1. Artificial Immune Systems Razieh Khamseh-Ashari Department of Electrical and Computer Eng Isfahan University of Technology Supervisor: Dr. Abdolreza Mirzaei

2. Introduction to the Immune System Artificial Immune Systems A Framework to Design Artificial Immune Systems (AIS) Representation Schemes Affinity Measures Immune Algorithms Outline 2 Artificial Immune Systems

3. Introduction to the Immune System Artificial Immune Systems A Framework to Design Artificial Immune Systems (AIS) Representation Schemes Affinity Measures Immune Algorithms Outline 3 Artificial Immune Systems

4. What is the Immune System ? a complex system of cellular and molecular components having the primary function of distinguishing self from not self and defense against foreign organisms or substances (Dorland's Illustrated Medical Dictionary) The immune system is a cognitive system whose primary role is to provide body maintenance (Cohen) Immune system was evolutionary selected as a consequence of its first and primordial function to provide an ideal inter-cellular communication pathway (Stewart) 4 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

5. Artificial Immune Systems 5 1. Immune System2. AIS Concepts 3. Designing a Framework

6. Classical Immunity The purpose of the immune system is defence Innate and acquired immunity Innate is the first line of defense. Germ line encoded (passed from parents) and is quite ‘static’ (but not totally static) Adaptive (acquired). Somatic (cellular) and is acquired by the host over the life time. Very dynamic. These two interact and affect each other 6 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

7. Multiple layers of the immune system 7 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

8. Innate Immunity May take days to remove an infection, if it fails, then the adaptive response may take over Macrophages and neurophils are actors Other actors such as TLR’s and dendritic cells (next lecture) are essential for recognition 8 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

9. Adaptive Immune System 9 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

10. Lymphocytes Carry antigen receptors that are specific They are produced in the bone marrow through B and T Cells are the main actors of the adaptive immune system 10 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

11. B Cell Pattern Recognition B cells have receptors called antibodies The immune recognition is based on the complementarity between the binding region of the receptor and a portion of the antigen called the epitope. Recognition is not just by a single antibody, but a collection of them Learn not through a single agent, but multiple ones 11 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

12. Processes within the Immune System (very basically) Negative Selection Censoring of T-cells in the thymus gland of T-cells that recognise self Defining normal system behavior Clonal Selection Proliferation and differentiation of cells when they have recognised something Generalise and learn Self vs Non-Self 12 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

13. Clonal Selection 13 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

14. Clonal Selection 14 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

15. Affinity Maturation Responses mediated by T cells improve with experience Mutation on receptors (hypermutation and receptor editing) During the clonal expansion, mutation can lead to increased affinity, these new ones are selected to enter a ‘pool’ of memory cells Can also lead to bad ones and these are deleted 15 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

16. Immune Responses 16 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

17. Summary Innate and adaptive immunity Focused on adaptive here Lymphocytes Negative selection Clonal selection Immune memory and learning 17 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

18. Further Immunology and Modelling 1. Immune System2. AIS Concepts 3. Designing a Framework

19. What is the Immune System ? The are many different viewpoints These views are not mutually exclusive Lots of common ingredients classical networkdanger 19 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

20. Problems with the classical view What happens if self changes? What about things that are “not harmful” The Danger model was proposed 20 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

21. Danger theory (Matzinger 1994) it is not “non-self”, but “danger” that the IS recognises dangerous invaders cause cell death or stress these cells generate “danger signal” molecules unlike natural cell death these stimulate an immune response local to the danger to identify the “culprit” 21 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

22. Immune Network Theory Idiotypic network (Jerne, 1974) B cells co-stimulate each other Treat each other a bit like antigens Creates an immunological memory 22 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

23. Introduction to the Immune System Artificial Immune Systems Remarkable Immune Properties Concepts, Scope and Applications Brief History of AIS A Framework to Design Artificial Immune Systems (AIS) Representation Schemes Affinity Measures Immune Algorithms Outline 23 Artificial Immune Systems

24. Artificial Immune Systems: A Definition AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models, which are applied to complex problem domains [De Castro and Timmis,2002] 24 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

25. Concepts Specificity Diversity Clonal selection Affinity maturation Immunity memory Positive and negative selection Distributing ability Multi-layering Self-organization Anomaly detection 25 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

26. Scope of AIS Pattern recognition Fault and anomaly detection Data analysis (classification, clustering, etc.) Agent-based systems Search and optimization Machine-learning Autonomous navigation and control Artificial life Security of information systems 26 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

27. Scope of AIS Clustering/classification Anomaly detection Computer security Optimisation Learning Bioinformatics Image proc. RoboticsControl Web mining 0 10 20 27 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

28. The Early Days: Developed from the field of theoretical immunology in the mid 1980’s. 1990 – Bersini first use of immune algorithms to solve problems Forrest et al – Computer Security mid 1990’s Work by IBM on virus detection Hunt et al, mid 1990’s – Machine learning 28 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

29. Introduction to the Immune System Artificial Immune Systems Remarkable Immune Properties Concepts, Scope and Applications Brief History of AIS A Framework to Design Artificial Immune Systems (AIS) Representation Schemes Affinity Measures Immune Algorithms Outline 29 Artificial Immune Systems

30. A Framework for AIS Algorithms Affinity Representation Application Solution AIS Shape-Space Binary Integer Real-valued Symbolic [De Castro and Timmis, 2002] 30 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

31. Shape-Space An antibody can recognise any antigen whose complement lies within a small surrounding region of width (the cross-reactivity threshold) This results in a volume v known as the recognition region of the antibody v V S v v [Perelson,1989] 31 Shape space (or solution space) Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

32. Choice of Representation Assume the general case: Ab =  Ab 1, Ab 2,..., Ab L  Ag =  Ag 1, Ag 2,..., Ag L  Binary representation Matching by bits Continuous (numeric) Real or Integer, typically Euclidian Categorical (nominal) E.g female or male of the attribute Gender. 32 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

33. Representation – Shape Space Used for modeling antibody and antigen Determine a measure to calculate affinity Hamming shape space(binary) if Ab i != Ag i : 0 otherwise (XOR operator) Antigen Antibody 33 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

34. A Framework for AIS Algorithms Affinity Representation Application Solution AIS Euclidean Manhattan Hamming 34 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

35. Affinities: related to distance/similarity Examples of affinity measures Euclidean Manhattan Hamming Affinity 35 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

36. Affinities in Hamming Shape-Space (a) Hamming distance (b) r-contiguous bits rule 36 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

37. Mutation - Binary Single point mutation Multi-point mutation 37 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

38. Affinity Proportional Mutation Affinity maturation is controlled Proportional to antigenic affinity  (D*) = exp(-  D*)  =mutation rate D*= affinity  =control parameter 38 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

39. A Framework for AIS Algorithms Affinity Representation Application Solution AIS Bone Marrow Models Clonal Selection Negative Selection Positive Selection Immune Network Models 39 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

40. The Algorithms Layer Bone Marrow Models Clonal Selection CLONALG AIRS Negative Selection Positive Selection Network Models AINE aiNET 40 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

41. The Algorithms Layer Bone Marrow Models Clonal Selection CLONALG AIRS Negative Selection Positive Selection Network Models AINE aiNET 41 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

42. Bone Marrow Models Gene libraries are used to create antibodies from the bone marrow Use this idea to generate attribute strings that represent receptors Antibody production through a random concatenation from gene libraries 42 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

43. Clonal Selection –CLONALG 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics 3. Cycle 43 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

44. 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics 3. Cycle CLONALG Create a random population of individuals (P) 44 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

45. 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics 3. Cycle CLONALG For each antigenic pattern in the data-set S do: 45 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

46. 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics 3. Cycle CLONALG Present it to the population P and determine its affinity with each element of the population 46 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

47. 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics 3. Cycle CLONALG Select n highest affinity elements of P Generate clones proportional to their affinity with the antigen (higher affinity=more clones) 47 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

48. 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics 3. Cycle CLONALG Mutate each clone High affinity=low mutation rate and vice-versa Add mutated individuals to population P Reselect best individual to be kept as memory m of the antigen presented 48 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

49. CLONALG 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics 3. Cycle Replace a number r of individuals with low affinity with randomly generated new ones 49 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

50. 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics 3. Cycle CLONALG Repeat step 2 until a certain stopping criteria is met 50 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

51. CLONALG n ci =round((β*n h )/i) 51 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

52. Negative Selection Algorithms Forrest 1994: Idea taken from the negative selection of T-cells in the thymus Applied initially to computer security Artificial Immune Systems 52 Developing the detector set Using the detector set 1. Immune System2. AIS Concepts 3. Designing a Framework

53. Training ALCs with negative selection 53 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

54. Artificial Immune Network Timmis and Neal,2000 The ALCs interact with each other to learn the structure of a non- self pattern The ALCs in a network co-stimulates and/or co-suppress each other to adapt to the non-self pattern The stimulation level antigen stimulation network stimulation network suppression Artificial Immune Systems 54 1. Immune System2. AIS Concepts 3. Designing a Framework

55. 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics e. Clonal suppression 3. Network interactions 4. Network suppression 5. Diversity 6. Cycle aiNET For each antigenic pattern in the data-set S do: 55 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

56. 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics e. Clonal suppression 3. Network interactions 4. Network suppression 5. Diversity 6. Cycle aiNET Present it to the population P and determine its affinity with each element of the population 56 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

57. 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics e. Clonal suppression 3. Network interactions 4. Network suppression 5. Diversity 6. Cycle aiNET Select n highest affinity elements of P Generate clones proportional to their affinity with the antigen (higher affinity=more clones) 57 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

58. 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics e. Clonal suppression 3. Network interactions 4. Network suppression 5. Diversity 6. Cycle aiNET Mutate each clone High affinity=low mutation rate and vice-versa Select h highest affinity cells and place into memory set 58 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

59. aiNET 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics e. Clonal suppression 3. Network interactions 4. Network suppression 5. Diversity 6. Cycle Eliminate all memory clones whose affinity with the antigen is less than a predefined threshold 59 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

60. 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics e. Clonal suppression 3. Network interactions 4. Network suppression 5. Diversity 6. Cycle aiNET Determine similarity between each pair of network antibodies 60 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

61. 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics e. Clonal suppression 3. Network interactions 4. Network suppression 5. Diversity 6. Cycle aiNET Eliminate all network antibodies whose affinity is less than a pre-defined threshold 61 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

62. 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics e. Clonal suppression 3. Network interactions 4. Network suppression 5. Diversity 6. Cycle aiNET Introduce a random number of new antibodies into P 62 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

63. 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics e. Clonal suppression 3. Network interactions 4. Network suppression 5. Diversity 6. Cycle aiNET Repeat 2 - 5 for a pre-defined number of iterations 63 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

64. aiNET 64 Artificial Immune Systems

65. 65 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

66. aiNET 66 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework The aiNet model uses the minimal spanning tree of the formed weighted-edge graph or hierarchical agglomerative clustering to determine the structure of the network clusters in the graph. The stopping condition of the while-loop can be one of the following Setting an iteration counter Setting the maximum size of the network Testing for convergence

67. aiNET on Data Mining Limited visualisation Interpret via MST or dendrogram Compression rate of 81% Successfully identifies the clusters Training Pattern Result immune network 67 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

68. aiNET on multimodal optimisation Initial population Final population 68 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

69. Results – Multi Function aiNET CLONALG 69 Artificial Immune Systems 1. Immune System2. AIS Concepts 3. Designing a Framework

70. Reference L. N. de Castro, J. I. Timmis, Artificial immune systems as a novel soft computing paradigm, Soft Computing 7 (2003) 526–544 Adndries P.Engelbrecht,Computational Intelligence An introduction D. Dasguptaa, S.Yua, et al, Recent Advances in Artificial Immune Systems: Models and Applications, Applied Soft Computing 11 (2011) 1574–1587. J. Zheng,Y. Chen, A Survey of artificial immune applications, Artif Intell Rev (2010) 34:19–34 Artificial Immune Systems 70

71. Thank You 71 Artificial Immune Systems

72. Questions? 72 Artificial Immune Systems