Theoretical advances in artificial immune systems

Artificial immune systems (AIS) constitute a relatively new area of bio-inspired computing. Biological models of the natural immune system, in particular the theories of clonal selection, immune networks and negative selection, have provided the inspiration for AIS algorithms. Moreover, such algorithms have been successfully employed in a wide variety of different application areas. However, despite these practical successes, until recently there has been a dearth of theory to justify their use. In this paper, the existing theoretical work on AIS is reviewed. After the presentation of a simple example of each of the three main types of AIS algorithm (that is, clonal selection, immune network and negative selection algorithms respectively), details of the theoretical analysis for each of these types are given. Some of the future challenges in this area are also highlighted.

[1]  Peter Ross,et al.  Exploiting the analogy between immunology and sparse distributedmemories : a system for lustering non-stationary data , 2002 .

[2]  Riccardo Poli,et al.  Foundations of Genetic Programming , 1999, Springer Berlin Heidelberg.

[3]  H. Abbass,et al.  aiNet : An Artificial Immune Network for Data Analysis , 2022 .

[4]  Osamu Watanabe,et al.  A Probabilistic 3-SAT Algorithm Further Improved , 2002, STACS.

[5]  Jonathan Timmis,et al.  Artificial immune systems—today and tomorrow , 2007, Natural Computing.

[6]  Jonathan Timmis,et al.  Artificial immune systems - a new computational intelligence paradigm , 2002 .

[7]  Stephanie Forrest,et al.  Architecture for an Artificial Immune System , 2000, Evolutionary Computation.

[8]  John E. Hunt,et al.  Recognising Promoter Sequences Using An Artificial Immune System , 1995, ISMB.

[9]  Hussein A. Abbass,et al.  Data Mining: A Heuristic Approach , 2002 .

[10]  Lois Boggess,et al.  An investigation into the source of power for AIRS, an artificial immune classification system , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[11]  Francisco C. Santos,et al.  Tolerance vs Intolerance: How Affinity Defines Topology in an Idiotypic Network , 2006, ICARIS.

[12]  Alan S. Perelson,et al.  Theoretical Immunology, Part One , 1988 .

[13]  I. Cohen.  Tending Adam's Garden: Evolving the Cognitive Immune Self , 2004 .

[14]  D. Jordan,et al.  Nonlinear ordinary differential equations (2nd ed.) , 1987 .

[15]  Jon Timmis,et al.  Jisys: The Envelopment of an Artificial Immune System for Real World Applications , 1999 .

[16]  Pietro Simone Oliveto,et al.  On the Convergence of Immune Algorithms , 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.

[17]  Feller William,et al.  An Introduction To Probability Theory And Its Applications , 1950 .

[18]  Jonathan Timmis,et al.  Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[19]  Carlos A. Coello Coello,et al.  Multiobjective Optimization Using Ideas from the Clonal Selection Principle , 2003, GECCO.

[20]  Julie Greensmith.  New Frontiers For An Artificial Immune System , 2003 .

[21]  Peter Ross,et al.  Studies on the Implications of Shape-Space Models for Idiotypic Networks , 2004, ICARIS.

[22]  Simon M. Garrett,et al.  How Do We Evaluate Artificial Immune Systems? , 2005, Evolutionary Computation.

[23]  Alan S. Perelson,et al.  The immune system, adaptation, and machine learning , 1986 .

[24]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1967 .

[25]  John E. Hunt,et al.  Learning using an artificial immune system , 1996 .

[26]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[27]  XI FachbereichInformatik.  Finite Markov Chain Results in Evolutionary Computation: a Tour D'horizon , 1998 .

[28]  F. Burnet.  The clonal selection theory of acquired immunity , 1959 .

[29]  R. K. Shyamasundar,et al.  Introduction to algorithms , 1996 .

[30]  D. Dasgupta.  Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[31]  P. Delves,et al.  The Immune System , 2000 .

[32]  A. Perelson,et al.  Predicting the size of the T-cell receptor and antibody combining region from consideration of efficient self-nonself discrimination. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[33]  Emma Hart,et al.  Not All Balls Are Round: An Investigation of Alternative Recognition-Region Shapes , 2005, ICARIS.

[34]  Yoshiteru Ishida.  Immunity-Based Systems: A Design Perspective , 2010 .

[35]  G. Oster,et al.  Theoretical studies of clonal selection: minimal antibody repertoire size and reliability of self-non-self discrimination. , 1979, Journal of theoretical biology.

[36]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[37]  Michael D. Vose,et al.  Modeling genetic algorithms with Markov chains , 1992, Annals of Mathematics and Artificial Intelligence.

[38]  A. Perelson.  Immune Network Theory , 1989, Immunological reviews.

[39]  G. Grimmett,et al.  Probability and random processes , 2002 .

[40]  Luca Cardelli,et al.  A Correct Abstract Machine for the Stochastic Pi-calculus , 2004 .

[41]  Paul Helman,et al.  Negative representations of information , 2009, International Journal of Information Security.

[42]  C. Janeway,et al.  Innate immune recognition. , 2002, Annual review of immunology.

[43]  Carlos A. Coello Coello,et al.  Convergence Analysis of a Multiobjective Artificial Immune System Algorithm , 2004, ICARIS.

[44]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[45]  Emma Hart,et al.  Analysis of a Growth Model for Idiotypic Networks , 2006, ICARIS.

[46]  Yoshiteru Ishida,et al.  Active Diagnosis by Self-Organization: An Approach by The Immune Network Metaphor , 1997, IJCAI.

[47]  G. Weisbuch,et al.  Immunology for physicists , 1997 .

[48]  Hugues Bersini,et al.  Hints for Adaptive Problem Solving Gleaned from Immune Networks , 1990, PPSN.

[49]  F. von Zuben,et al.  An evolutionary immune network for data clustering , 2000, Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks.

[50]  J. Uspensky.  Introduction to mathematical probability , 1938 .

[51]  H. Waelbroeck,et al.  Discrete Chaos , 1996, chao-dyn/9610005.

[52]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[53]  Julie Greensmith,et al.  Two Ways to Grow Tissue for Artificial Immune Systems , 2005, ICARIS.

[54]  Jerne Nk.  Towards a network theory of the immune system. , 1974 .

[55]  Victor A. Skormin,et al.  Immunocomputing - principles and applications , 2003 .

[56]  E. Seneta.  Non-negative Matrices and Markov Chains , 2008 .

[57]  N. Packard,et al.  North-Holland, Amsterdam THE IMMUNE SYSTEM, ADAPTATION, AND MACHINE LEARNING , 2004 .

[58]  U. Schöning.  A probabilistic algorithm for k-SAT and constraint satisfaction problems , 1999, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039).

[59]  Jonathan Timmis,et al.  Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm , 2004, Genetic Programming and Evolvable Machines.

[60]  Walter Kern,et al.  An improved deterministic local search algorithm for 3-SAT , 2004, Theor. Comput. Sci..

[61]  Andrew N.W. Hone,et al.  Mathematical Analysis of Artificial Immune System Dynamics and Performance , 2007 .

[62]  Jonathan Timmis,et al.  A Markov Chain Model of the B-Cell Algorithm , 2005, ICARIS.

[63]  Alan S. Perelson,et al.  The Evolution of Emergent Organization in Immune System Gene Libraries , 1995, ICGA.

[64]  Dipankar Dasgupta,et al.  An Overview of Artificial Immune Systems and Their Applications , 1993 .

[65]  Lois Boggess,et al.  ARTIFICIAL IMMUNE SYSTEM CLASSIFICATION OF MULTIPLE- CLASS PROBLEMS , 2002 .

[66]  Jonathan Timmis,et al.  Immune Inspired Somatic Contiguous Hypermutation for Function Optimisation , 2003, GECCO.

[67]  Jonathan Timmis,et al.  Artificial Immune Systems : Using the Immune System as Inspiration for Data Mining , 2001 .

[68]  Michael D. Vose,et al.  Modeling Simple Genetic Algorithms , 1992, FOGA.

[69]  Yoshiteru Ishida.  An immune network model and its applications to process diagnosis , 1993, Systems and Computers in Japan.

[70]  Hugues Bersini,et al.  The Immune Learning Mechanisms: Recruitment Reinforcement and their applications , 1993 .

[71]  C. Janeway.  Immunobiology: The Immune System in Health and Disease , 1996 .

[72]  Hans B. Sieburg,et al.  The Cellular Device Machine Development System for Modeling Biology on the Computer , 1991, Complex Syst..

[73]  Jonathan Timmis,et al.  Application Areas of AIS: The Past, The Present and The Future , 2005, ICARIS.

[74]  D. Jordan,et al.  Nonlinear Ordinary Differential Equations: An Introduction for Scientists and Engineers , 1979 .

[75]  Andrew Hone,et al.  Conceptual Frameworks for Artificial Immune Systems , 2005, Int. J. Unconv. Comput..

[76]  Stephanie Forrest,et al.  Infect Recognize Destroy , 1996 .

[77]  Julie Greensmith,et al.  Articulation and Clarification of the Dendritic Cell Algorithm , 2006, ICARIS.

[78]  Valerie Isham,et al.  Non‐Negative Matrices and Markov Chains , 1983 .

[79]  M Cohn,et al.  The 'complete' idiotype network is an absurd immune system. , 1986, Immunology today.

[80]  Yoshiteru Ishida.  Fully distributed diagnosis by PDP learning algorithm: towards immune network PDP model , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[81]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[82]  J. A. Robinson,et al.  A Machine-Oriented Logic Based on the Resolution Principle , 1965, JACM.

[83]  Larry J. Eshelman,et al.  Proceedings of the 6th International Conference on Genetic Algorithms , 1995 .

[84]  R. Germain.  An innately interesting decade of research in immunology , 2004, Nature Medicine.

[85]  Joachim Niehren,et al.  Gene Regulation in the Pi Calculus: Simulating Cooperativity at the Lambda Switch , 2006, Trans. Comp. Sys. Biology.

[86]  François Robert.  Les systèmes dynamiques discrets , 1995 .

[87]  Yoshiteru Ishida,et al.  Immunity-Based Systems , 2004, Advanced Information Processing.

[88]  Alex Alves Freitas,et al.  Revisiting the Foundations of Artificial Immune Systems for Data Mining , 2007, IEEE Transactions on Evolutionary Computation.

[89]  Mark Neal,et al.  An artificial immune system for continuous analysis of time-varying data , 2002 .

[90]  Martin Thorsen Ranang.  An Artificial Immune System Approach to Preserving Security in Computer Networks , 2002 .

[91]  Andrew Watkins,et al.  Exploiting immunological metaphors in the development of serial, parallel and distributed learning algorithms , 2005 .

[92]  Jonathan Timmis.  Artificial immune systems : a novel data analysis technique inspired by the immune network theory , 2000 .