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Andrea G. B. Tettamanzi, 2002 Algoritmi Evolutivi Andrea G. B. Tettamanzi

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Andrea G. B. Tettamanzi, 2002 Lezione 1 23 aprile 2002

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Andrea G. B. Tettamanzi, 2002 Contents of the Lectures Taxonomy and History; Evolutionary Algorithms basics; Theoretical Background; Outline of the various techniques: plain genetic algorithms, evolutionary programming, evolution strategies, genetic programming; Practical implementation issues; Evolutionary algorithms and soft computing; Selected applications from the biological and medical area; Summary and Conclusions.

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Andrea G. B. Tettamanzi, 2002 Bibliography Th. Bäck. Evolutionary Algorithms in Theory and Practice. Oxford University Press, 1996 L. Davis. The Handbook of Genetic Algorithms. Van Nostrand & Reinhold, 1991 D.B. Fogel. Evolutionary Computation. IEEE Press, 1995 D.E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, 1989 J. Koza. Genetic Programming. MIT Press, 1992 Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer Verlag, 3rd ed., 1996 H.-P. Schwefel. Evolution and Optimum Seeking. Wiley & Sons, 1995 J. Holland. Adaptation in Natural and Artificial Systems. MIT Press 1995

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Andrea G. B. Tettamanzi, 2002 Tassonomia (1) Algoritmi Genetici Algoritmi Evolutivi Programmazione Evolutiva Strategie Evolutive Programmazione Genetica Ricottura simulata (simulated annealing) Taboo Search Metodi Monte Carlo Metodi stocastici di ottimizzazione

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Andrea G. B. Tettamanzi, 2002 Tassonomia Tratti distintivi di un algoritmo evolutivo opera su una codifica appropriata delle soluzioni; considera a ogni istante una popolazione di soluzioni candidate; non richiede condizioni di regolarità (p.es. derivabilità); segue regole di transizione probabilistiche.

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Andrea G. B. Tettamanzi, 2002 History (1) I. Rechenberg, H.-P. Schwefel TU Berlin, 60s John H. Holland University of Michigan, Ann Arbor, 60s L. Fogel UC S. Diego, 60s John Koza Stanford University 80s

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Andrea G. B. Tettamanzi, 2002 History (2) 1859 Charles Darwin: inheritance, variation, natural selection 1957 G. E. P. Box: random mutation & selection for optimization 1958 Fraser, Bremermann: computer simulation of evolution 1964 Rechenberg, Schwefel: mutation & selection 1966 Fogel et al.: evolving automata - evolutionary programming 1975 Holland: crossover, mutation & selection - reproductive plan 1975 De Jong: parameter optimization - genetic algorithm 1989 Goldberg: first textbook 1991 Davis: first handbook 1993 Koza: evolving LISP programs - genetic programming

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Andrea G. B. Tettamanzi, 2002 Evolutionary Algorithms Basics what an EA is (the Metaphor) object problem and fitness the Ingredients schemata implicit parallelism the Schema Theorem the building blocks hypothesis deception

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Andrea G. B. Tettamanzi, 2002 La metafora AmbienteProblema da risolvere Individuo Addattamento Soluzione candidata Qualità della soluzione EVOLUZIONE PROBLEM SOLVING

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Andrea G. B. Tettamanzi, 2002 Object problem and Fitness genotype solution M object problem s f fitness

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Andrea G. B. Tettamanzi, 2002 Gli ingredienti di un algoritmo evolutivo generazione t t + 1 mutazione ricombinazione riproduzione selezione (sopravvivenza del più adatto) popolazione di soluzioni (appropriatamente codificate) DNA di una soluzione

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Andrea G. B. Tettamanzi, 2002 Il ciclo evolutivo Ricombinazione Mutazione Popolazione FigliGenitori Selezione Sostituzione Riproduzione

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Andrea G. B. Tettamanzi, 2002 Pseudocode generation = 0; SeedPopulation(popSize);// at random or from a file while(!TerminationCondition()) { generation = generation + 1; CalculateFitness();//... of new genotypes Selection();// select genotypes that will reproduce Crossover(p cross );// mate p cross of them on average Mutation(p mut );// mutate all the offspring with Bernoulli // probability p mut over genes }

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Andrea G. B. Tettamanzi, 2002 A Sample Genetic Algorithm The MAXONE problem Genotypes are bit strings Fitness-proportionate selection One-point crossover Flip mutation (transcription error)

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Andrea G. B. Tettamanzi, 2002 The MAXONE Problem Problem instance: a string of l binary cells, l : Objective: maximize the number of ones in the string. Fitness:

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Andrea G. B. Tettamanzi, 2002 Fitness Proportionate Selection Implementation: Roulette Wheel Probability of being selected:

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Andrea G. B. Tettamanzi, 2002 One Point Crossover 00000111110110101010 crossover point 01000010100010111111 parentsoffspring

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Andrea G. B. Tettamanzi, 2002 Mutation 1110101010 p mut 0110101011 independent Bernoulli transcription errors

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Andrea G. B. Tettamanzi, 2002 Example: Selection 0111011011f = 7Cf = 7P = 0.125 1011011101f = 7Cf = 14P = 0.125 1101100010f = 5Cf = 19P = 0.089 0100101100f = 4Cf = 23P = 0.071 1100110011f = 6Cf = 29P = 0.107 1111001000f = 5Cf = 34P = 0.089 0110001010f = 4Cf = 38P = 0.071 1101011011f = 7Cf = 45P = 0.125 0110110000f = 4Cf = 49P = 0.071 0011111101f = 7Cf = 56P = 0.125 Random sequence: 43, 1, 19, 35, 15, 22, 24, 38, 44, 2

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Andrea G. B. Tettamanzi, 2002 Example: Recombination & Mutation 0111011011 0111011011 0111111011f = 8 0111011011 0111011011 0111011011f = 7 110|1100010 1100101100 1100101100f = 5 010|0101100 0101100010 0101100010f = 4 1|100110011 1100110011 1100110011f = 6 1|100110011 1100110011 1000110011f = 5 0110001010 0110001010 0110001010f = 4 1101011011 1101011011 1101011011f = 7 011000|1010 0110001011 0110001011f = 5 110101|1011 1101011010 1101011010f = 6 TOTAL = 57

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Andrea G. B. Tettamanzi, 2002 Lezione 2 24 aprile 2002

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Andrea G. B. Tettamanzi, 2002 Schemata Dont care symbol: 1 10 a schema S matches 2 l - o(S) strings a string of length l is matched by 2 l schemata order of a schema: o(S) = # fixed positions defining length (S) = distance between first and last fixed position

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Andrea G. B. Tettamanzi, 2002 Implicit Parallelism In a population of n individuals of length l 2 l # schemata processed n2 l n 3 of which are processed usefully (Holland 1989) (i.e. are not disrupted by crossover and mutation) But see Bertoni & Dorigo (1993) Implicit Parallelism in Genetic Algorithms Artificial Intelligence 61(2), p. 307 314

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Andrea G. B. Tettamanzi, 2002 Fitness of a schema f( ): fitness of string q x ( ): fraction of strings equal to in population x q x (S): fraction of strings matched by S in population x

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Andrea G. B. Tettamanzi, 2002 The Schema Theorem {X t } t=0,1,... populations at times t suppose thatis constant i.e. above-average individuals increase exponentially!

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Andrea G. B. Tettamanzi, 2002 The Schema Theorem (proof)

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Andrea G. B. Tettamanzi, 2002 The Building Blocks Hypothesis An evolutionary algorithm seeks near-optimal performance through the juxtaposition of short, low-order, high-performance schemata the building blocks

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Andrea G. B. Tettamanzi, 2002 Deception i.e. when the building block hypothesis does not hold: butfor some schema S, Example: * = 1111111111 S 1 = 111******* S 2 = ********11 S = 111*****11 S = 000*****00

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Andrea G. B. Tettamanzi, 2002 Remedies to deception Prior knowledge of the objective function Non-deceptive encoding Inversion Semantics of genes not positional Messy Genetic Algorithms Underspecification & overspecification

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Andrea G. B. Tettamanzi, 2002 Theoretical Background Theory of random processes; Convergence in probability; Open question: rate of convergence.

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Andrea G. B. Tettamanzi, 2002 Eventi Spazio campionario A B D

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Andrea G. B. Tettamanzi, 2002 Variabili aleatorie 0 X

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Andrea G. B. Tettamanzi, 2002 Processi Stocastici Un processo stocastico è una successione di v.a. Ciascuna con la propria distribuzione di probabilità. Notazione:

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Andrea G. B. Tettamanzi, 2002 EAs as Random Processes a sample of size n probability space random numbers trajectory evolutionary process

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Andrea G. B. Tettamanzi, 2002 Catene di Markov Un processo stocastico è una catena di Markov sse il suo stato dipende solo dallo stato precedente, cioè, per ogni t, ABC 0.4 0.6 0.3 0.7 0.25 0.75

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Andrea G. B. Tettamanzi, 2002 Abstract Evolutionary Algorithm select: (n) cross: mutate: mate: insert: XtXt X t+1 select cross mate insertmutate Stochastic functions: Transition function:

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Andrea G. B. Tettamanzi, 2002 Convergence to Optimum Theorem: if {X t ( )} t = 0, 1,... is monotone, homogeneous, x 0 is given, y in reach (x 0 ) (n) O reachable, then Theorem: if select, mutate are generous, the neighborhood structure is connective, transition functions T t ( ), t = 0, 1,... are i.i.d. and elitist, then

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Andrea G. B. Tettamanzi, 2002 Lezione 3 7 maggio 2002

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Andrea G. B. Tettamanzi, 2002 Outline of various techniques Plain Genetic Algorithms Evolutionary Programming Evolution Strategies Genetic Programming

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Andrea G. B. Tettamanzi, 2002 Plain Genetic Algorithms Individuals are bit strings Mutation as transcription error Recombination is crossover Fitness proportionate selection

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Andrea G. B. Tettamanzi, 2002 Evolutionary Programming Individuals are finite-state automata Used to solve prediction tasks State-transition table modified by uniform random mutation No recombination Fitness depends on the number of correct predictions Truncation selection

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Andrea G. B. Tettamanzi, 2002 Evolutionary Programming: Individuals Finite-state automaton: ( Q, q 0, A,, ) set of states Q ; initial state q 0 ; set of accepting states A ; alphabet of symbols ; transition function : Q Q ; output mapping function : Q ; q0q0 q1q1 q2q2 a b c state input q0q0 q0q0 q0q0 q1q1 q1q1 q1q1 q2q2 q2q2 q2q2 q1q1 q0q0 q2q2 b/cc/b a/b c/c a/b b/c a/a c/ab/a a cc c a ab bb

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Andrea G. B. Tettamanzi, 2002 Evolutionary Programming: Fitness abcabcab b =? no yes f( ) = f( ) + 1 individual prediction

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Andrea G. B. Tettamanzi, 2002 Evolutionary Programming: Selection Variant of stochastic q-tournament selection: 1 2 q... score( ) = #{ i | f( ) > f( i ) } Order individuals by decreasing score Select first half (Truncation selection)

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Andrea G. B. Tettamanzi, 2002 Evolution Strategies Individuals are n-dimensional vectors of reals Fitness is the objective function Mutation distribution can be part of the genotype (standard deviations and covariances evolve with solutions) Multi-parent recombination Deterministic selection (truncation selection)

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Andrea G. B. Tettamanzi, 2002 Evolution Strategies: Individuals candidate solution rotation angles standard deviations

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Andrea G. B. Tettamanzi, 2002 Evolution Strategies: Mutation Hans-Paul Schwefel suggests: self-adaptation

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Andrea G. B. Tettamanzi, 2002 Genetic Programming Program induction LISP (historically), math expressions, machine language,... Applications: –optimal control; –planning; –sequence induction; –symbolic regression; –modelling and forecasting; –symbolic integration and differentiation; –inverse problems

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Andrea G. B. Tettamanzi, 2002 Genetic Programming: The Individuals subset of LISP S-expressions (OR (AND (NOT d0) (NOT d1)) (AND d0 d1)) OR AND NOT d0 NOT d1 AND d0d1

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Andrea G. B. Tettamanzi, 2002 Genetic Programming: Initialization ORANDNOT d0 NOT d1 ANDd0d1OR AND OR AND OR AND NOT

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Andrea G. B. Tettamanzi, 2002 Genetic Programming: Crossover OR ANDNOT d0 d1 OR AND d1NOT d0 d1 OR AND NOT d0 d1 OR AND d1NOT d0 d1

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Andrea G. B. Tettamanzi, 2002 Genetic Programming: Other Operators Mutation: replace a terminal with a subtree Permutation: change the order of arguments to a function Editing: simplify S-expressions, e.g. (AND X X) X Encapsulation: define a new function using a subtree Decimation: throw away most of the population

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Andrea G. B. Tettamanzi, 2002 Genetic Programming: Fitness Fitness cases:j = 1,..., N e Raw fitness: Standardized fitness:s( ) [0, + ) Adjusted fitness:

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Andrea G. B. Tettamanzi, 2002 Sample Application: Myoelectric Prosthesis Control Control of an upper arm prosthesis Genetic Programming application Recognize thumb flection, extension and abduction patterns

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Andrea G. B. Tettamanzi, 2002 Prosthesis Control: The Context human arm myoelectric signals measure raw myo-measurements preprocess myo-signal features deduce intentions map into goal human motion robot motion convert actuator commands robot arm 150 ms 2 electrodes

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Andrea G. B. Tettamanzi, 2002 Prosthesis Control: Terminals Features for electrodes 1, 2: Mean absolute value (MAV) Mean absolute value slope (MAVS) Number of zero crossings (ZC) Number of slope sign changes (SC) Waveform length (LEN) Average value (AVG) Up slope (UP) Down slope (DOWN) MAV1/MAV2, MAV2/MAV1 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 0.01, -1.0

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Andrea G. B. Tettamanzi, 2002 Prosthesis Control: Function Set Additionx + y Subtractionx - y Multiplicationx * y Divisionx / y(protected for y=0) Square rootsqrt(|x|) Sinesin x Cosinecos x Tangenttan x (protected for x= /2) Natural logarithmln |x| (protected for x=0) Common logarithmlog |x| (protected for x=0) Exponentialexp x Power functionx ^ y Reciprocal1/x (protected for x=0) Absolute value|x| Integer or truncateint(x) Signsign(x)

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Andrea G. B. Tettamanzi, 2002 Prosthesis Control: Fitness type 1 type 2type 3 undefined separation spread 22 signals per motion result

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Andrea G. B. Tettamanzi, 2002 Myoelectric Prosthesis Control Reference Jaime J. Fernandez, Kristin A. Farry and John B. Cheatham. Waveform Recognition Using Genetic Programming: The Myoelectric Signal Recognition Problem. GP 96, The MIT Press, pp. 63–71

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Andrea G. B. Tettamanzi, 2002 Classifier Systems (Michigan approach) IF X = A AND Y = B THEN Z = D individual: IF... THEN... where number of attributes in antecedent part

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Andrea G. B. Tettamanzi, 2002 Lezione 4 14 maggio 2002

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Andrea G. B. Tettamanzi, 2002 Practical Implementation Issues from elegant academia to not so elegant but robust and efficient real-world applications, evolution programs handling constraints hybridization parallel and distributed algorithms

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Andrea G. B. Tettamanzi, 2002 Evolution Programs Slogan: Genetic Algorithms + Data Structures = Evolution Programs Key ideas: use a data structure as close as possible to object problem write appropriate genetic operators ensure that all genotypes correspond to feasible solutions ensure that genetic operators preserve feasibility

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Andrea G. B. Tettamanzi, 2002 Encodings: Pie Problems 0–255 128329020 0–255 WXYZ X = 32/270 = 11.85%

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Andrea G. B. Tettamanzi, 2002 Encodings: Permutation Problems Adjacency Representation Ordinal Representation (2, 4, 8, 3, 9, 7, 1, 5, 6) 1 - 2 - 4 - 3 - 8 - 5 - 9 - 6 - 7 (1, 1, 2, 1, 4, 1, 3, 1, 1) Path Representation (1, 2, 4, 3, 8, 5, 9, 6, 7) Matrix Representation 101 0 0 0 0 0 0 0 0 111111 01111111 00011111 00111111 00001111 00000111 00000011 10 0 000000 0000000 Sorting Representation (-23, -6, 2, 0, 19, 32, 85, 11, 25)

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Andrea G. B. Tettamanzi, 2002 Handling Constraints Penalty functions Risk of spending most of the time evaluating unfeasible solutions, sticking with the first feasible solution found, or finding an unfeasible solution that scores better of feasible solutions Decoders or repair algorithms Computationally intensive, tailored to the particular application Appropriate data structures and specialized genetic operators All possible genotypes encode for feasible solutions

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Andrea G. B. Tettamanzi, 2002 Penalty Functions S c P

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Andrea G. B. Tettamanzi, 2002 Decoders / Repair Algorithms S c recombination mutation

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Andrea G. B. Tettamanzi, 2002 Hybridization 2) Use local optimization algorithms as genetic operators (Lamarckian mutation) 1) Seed the population with solutions provided by some heuristics heuristicsinitial population 3) Encode parameters of a heuristics genotype heuristicscandidate solution

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Andrea G. B. Tettamanzi, 2002 Sample Application: Unit Commitment Multiobjective optimization problem: cost VS emission Many linear and non-linear constraints Traditionally approached with dynamic programming Hybrid evolutionary/knowledge-based approach A flexible decision support system for planners Solution time increases linearly with the problem size

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Andrea G. B. Tettamanzi, 2002 The Unit Commitment Problem EmissionsCost

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Andrea G. B. Tettamanzi, 2002 Predicted Load Curve

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Andrea G. B. Tettamanzi, 2002 Unit Commitment: Constraints Power balance requirement Spinning reserve requirement Unit maximum and minimum output limits Unit minimum up and down times Power rate limits Unit initial conditions Unit status restrictions Plant crew constraints...

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Andrea G. B. Tettamanzi, 2002 Unit Commitment: Encoding Unit 1Unit 2Unit 3Unit 4Time 1.000:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 1.0 0.9 0.8 0.4 0.8 0.75 0.8 0.2 0.25 0.2 0.15 0.0 0.5 0.65 0.5 1.0 Fuzzy Knowledge Base

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Andrea G. B. Tettamanzi, 2002 Unit Commitment: Solution Unit 1Unit 2Unit 3Unit 4Time 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 down hot-stand-by starting shutting down up

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Andrea G. B. Tettamanzi, 2002 Unit Commitment: Selection cost ($) emission $507,762$516,511 213,489 £60,080 £ competitive selection:

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Andrea G. B. Tettamanzi, 2002 Unit Commitment References D. Srinivasan, A. Tettamanzi. An Integrated Framework for Devising Optimum Generation Schedules. In Proceedings of the 1995 IEEE International Conference on Evolutionary Computing (ICEC 95), vol. 1, pp. 1-4. D. Srinivasan, A. Tettamanzi. A Heuristic-Guided Evolutionary Approach to Multiobjective Generation Scheduling. IEE Proceedings Part C - Generation, Transmission, and Distribution, 143(6):553-559, November 1996. D. Srinivasan, A. Tettamanzi. An Evolutionary Algorithm for Evauation of Emission Compliance Options in View of the Clean Air Act Amendments. IEEE Transactions on Power Systems, 12(1):336-341, February 1997.

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Andrea G. B. Tettamanzi, 2002 Lezione 5 15 maggio 2002

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Andrea G. B. Tettamanzi, 2002 Algoritmi Evolutivi Paralleli Algoritmo evolutivo standard enunciato come sequenziale... … ma gli algoritmi evolutivi sono intrinsecamente paralleli Vari modelli: –algoritmo evolutivo cellulare –algoritmo evolutivo parallelo a grana fine (griglia) –algoritmo evolutivo parallelo a grana grossa (isole) –algoritmo evolutivo sequenziale con calcolo della fitness parallelo (master - slave)

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Andrea G. B. Tettamanzi, 2002 Island Model

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Andrea G. B. Tettamanzi, 2002 Sample Application: Protein Folding Finding 3-D geometry of a protein to understand its functionality Very difficult: one of the grand challenge problems Standard GA approach Simplified protein model

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Andrea G. B. Tettamanzi, 2002 Protein Folding: The Problem Much of a proteins function may be derived from its conformation (3-D geometry or tertiary structure). Magnetic resonance & X-ray crystallography are currently used to view the conformation of a protein: –expensive in terms of equipment, computation and time; –require isolation, purification and crystallization of protein. Prediction of the final folded conformation of a protein chain has been shown to be NP-hard. Current approaches: –molecular dynamics modelling (brute force simulation); –statistical prediction; –hill-climbing search techniques (simulated annealing).

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Andrea G. B. Tettamanzi, 2002 Protein Folding: Simplified Model 90° lattice (6 degrees of freedom at each point); Peptides occupy intersections; No side chains; Hydrophobic or hydrophilic (no relative strengths) amino acids; Only hydrophobic/hydrophilic forces considered; Adjacency considered only in cardinal directions; Cross-chain hydrophobic contacts are the basis for evaluation.

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Andrea G. B. Tettamanzi, 2002 Protein Folding: Representation preference order encoding: relative move encoding: UPDOWNFORWARDLEFTUPRIGHT UP LEFT RIGHT DOWN FORWARD DOWN LEFT UP FORWARD RIGHT FORWARD UP DOWN LEFT RIGHT LEFT DOWN FORWARD UP RIGHT...

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Andrea G. B. Tettamanzi, 2002 Protein Folding: Fitness Decode: plot the course encoded by the genotype. Test each occupied cell: any collisions: -2; no collisions AND a hydrophobe in an adjacent cell: 1. Notes: for each contact: +2; adjacent hydrophobes not discounted in the scoring; multiple collisions (>1 peptides in one cell): -2; hydrophobe collisions imply an additional penalty (no contacts are scored).

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Andrea G. B. Tettamanzi, 2002 Protein Folding: Experiments Preference ordering encoding; Two-point crossover with a rate of 95%; Bit mutation with a rate of 0.1%; Population size: 1000 individuals; crowding and incest reduction. Test sequences with known minimum configuration;

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Andrea G. B. Tettamanzi, 2002 Protein Folding References S. Schulze-Kremer. Genetic Algorithms for Protein Tertiary Structure Prediction. PPSN 2, North-Holland 1992. R. Unger and J. Moult. A Genetic Algorithm for 3D Protein Folding Simulations. ICGA-5, 1993, pp. 581–588. Arnold L. Patton, W. F. Punch III and E. D. Goodman. A Standard GA Approach to Native Protein Conformation Prediction. ICGA 6, 1995, pp. 574–581.

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Andrea G. B. Tettamanzi, 2002 Sample Application: Drug Design Purpose: given a chemical specification (activity), design a tertiary structure complying with it. Requirement: a quantitative structure-activity relationship model. Example: design ligands that can bind targets specifically and selectively. Complementary peptides.

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Andrea G. B. Tettamanzi, 2002 Drug Design: Implementation NLHAFGLFKA amino acid (residue) individual name hydropathic value Operators: Hill-climbing Crossover Hill-climbing Mutation Reordering (no selection) implicit selection

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Andrea G. B. Tettamanzi, 2002 Drug Design: Fitness target acomplement b moving average hydropathy hydropathy of residues k s,..., n s n : number of residues in target (lower Q = better complementarity)

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Andrea G. B. Tettamanzi, 2002 Drug Design: Results

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Andrea G. B. Tettamanzi, 2002 Drug Design References T. S. Lim. A Genetic Algorithms Approach for Drug Design. MS Dissertation, Oxford University, Computing Laboratory, 1995. A. L. Parrill. Evolutionary and Genetic Methods in Drug Design. Drug Discovery Today, Vol. 1, No. 12, Dec 1996, pp. 514–521.

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