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Academic Course: 05 Overview of Methods for Engineering Autonomic Self-Aware Systems


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Artificial Immune Systems by Mark read

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Academic Course: 05 Overview of Methods for Engineering Autonomic Self-Aware Systems

  1. 1. Overview of Methods for Engineering Autonomic Self-Aware Systems Artificial Immune Systems Designed by Mark Read
  2. 2. The immune system • There are many features of the immune system that are attractive to engineers [2]: – Distribution and self-organisation – Learning, adaption and memory – Pattern recognition – Classification – Homeostasis Designed by Mark Read
  3. 3. What is the immune system? • Classic View: complex system of cellular and molecular components having the primary function of distinguishing self from not self and defense against foreign organisms or substances • Cognitive View: The immune system is a cognitive system whose primary role is to provide body maintenance • Danger View : The immune system recognises dangerous agents and not non-self Designed by Mark Read
  4. 4. Artificial Immune Systems (AIS) • “AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models, which are applied to problem solving” [1] • Several flavours of AIS have emerged based on immunological theory: – Clonal selection theory – Immune network theory – Negative selection – Danger theory • … but new flavours are continually emerging Designed by Mark Read
  5. 5. Clonal Selection AIS • Clonal selection: competition for resource drives immune system cells to mutate and better recognize pathogens. • This process drives the immune response from weakly recognising a pathogen and mounting an ineffective response, to being highly specific and effective. Designed by Mark Read
  6. 6. Clonal Selection AIS • Basic algorithm [4]: • Clonal selection AIS are typically used for pattern recognition and classification problems [6] Designed by Mark Read
  7. 7. Immune Network Theory AIS • Cells of the IS recognize not only protein structures expressed by pathogens, but each other also • Through this self-recognition the IS forms positive and negative feedback networks that modulate the immune response [3] Designed by Mark Read
  8. 8. Immune Network Theory AIS • Basic algorithm [4]: • Used in data clustering and classification applications [6] Designed by Mark Read
  9. 9. Negative Selection AIS • Inspired by notions of central tolerance: • The random generation of receptors on IS cells allows the IS to effectively cover the space of protein structures that fast-evolving pathogens use (often to evade immune detection). • Central tolerance entails deleting IS cells that recognise proteins of the host, and thus prevents autoimmunity. • Though negative selection is known to be imperfect – all healthy beings contain self- reactive cells – it has been effectively used in intrusion detection systems Designed by Mark Read
  10. 10. Negative Selection AIS Basic Algorithm [4]: Used in intrusion detection systems Designed by Mark Read
  11. 11. Danger Theory AIS • Rather than detect the presence of non-host entities, the immune system recognises damage to tissues (danger) and associates the damage with what is found in the tissues [5] • If there is no damage in the tissues, immune cells that recognise structures there are instead suppressed Designed by Mark Read
  12. 12. Danger Theory AIS • Basic algorithm: – Create pool of dendritic cells (DCs) – Use some DCs to sample available antigen, and perceive danger/safe signals • Has found extensive use in anomaly detection [6] Designed by Mark Read
  13. 13. Engineering AIS • How to engineer an AIS for a particular application domain [1]? : Designed by Mark Read
  14. 14. Engineering AIS • 1. decide on appropriate representation of the problem’s data. This facilitates mapping the problem onto immune concepts, which affects how the system finds solutions. • 2. decide on appropriate affinity measures. These quantify how elements of the system interact with the problem environment, and with each other. • 3. Select algorithm(s) to operate over immune elements of the system Designed by Mark Read
  15. 15. Future directions in AIS • Impasse, better capture natural system • Modelling/conceptual framework • Features of problem domains Designed by Mark Read
  16. 16. References • [1] De Castro, L., Timmis, J. Artificial Immune Systems: A New Computational Intelligence Approach. Springer, 2002 • [2] Read, M., Andrews, P., Timmis, J. An Introduction to Artificial Immune Systems. The handbook of natural Computing, edited by Grzegorz Rozenberg, Thomas Back and Joost Kok. Springer, 2011 • [3] Niels K. Jerne. Towards a network theory of the immune system. Ann. Immunol. (Inst. Pasteur), 125C:373–389, 1974 • [4] Freitas, A., Timmis, J. - Revisiting the Foundations of Artificial Immune Systems for Data Mining. IEEE Transactions on Evolutionary Computation 11(4) pp. 521-540 (2007) • [5] Polly Matzinger. Tolerance, danger, and the extended family. Annual Review of Immunology, 12:991–1045, April 1994 • [6] E. Hart and J. Timmis. Application Areas of AIS: The Past, Present and the Future. Journal of Applied Soft Computing 8(1). pp.191- 201. 2008 Designed by Mark Read
  17. 17. Overview of Methods for Engineering Autonomic Self-Aware Systems Evolutionary Algorithms Designed by Gusz Eiben
  18. 18. The Main EC Metaphor EVOLUTION Environment Individual Fitness PROBLEM SOLVING Problem Candidate Solution Quality Quality  chance for seeding new solutionsCV Fitness  chances for survival and reproduction Fitness in nature: observed, 2ndary, EC: primary Designed by Gusz Eiben
  19. 19. General scheme of EAs Population Parents Parent selection Survivor selection Offspring Recombination (crossover) Mutation Intialization Termination Designed by Gusz Eiben
  20. 20. EAs as problem solvers • EAs fall into the category of “generate and test” algorithms • They are stochastic, population-based algorithms • Variation operators (recombination and mutation) create the necessary diversity and thereby facilitate novelty • Selection reduces diversity and acts as a force pushing quality • Traditionally seen as optimizers (also in data mining applications) • Lately as the engine behind adaptation Designed by Gusz Eiben
  21. 21. Two pillars of evolution There are two competing forces  Increasing population diversity by genetic operators  mutation  recombination Push towards novelty  Decreasing population diversity by selection  of parents  of survivors Push towards quality Designed by Gusz Eiben
  22. 22. Common model of evolutionary processes • Population of individuals • Individuals have a fitness • Variation operators: crossover, mutation • Selection towards higher fitness – “survival of the fittest” and – “mating of the fittest” Neo Darwinism: Evolutionary progress towards higher life forms = Optimization according to some fitness-criterion (optimization on a fitness landscape) Designed by Gusz Eiben
  23. 23. Main EA components • Representation • Population • Selection (parent selection, survivor selection) • Variation (mutation, recombination) • Initialisation • Termination condition Designed by Gusz Eiben
  24. 24. Representation • Role: provides code for candidate solutions that can be manipulated by variation operators • Leads to two levels of existence – phenotype: object in original problem context, the outside – genotype: code to denote that object, the inside (chromosome, “digital DNA”) • Implies two mappings: – Encoding : phenotype=> genotype (not necessarily one to one) – Decoding : genotype=> phenotype (must be one to one) • Chromosomes contain genes, which are in (usually fixed) positions called loci (sing. locus) and have a value (allele) Designed by Gusz Eiben
  25. 25. Genotype spacePhenotype space Encoding (representation) Decoding (inverse representation) B 0 c 0 1 c d G 0 c 0 1 c d R 0 c 0 1 c d Representation 2 In order to find the global optimum, every feasible solution must be represented in genotype space Designed by Gusz Eiben
  26. 26. Evaluation (fitness) function • Role: – Represents the task to solve, the requirements to adapt to (can be seen as “the environment”) – enables selection (provides basis for comparison) – e.g., some phenotypic traits are advantageous, desirable, e.g. big ears cool better, hese traits are rewarded by more offspring that will expectedly carry the same trait • A.k.a. quality function or objective function • Assigns a single real-valued fitness to each phenotype which forms the basis for selection – So the more discrimination (different values) the better • Typically we talk about fitness being maximised – Some problems may be best posed as minimisation problems, but conversion is trivial Designed by Gusz Eiben
  27. 27. Population • Role: holds the candidate solutions of the problem as individuals (genotypes) • Formally, a population is a multiset of individuals, i.e. repetitions are possible • Population is the basic unit of evolution, i.e., the population is evolving, not the individuals • Selection operators act on population level • Variation operators act on individual level Designed by Gusz Eiben
  28. 28. Population 2 • Some sophisticated EAs also assert a spatial structure on the population e.g., a grid • Selection operators usually take whole population into account i.e., reproductive probabilities are relative to current generation • Diversity of a population refers to the number of different fitnesses / phenotypes / genotypes present (note: not the same thing) Designed by Gusz Eiben
  29. 29. Selection Role: • Identifies individuals – to become parents – to survive • Pushes population towards higher fitness • Usually probabilistic – high quality solutions more likely to be selected than low quality – but not guaranteed – even worst in current population usually has non-zero probability of being selected • This stochastic nature can aid escape from local optima Designed by Gusz Eiben
  30. 30. Example: roulette wheel selection fitness(A) = 3 fitness(B) = 1 fitness(C) = 2 A C 1/6 = 17% 3/6 = 50% B 2/6 = 33% Selection mechanism example In principle, any selection mechanism can be used for parent selection as well as for survivor selection Designed by Gusz Eiben
  31. 31. Survivor selection • A.k.a. replacement • Most EAs use fixed population size so need a way of going from (parents + offspring) to next generation • Often deterministic (while parent selection is usually stochastic) – Fitness based : e.g., rank parents+offspring and take best – Age based: make as many offspring as parents and delete all parents • Sometimes a combination of stochastic and deterministic (elitism) Designed by Gusz Eiben
  32. 32. Variation operators • Role: to generate new candidate solutions • Usually divided into two types according to their arity (number of inputs): – Arity 1 : mutation operators – Arity >1 : Recombination operators – Arity = 2 typically called crossover – Arity > 2 is formally possible, seldomly used in EC • There has been much debate about relative importance of recombination and mutation – Nowadays most EAs use both – Variation operators must match the given representation Designed by Gusz Eiben
  33. 33. Mutation • Role: causes small, random variance • Acts on one genotype and delivers another • Element of randomness is essential and differentiates it from other unary heuristic operators • Importance ascribed depends on representation and historical dialect: – Binary GAs – background operator responsible for preserving and introducing diversity – EP for FSM’s/ continuous variables – only search operator – GP – hardly used • May guarantee connectedness of search space and hence convergence proofs Designed by Gusz Eiben
  34. 34. before 1 1 1 0 1 1 1 after 1 1 1 1 1 1 1 Mutation example Designed by Gusz Eiben
  35. 35. Recombination • Role: merges information from parents into offspring • Choice of what information to merge is stochastic • Most offspring may be worse, or the same as the parents • Hope is that some are better by combining elements of genotypes that lead to good traits • Principle has been used for millennia by breeders of plants and livestock Designed by Gusz Eiben
  36. 36. 1 1 1 1 1 1 1 0 0 0 0 0 0 0 Parents cut cut Offspring Recombination example 1 1 1 0 0 0 0 0 0 0 1 1 1 1 Designed by Gusz Eiben
  37. 37. Initialisation / Termination • Initialisation usually done at random, – Need to ensure even spread and mixture of possible allele values – Can include existing solutions, or use problem-specific heuristics, to “seed” the population • Termination condition checked every generation – Reaching some (known/hoped for) fitness – Reaching some maximum allowed number of generations – Reaching some minimum level of diversity – Reaching some specified number of generations without fitness improvement Designed by Gusz Eiben
  38. 38. What are the different types of EAs • Historically different flavours of EAs have been associated with different data types to represent solutions – Binary strings : Genetic Algorithms – Real-valued vectors : Evolution Strategies – Finite state Machines: Evolutionary Programming – LISP trees: Genetic Programming • These differences are largely irrelevant, best strategy – choose representation to suit problem – choose variation operators to suit representation • Selection operators only use fitness and so are independent of representation Designed by Gusz Eiben
  39. 39. Typical behaviour of an EA Stages in optimising on a 1-dimensional fitness landscape Early stage: quasi-random population distribution Mid-stage: population arranged around/on hills Late stage: population concentrated on high hills Designed by Gusz Eiben
  40. 40. Typical run: progression of fitness Typical run of an EA shows so-called “anytime behavior” Bestfitnessinpopulation Time (number of generations) Designed by Gusz Eiben
  41. 41. Evolutionary Algorithms in context • There are many views on the use of EAs as robust problem solving tools • For most problems a problem-specific tool may: – perform better than a generic search algorithm on most instances, – have limited utility, – not do well on all instances • Evolutionary Algorithms: – Provide rather stable performance over a range of problems and instances – Can be easily ported from one problem to another one – Can be hybridized with existing problem specific heuristics Designed by Gusz Eiben
  42. 42. Scale of “all” problems Performanceofmethodsonproblems Random search Special, problem tailored method Evolutionary algorithm EAs as problem solvers: Goldberg view (1989) Designed by Gusz Eiben
  43. 43. Summary Evolutionary Algorithms: • Are population-based, stochastic search methods • Can be used for optimization and as “adaptive engine” • Can be hybridized with problem specific heuristics • Well suited for parallel execution • Can be easily ported to new problems An EA is the second best solver for any problem Designed by Gusz Eiben
  44. 44. Further reading • A.E. Eiben, Evolutionary computing: the most powerful problem solver in the universe?, Dutch Mathematical Archive (Nederlands Archief voor Wiskunde), vol. 5/3, nr. 2, 126- 131, 2002 • Eiben-Smith book Designed by Gusz Eiben