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

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



Artificial Immune Systems by Mark read

Artificial Immune Systems by Mark read



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

    • Overview of Methods for Engineering Autonomic Self-Aware Systems Artificial Immune Systems Designed by Mark Read
    • 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
    • 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
    • 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
    • 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
    • Clonal Selection AIS • Basic algorithm [4]: • Clonal selection AIS are typically used for pattern recognition and classification problems [6] Designed by Mark Read
    • 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
    • Immune Network Theory AIS • Basic algorithm [4]: • Used in data clustering and classification applications [6] Designed by Mark Read
    • 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
    • Negative Selection AIS Basic Algorithm [4]: Used in intrusion detection systems Designed by Mark Read
    • 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
    • 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
    • Engineering AIS • How to engineer an AIS for a particular application domain [1]? : Designed by Mark Read
    • 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
    • Future directions in AIS • Impasse, better capture natural system • Modelling/conceptual framework • Features of problem domains Designed by Mark Read
    • 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
    • Overview of Methods for Engineering Autonomic Self-Aware Systems Evolutionary Algorithms Designed by Gusz Eiben
    • 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
    • General scheme of EAs Population Parents Parent selection Survivor selection Offspring Recombination (crossover) Mutation Intialization Termination Designed by Gusz Eiben
    • 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
    • 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
    • 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
    • Main EA components • Representation • Population • Selection (parent selection, survivor selection) • Variation (mutation, recombination) • Initialisation • Termination condition Designed by Gusz Eiben
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • before 1 1 1 0 1 1 1 after 1 1 1 1 1 1 1 Mutation example Designed by Gusz Eiben
    • 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
    • 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
    • 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
    • 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
    • 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
    • Typical run: progression of fitness Typical run of an EA shows so-called “anytime behavior” Bestfitnessinpopulation Time (number of generations) Designed by Gusz Eiben
    • 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
    • Scale of “all” problems Performanceofmethodsonproblems Random search Special, problem tailored method Evolutionary algorithm EAs as problem solvers: Goldberg view (1989) Designed by Gusz Eiben
    • 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
    • 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