dissertation · 2012
Self-configurable memetic algorithm
Cited by 0Open access
Abstract
To date, most successful advanced stochastic optimization algorithms involve some forms of individual learning or meme in their design. Memetic Algorithm (MA), as a form of hybridization between population-based and individual-based searches, represents one of the recent growing areas in evolutionary algorithm research. In the success and surge in interests on MAs, researchers have been exploring on various possible hybridizations of search operators towards the development and manual crafting of specialized algorithms that solve a specific problem or a set of problems effectively, using the domain knowledge obtained from human expertise. However, with so many population-based and individual-based procedures available for hybridizing, it is a tedious task, if not impossible, to design in advance an effective memetic algorithm for a given problem at hand. Furthermore, when high-fidelity analysis codes are used I would like to express my deepest gratitude to my academic supervisor in Nanyang Technological University (NTU), Dr. Ong Yew Soon.
Cite this paper
Le, M. N. (2012). Self-configurable memetic algorithm. https://doi.org/10.32657/10356/49033
Le, Minh Nghia. “Self-configurable memetic algorithm.” 2012, https://doi.org/10.32657/10356/49033.
Le, Minh Nghia. 2012. “Self-configurable memetic algorithm.” https://doi.org/10.32657/10356/49033.
Le, M.N. (2012) ‘Self-configurable memetic algorithm’. Available at: https://doi.org/10.32657/10356/49033.
@phdthesis{le2012self,
title = {{Self-configurable memetic algorithm}},
author = {Le, Minh Nghia},
year = {2012},
doi = {10.32657/10356/49033}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.