Optima, Extrema, and Artificial Immune Systems

Hone, Andrew N. W.; Kelsey, J.

doi:10.1007/978-3-540-30220-9_7

Cited by 11 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of convergence proofs, one paper, (Villalobos-Arias et al 2004) presents a complete proof for a specific multiobjective clonal selection algorithm using Markov chains (Grimmett and Stirzaker 1982). As pointed out by Hone and Kelsey (Hone and Kelsey 2004) a useful and valued avenue to explore would be into the dynamics of immune algorithms based on nonlinear dynamical systems inspired by biological models (Farmer et al 1986), and stochastic differential equations. Given the use of clonal selection based algorithms within AIS, a great deal could be gained by the community with further theoretical investigations such as, the role of mutation operators, which could be used to provide information for optimal mutation rates for specific functions.…”

Section: Theoretical Perspectivementioning

confidence: 99%

A Survey of artificial immune applications

2010

View full text Add to dashboard Cite

The artificial immune system (AIS) community has been vibrant and active for a number of years now, producing a prolific amount of research ranging from modeling the natural immune system, to tackling real world applications, using an equally diverse set of immune inspired algorithms. We review the current immune applications of the AIS approach, and propose a number of suggestions to the AIS community that can be undertaken to help move the area forward. Despite many successes of AIS techniques, there remain some open issues which have to be addressed in order to make the AIS a real-world problem solving technique.

show abstract

Section: Theoretical Perspectivementioning

confidence: 99%

A Survey of artificial immune applications

2010

View full text Add to dashboard Cite

show abstract

“…The B-Cell Algorithm (BCA) [22,23,29,19] was applied to optimization problems, the Clonal Selection Algorithm (CLONALG) [15,16] is the most popular algorithm based on the clonal selection theory. Different clonal selection algorithms are described next.…”

Section: Issn: 1988-3064(on-line) C Aepia and The Authors 3 Previous mentioning

confidence: 99%

Artificial Immune System for Solving Global Optimization Problems

Aragón¹,

Esquivel²,

Coello³

2010

Int. Artif.

View full text Add to dashboard Cite

In this paper, we present a novel model of an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed model is used for global optimization problems. The model operates on four populations: Virgins, Effectors (CD4 and CD8) and Memory. Each of them has a different role, representation and procedures. We validate our proposed approach with a set of test functions taken from the specialized literature, we also compare our results with the results obtained by different bio-inspired approaches and we statistically analyze the results gotten by our approach.

show abstract

“…In terms of convergence proofs, one paper, [38], presents a complete proof for a specific multi-objective clonal selection algorithm using markov chains [39]. As pointed out by Hone and Kelsey [40] a useful and valued avenue to explore would be into the dynamics of immune algorithms based on nonlinear dynamical systems inspired by biological models [41], and stochastic differential equations [42]. Given the use of clonal selection based algorithms within AIS, a great deal could be gained by the community with further theoretical investigations such as, the role of mutation operators, which could be used to provide information for optimal mutation rates for specific functions.…”

Section: Theoretical Perspectivementioning

confidence: 99%

Neural Nets

2006

Lecture Notes in Computer Science

View full text Add to dashboard Cite

PrefaceThis volume reports the proceedings of the 16th Italian Workshop on Neural Nets WIRN 2005 and the satellite International Workshop on Natural and Artificial Immune Systems NAIS 2005. The workshops, held in Vietri sul Mare (SA) from 8 to 11 June, were jointly organized by the International Institute for Advanced Scientific Studies "Eduardo R. Caianiello" (IIASS) and the Società Italiana Reti Neuroniche (SIREN). The new format of the SIREN annual meeting-WIRN plus satellite conference-stems from the aim of sharing neural network methodologies with close research communities. The goal this year was to recognize and exploit a commonality of subsymbolic methods used both in neural networks and immune system research to study complex systems with a distinguishing common feature. All these systems are indeed constituted by a mass of elementary organisms/computational units generating ensemble functionalities, such as optimization and learning, denoting traits of intelligent thought. Moreover, like neural networks, the area of natural and artificial immune systems is also developing in response to needs for a mix of inter-and multidisciplinary expertise, spanning immunology, biology, mathematics, computer science, and medicine, among others, having rebounding of models and results from biological to computational realms and vice versa as a great strength point.The volume contains invited review papers and selected original contributions presented in oral or poster sessions by both Italian and foreign researchers. The contributions have been assembled, for reading convenience, into eight sections. The first section reports the lecture given by the winner of the Premio Caianiello award. Sections II to V contain wide-spectrum papers on neural networks, split in four parts; models, architectures and algorithms, signal processing, and pattern recognition, respectively. The sixth section focuses on methodologies and applications of fuzzy and neurofuzzy computing promoted by a joint Spanish-Italian working group. The last two sections gather papers on immune systems divided into natural systems and artificial systems.The editors would like to thank the invited speakers and all the contributors whose highly qualified papers helped the success of the workshops. Finally, special thanks go to the referees for their accurate work.

show abstract

Optima, Extrema, and Artificial Immune Systems

Cited by 11 publications

References 13 publications

A Survey of artificial immune applications

A Survey of artificial immune applications

Artificial Immune System for Solving Global Optimization Problems

Neural Nets

Contact Info

Product

Resources

About