaining valuable information from the previous snapshot, leading
to negative transfer for the next time steps. This paper focuses on dynamic
community detection with substantial changes by integrating higher-order
knowledge from the previous snapshots to aid the subsequent snapshots.
Moreover, to improve search efficiency, a higher-order knowledge transfer
strategy is designed to determine first-order and higher-order knowledge by
detecting the similarity of the adjacency matrix of snapshots. In this way, our
proposal can better keep the advantages of previous community detection results
and transfer them to the next task. We conduct the experiments on four
real-world networks, including the networks with great or minor changes.
Experimental results in the low-similarity datasets demonstrate that
higher-order knowledge is more valuable than first-order knowledge when the
network changes significantly and keeps the advantage even if handling the
high-similarity datasets. Our proposal can also guide other dynamic
optimization problems with great changes.Comment: Submitted to IEEE TEV