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Denoising chaotic time series using an evolutionary state estimation approach
Published in Marcio Eisencraft, Romis Attux, Ricardo Suyama, Chaotic Signals in Digital Communications, 2018
Diogo C. Soriano, Murilo B. Loiola, Ricardo Suyama, Eisencraft Marcio, Vanessa B. Olivatto, João Marcos T. Romano, Romis Attux
The modus operandi of the CLONALG is based on two conceptual pillars: clonal selection and affinity maturation [6]. The clonal selection principle establishes that, when an organism is invaded by antigens (e.g., virus or bacteria), specific cells of the immune system recognize the exogenous element and are selected to proliferate, which gives rise to a cloning process with rates proportional to the affinity - defined by some measure of recognition - of these cells to the antigens. In the affinity maturation process, the individuals produced in the current generation can exhibit mutations with rates inversely proportional to their affinity with the antigens, and the mutated generation can eventually present individuals with higher affinity [6, 7].
Biologic Drug Substance and Drug Product Manufacture
Published in Anthony J. Hickey, Sandro R.P. da Rocha, Pharmaceutical Inhalation Aerosol Technology, 2019
Ajit S. Narang, Mary E. Krause, Shelly Pizarro, Joon Chong Yee
These selected cells, however, are polyclonal since this is a collection of cells that have integrated the transgene at different locations on their expression systems and have other differences, such as post-translational modification (PTM), transgene expression. Selection of single clones, called clonal selection, is then performed by growth and isolation of single cells upon dilution and amplification in the deficient growth medium.
Glossary of scientific and technical terms in bioengineering and biological engineering
Published in Megh R. Goyal, Scientific and Technical Terms in Bioengineering and Biological Engineering, 2018
Clonal selection is the production of a population of plasma cells all producing the same antibody in response to the interaction between a B lymphocyte producing that specific antibody and the antigen bound by that antibody.
Hybrid Cuckoo Search with Clonal Selection for Triclustering Gene Expression Data of Breast Cancer
Published in IETE Journal of Research, 2023
P. Swathypriyadharsini, K. Premalatha
The immune system is a group of cells, tissues, and organs that function together to defend the body against foreign invaders [21]. Clonal selection depicts how antibodies behave and what they can do in the acquired immune system [22]. Clonal selection algorithm is a form of artificial immune system inspired by the clonal selection theory, which describes how B and T lymphocytes enhance their response to antigens over time, a process known as affinity maturation [23]. This algorithm concentrates on the Darwinian theory, such as selection being influenced by antigen–antibody affinity, replication by cell division, and variation by somatic hypermutation [23]. The main features of Clonal Selection Algorithm are The number of clones, which is proportional to the antibody’s affinity with respect to the antigens;The rate of mutation, which is inversely proportional to affinity
Optimising workforce efficiency in healthcare during the COVID-19: a computational study of vehicle routeing method for homebound vaccination
Published in Production Planning & Control, 2022
Giustina Secundo, Francesco Nucci, Riad Shams, Francesco Albergo
Briefly, AIA is founded on the behaviour of the animal immune system that defends from external germs. The immune system is an adaptive pattern recognition mechanism that guards against foreign viruses and bacteria. The cells of the immune system, called antibodies, are casually spread throughout the human body. The immune system reacts to pathogens and expands the process of identifying and removing pathogens using two mechanisms: clonal selection and affinity maturation. When a disease agent strikes an organism, clonal selection produces a number of immune cells that detect and eliminate the pathogen. As cell reproduction proceeds, cells experience fast mutations, along with a selection procedure. Cells that have an excellent affinity for the pathogen propagate into memory cells.
Probabilistic assessment of transport network vulnerability with equilibrium flows
Published in International Journal of Sustainable Transportation, 2021
Yu Jiang, Yi Wang, W. Y. Szeto, Andy H. F. Chow, Anna Nagurney
It is well-known that bi-level programs are inherently non-convex and challenging to solve (Ban et al., 2006; Meng et al., 2001; Meng & Yang, 2002). Concerning the solution method, metaheuristics are gaining popularity in handling bi-level transportation optimization problems, owing to their insensitivity to the mathematical property of the problems. A number of metaheuristics or their hybrids have been extensively applied for tackling bi-level transportation optimization problems, including the Genetic Algorithm (GA) (e.g., Unnikrishnan & Lin, 2012), Ant Colony Optimization (e.g., Vitins & Axhausen, 2009), Chemical Reaction Optimization (e.g., Szeto et al., 2014), and Artificial Bee Colony (e.g., Jiang et al., 2013; Szeto & Jiang, 2012, 2014). This study adopts an evolutionary algorithm named the Clonal Selection Algorithm (CSA) to solve the proposed bi-level optimization problem. The CSA is inspired by Burnet’s clonal selection theory, which exploits the diversity and learning properties of the acquired immune system of vertebrates (Brownlee, 2007). It has been reported that the algorithm is capable of solving several benchmark problems in machine learning and optimization (De Castro & Von Zuben, 2000, 2002) and the algorithm performs better than other heuristics such as GA in some cases (Ulutas & Kulturel-Konak, 2011). In the field of transportation research, to the best of our knowledge, only Miandoabchi, Farahani, & Szeto, et al. (2012), Miandoabchi, Farahani, Dullaert, et al. (2012), and Miandoabchi et al. (2013) applied the algorithm to solve their studied bi-level transportation network design problems, which motivates us to investigate its capability for solving the proposed bi-level optimization problem.