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Introductory Workshop on Evolutionary Computing

Introductory Workshop on Evolutionary Computing. Part I: Introduction to Evolutionary Algorithms. Dr. Daniel Tauritz Director, Natural Computation Laboratory Associate Professor, Department of Computer Science Research Investigator, Intelligent Systems Center

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Introductory Workshop on Evolutionary Computing

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  1. Introductory Workshop on Evolutionary Computing Part I: Introduction to Evolutionary Algorithms Dr. Daniel Tauritz Director, Natural Computation Laboratory Associate Professor, Department of Computer Science Research Investigator, Intelligent Systems Center Collaborator, Energy Research & Development Center

  2. Motivation • Real-world optimization problems are typically characterized by huge, ill-behaved solution spaces • Infeasible to exhaustively search • Defy traditional (gradient-based) optimization algorithms because they are non-linear, non-differentiable, non-continuous, or non-convex

  3. Real-World Example • Electric Power Transmission Systems • Supply is not keeping up with demand • Expansion hampered by: • Social, environmental, and economic constraints • Transmission system is “stressed” • Already carrying more than intended • Dramatic increase in incidence reports

  4. The Grid

  5. The Grid: Failure

  6. The Grid: Redistribution

  7. The Grid: A Cascade

  8. The Grid: Redistribution

  9. The Grid: Unsatisfiable

  10. The Grid: Unsatisfiable

  11. Failure Analysis • Failure spreads relatively quickly • Too quickly for conventional control • Cascade may be avoidable • Utilize unused capacities (flow compensation) • Unsatisfiable condition may be avoidable • Better power flow control to reduce severity

  12. Possible Solution • Strategically place a number of power flow control devices • Flexible A/C Transmission System (FACTS) devices are a promising type of high-speed power-electronics power flow control devices • Unified Power Flow Controller (UPFC)

  13. FACTS Interaction Laboratory UPFC Simulation Engine HIL Line

  14. The placement optimization problem • UPFCs are extremely expensive, so only a limited number can be placed • Placement is a combinatorial problem • Given 1000 high-voltage lines and 10 UPFCs, there are 1000C10 total possible placements (about 2.6 x 1023) • If each placement is evaluated in 1 minute, then it will take about 5 x 1015 centuries to solve using exhaustive search

  15. The placement solution space • Placing individual UPFC devices are not independent tasks • There are complex non-linear interactions between UPFC devices • The placement solution space is ill-behaved, so traditional optimization algorithms are not usable

  16. Evolutionary Computing • The field of Evolutionary Computing (EC) studies the theory and application of Evolutionary Algorithms (EAs) • EAs can be described as a class of stochastic, population-based optimization algorithms inspired by natural evolution, genetics, and population dynamics

  17. Very high-level EA schematic problem instance EA representation fitness function EA operators EA parameters solution

  18. Intuitive view of why EAs work • Trial-and-error (aka generate-and-test) • Graduated solution quality creates virtual gradient • Stochastic local search of solution landscape

  19. (Darwinian) Evolution • The environment contains populations of individuals of the same species which are reproductively compatible • Natural selection • Random variation • Survival of the fittest • Inheritance of traits

  20. (Mendelian) Genetics • Genotypes vs. phenotypes • Pleitropy: one gene affects multiple phenotypic traits • Polygeny: one phenotypic trait is affected by multiple genes • Chromosomes (haploid vs. diploid) • Loci and alleles

  21. Nature versus the digital realm

  22. Scope • Genotype – functional unit of inheritance • Individual – functional unit of selection • Population – functional unit of evolution

  23. Solution Representation • Structural types: linear, tree, FSM, etc. • Data types: bit strings, integers, permutations, reals, etc. • EA genotype encodes solution representation and attributes • EA phenotype expresses the EA genotype in the current environment • Encoding & Decoding

  24. Fitness Function • Determines individuals’ fitness based selection chances • Transforms objective function to linearly ordered set with higher fitness values corresponding to higher quality solutions (i.e., solutions which better satisfy the objective function) • Knapsack Problem Example

  25. Initialization • (Initial) population size • Uniform random • Heuristic based • Knowledge based • Genotypes from previous runs • Seeding

  26. Parent selection • Fitness Proportional Selection (FPS) • Roulette wheel sampling • High risk of premature convergence • Uneven selective pressure • Fitness function not transposition invariant • Fitness Rank Selection • Mapping function (like a cooling schedule) • Tournament selection

  27. Variation operators • Mutation = Stochastic unary variation operator • Recombination = Stochastic multi-ary variation operator

  28. Mutation • Bit-String Representation: • Bit-Flip • E[#flips] = L * pm • Integer Representation: • Random Reset (cardinal attributes) • Creep Mutation (ordinal attributes)

  29. Mutation cont. • Floating-Point • Uniform • Non-uniform from fixed distribution • Gaussian, Cauche, Levy, etc. • Permutation • Swap • Insert • Scramble • Inversion

  30. Recombination • Recombination rate: asexual vs. sexual • N-Point Crossover (positional bias) • Uniform Crossover (distributional bias) • Discrete recombination (no new alleles) • (Uniform) arithmetic recombination • Simple recombination • Single arithmetic recombination • Whole arithmetic recombination

  31. Survivor selection • (µ+λ) – plus strategy • (µ,λ) – comma strategy (aka generational) • Typically fitness-based • Deterministic vs. stochastic • Truncation • Elitism • Alternatives include completely stochastic and age-based

  32. Termination • CPU time / wall time • Number of fitness evaluations • Lack of fitness improvement • Lack of genetic diversity • Solution quality / solution found • Combination of the above

  33. Simple Genetic Algorithm (SGA) • Representation: Bit-strings • Recombination: 1-Point Crossover • Mutation: Bit Flip • Parent Selection: Fitness Proportional • Survival Selection: Generational

  34. Problem solving steps • Collect problem knowledge (at minimum solution representation and objective function) • Define gene representation and fitness function • Creation of initial population • Parent selection, mate pairing • Define variation operators • Survival selection • Define termination condition • Parameter tuning

  35. Typical EA Strategy Parameters • Population size • Initialization related parameters • Selection related parameters • Number of offspring • Recombination chance • Mutation chance • Mutation rate • Termination related parameters

  36. EA Pros • More general purpose than traditional optimization algorithms; i.e., less problem specific knowledge required • Ability to solve “difficult” problems • Solution availability • Robustness • Inherent parallelism

  37. EA Cons • Fitness function and genetic operators often not obvious • Premature convergence • Computationally intensive • Difficult parameter optimization

  38. Behavioral aspects • Exploration versus exploitation • Selective pressure • Population diversity • Fitness values • Phenotypes • Genotypes • Alleles • Premature convergence

  39. Genetic Programming (GP) • Characteristic property: variable-size hierarchical representation vs. fixed-size linear in traditional EAs • Application domain: model optimization vs. input values in traditional EAs • Unifying Paradigm: Program Induction

  40. Program induction examples • Optimal control • Planning • Symbolic regression • Automatic programming • Discovering game playing strategies • Forecasting • Inverse problem solving • Decision Tree induction • Evolution of emergent behavior • Evolution of cellular automata

  41. GP specification • S-expressions • Function set • Terminal set • Arity • Correct expressions • Closure property • Strongly typed GP

  42. GP notes • Mutation or recombination (not both) • Bloat (survival of the fattest) • Parsimony pressure

  43. Case Study employing GPDeriving Gas-Phase Exposure History through Computationally Evolved Inverse Diffusion Analysis

  44. Find Contaminants and Fix Issues Examine Indoor Exposure History Unexplained Sickness Introduction

  45. Background • Indoor air pollution top five environmental health risks • $160 billion could be saved every year by improving indoor air quality • Current exposure history is inadequate • A reliable method is needed to determine past contamination levels and times

  46. Problem Statement • A forward diffusion differential equation predicts concentration in materials after exposure • An inverse diffusion equation finds the timing and intensity of previous gas contamination • Knowledge of early exposures would greatly strengthen epidemiological conclusions

  47. Proposed Solution • Use Genetic Programming (GP) as a directed search for inverse equation • Fitness based on forward equation x^5 + x^4 - tan(y) / pi x^2 + sin(x) sin(cos(x+y)^2) sin(x+y) + e^(x^2) 5x^2 + 12x - 4 x^2 - sin(x) X + Sin / ?

  48. Related Research • It has been proven that the inverse equation exists • Symbolic regression with GP has successfully found both differential equations and inverse functions • Similar inverse problems in thermodynamics and geothermal research have been solved

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