@inproceedings{2315c5cb2c654607a1b3bc87e17009f9,
title = "A memetic cooperative co-evolution model for large scale continuous optimization",
abstract = "Cooperative co-evolution (CC) is a framework that can be used to {\textquoteleft}scale up{\textquoteright} EAs to solve high dimensional optimization problems. This approach employs a divide and conquer strategy, which decomposes a high dimensional problem into sub-components that are optimized separately. However, the traditional CC framework typically employs only one EA to solve all the sub-components, which may be ineffective. In this paper, we propose a new memetic cooperative co-evolution (MCC) framework which divides a high dimensional problem into several separable and non-separable sub-components based on the underlying structure of variable interactions. Then, different local search methods are employed to enhance the search of an EA to solve the separable and non-separable sub-components. The proposed MCC model was evaluated on two benchmark sets with 35 benchmark problems. The experimental results confirmed the effectiveness of our proposed model, when compared against two traditional CC algorithms and a state-of-the-art memetic algorithm.",
keywords = "Continuous optimization problem, Cooperative co-evolution, Large scale global optimization, Memetic algorithm",
author = "Yuan Sun and Michael Kirley and Halgamuge, \{Saman K.\}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 3rd Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2017 ; Conference date: 31-01-2017 Through 02-02-2017",
year = "2017",
doi = "10.1007/978-3-319-51691-2\_25",
language = "English",
isbn = "9783319516905",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "291--300",
editor = "Xiaodong Li and Markus Wagner and Tim Hendtlass",
booktitle = "Artificial Life and Computational Intelligence - 3rd Australasian Conference, ACALCI 2017, Proceedings",
address = "Germany",
}