The Clonal Selection Algorithm with Engineering Applications 1

By Leandro Nunes de Castro, F. J. Zuben. 2000

777 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/04036a41b2f514cba7b5638bd23f2a19e56fcba7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm (2004, background): “With the exception of [7], until very recently Artificial Immune System (AIS) research into machine learning has focused on the development of unsupervised learning and clustering [ 10 ], [29] rather than supervised learning and reinforcement learning1.”
Application Areas of AIS: The Past, The Present and The Future (2005, background): “The majority of these publications are based on the application of the clonal selection principle, resulting in a number of algorithms such as Clonalg algorithm [ 25 ], opt-AINET [26] and the B-Cell algorithm [75] and opt-IA [19].”
Distributed Intrusion Detection System in a Multi-Layer Network Architecture of Smart Grids (2011, unknown): “For instance, de Castro proposed the clonal selection algorithm (CSA) algorithm in [ 33 ], transformed it into the CLONALG algorithm in [34], the clonal selection classification algorithm (CSCA) was reported in [35], and in [36] a dynamic clonal selection algorithm with the property of self-adaptati”
Advances in artificial immune systems (2006, unknown): “CLONAL BINARY AND INTEGER PATTERN MATCHING, [ 12 ] SELECTION AND AFFINITY SELECTION.”
Artificial immune systems as a novel soft computing paradigm (2003, background): “The problem of model selection in ANN can be dealt with several approaches, among which constructive (Kwok and Yeung, 1997; Fritzke, 1994) and pruning strategies (Reed, 1993; de Castro and Von Zuben, 1999 ) are the most common.”