Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148168
Title: Memes as building blocks : a case study on evolutionary optimization + transfer learning for routing problems
Authors: Feng, Liang
Ong, Yew-Soon
Tan, Ah-Hwee
Tsang, Ivor W.
Keywords: Engineering::Computer science and engineering
Issue Date: 2015
Source: Feng, L., Ong, Y., Tan, A. & Tsang, I. W. (2015). Memes as building blocks : a case study on evolutionary optimization + transfer learning for routing problems. Memetic Computing, 7(3), 159-180. https://dx.doi.org/10.1007/s12293-015-0166-x
Journal: Memetic Computing
Abstract: A significantly under-explored area of evolutionary optimization in the literature is the study of optimization methodologies that can evolve along with the problems solved. Particularly, present evolutionary optimization approaches generally start their search from scratch or the ground-zero state of knowledge, independent of how similar the given new problem of interest is to those optimized previously. There has thus been the apparent lack of automated knowledge transfers and reuse across problems. Taking this cue, this paper presents a Memetic Computational Paradigm based on Evolutionary Optimization$$+$$+Transfer Learning for search, one that models how human solves problems, and embarks on a study towards intelligent evolutionary optimization of problems through the transfers of structured knowledge in the form of memes as building blocks learned from previous problem-solving experiences, to enhance future evolutionary searches. The proposed approach is composed of four culture-inspired operators, namely, Learning, Selection, Variation and Imitation. The role of the learning operator is to mine for latent knowledge buried in past experiences of problem-solving. The learning task is modelled as a mapping between past problem instances solved and the respective optimized solution by maximizing their statistical dependence. The selection operator serves to identify the high quality knowledge that shall replicate and transmit to future search, while the variation operator injects new innovations into the learned knowledge. The imitation operator, on the other hand, models the assimilation of innovated knowledge into the search. Studies on two separate established NP-hard problem domains and a realistic package collection/deliver problem are conducted to assess and validate the benefits of the proposed new memetic computation paradigm.
URI: https://hdl.handle.net/10356/148168
ISSN: 1865-9284
DOI: 10.1007/s12293-015-0166-x
Rights: © 2015 Springer-Verlag Berlin Heidelberg. This is a post-peer-review, pre-copyedit version of an article published in Memetic Computing. The final authenticated version is available online at: http://dx.doi.org/10.1007/s12293-015-0166-x.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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