Conceptual and Practical Aspects of the aiNet Family of Algorithms

In this paper, a review of the conceptual and practical aspects of the aiNet (Artificial Immune Network) family of algorithms will be provided. This family of algorithms started with the aiNet algorithm, proposed in 2002 for data clustering and, since then, several variations have been developed for data clustering, biclustering and optimization in general. Although the algorithms will be positioned with respect to other pertinent approaches from the literature, the emphasis of this paper will be on the formalization and critical analysis of the set of contributions produced along almost one decade of research in this specific theme, together with the provision of insights for further extensions. DOI: 10.4018/jncr.2010010101 2 International Journal of Natural Computing Research, 1(1), 1-35, January-March 2010 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. But only in the mid 90s the AIS paradigm gained the status of an active research area, particularly with the publishing of the application of Self-Nonself Discrimination principles to the computer protection problem (Forrest et al., 1994), and also of the Negative Selection algorithm (Dasgupta, 1998). In late 90s, the work of de Castro and Von Zuben (1999) introduced the concepts and applications of the Clonal Selection principle (Burnet, 1978) to artificial immune systems, not only dealing with machine learning problems but also adapting the immune-inspired mechanisms to perform discrete and continuous optimization. In the trace of this achievement, the popular CLONALG algorithm (de Castro & Von Zuben, 2002a) – Clonal Selection Algorithm – was proposed, and followed by several applications and extensions. The addition of network-based aspects inspired from Jerne’s Immune Network Theory (Jerne, 1974) to the CLONALG algorithm led to the Artificial Immune Network algorithm, or simply aiNet (de Castro & Von Zuben, 2002b), that not only extended the capabilities of CLONALG but also introduced very interesting aspects such as the network interaction among solutions and the dynamic adaptation of the size of the set of candidate solutions. The aiNet algorithm was originally developed for clustering problems, and it is capable of automatically identifying a proper number of arbitrarily shaped clusters. Following the successful results obtained by the original aiNet algorithm, many extensions appeared in the literature, not only for clustering problems but also for continuous optimization (de Castro & Timmis, 2002b), combinatorial problems (de Sousa et al., 2004; Gomes et al., 2004), bioinformatics (Bezerra & de Castro, 2003; de Sousa et al., 2004; Gomes et al., 2004), prediction systems (Li et al., 2010), biclustering (Castro et al., 2007a,b,c; Coelho et al., 2008; de França et al., 2006b), multi-objective optimization (Coelho & Von Zuben, 2006a; Coelho et al., 2008), dynamic optimization problems (de França et al., 2005a; de França et al., 2006a; Junqueira et al., 2005; Junqueira et al., 2006), and many others. With almost ten years of existence and supporting a very active line of immune-inspired algorithms, it has become hard to keep track of all existent variations of the aiNet algorithm and their potential applications. Therefore, the goal of this paper is to critically review the original aiNet algorithm, describing the conceptual aspects associated with it, and to present and discuss its main extensions together with the results obtained so far. The paper is organized as follows: Section 2 presents the theoretical aspects of natural and artificial immune systems that were applied on the aiNet context; Section 3 is dedicated to draw a parallel between the clustering problem and the immune concepts presented in Section 2, and to explain and discuss the original aiNet algorithm; in Section 4 an overview of the aiNet family of algorithms is presented, together with a discussion of general aspects of the different algorithms and the problems that they are meant to solve; Section 5 is dedicated to optimization algorithms, where the variations of aiNet developed for optimization of continuous, discrete, multimodal, multi-objective and dynamic– environment problems will be detailed; Section 6 discusses and details the extensions of aiNet for biclustering; and, finally, Section 7 concludes this survey summarizing the most relevant issues, presenting the final remarks and discussing some prospects concerning the future of the aiNet family of algorithms. 2. NaTural aNd arTIFICIal IMMuNE SySTEMS As the name of the algorithms in the aiNet family suggests, they are inspired by the natural immune system of vertebrates, more specifically, by the mechanisms associated with B-cells in the adaptive immune response (de Castro & Von Zuben, 2002b). These immuneinspired algorithms are based on a paradigm known as Artificial Immune Systems (AIS), originated from attempts to model and apply 33 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/conceptual-practical-aspectsainet-family/41942?camid=4v1 This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Medicine, Healthcare, and Life Science. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=2