Evolutionary ComputingIntroduction1 Introduction, Part I Positioning of EC and the basic EC metaphor Historical perspective Biological inspiration: – Darwinian evolution theory (simplified!) – Genetics (simplified!) Motivation for EC What can EC do: examples of application areas Demo: evolutionary magic square solver

Evolutionary ComputingIntroduction2 You are here

Evolutionary ComputingIntroduction3 Positioning of EC EC is part of computer science EC is not part of life sciences/biology Biology delivered inspiration and terminology EC can be applied in biological research

Evolutionary ComputingIntroduction4 Fathers of evolutionary computing Charles Darwin “Father of the evolution theory” Gregor Mendel “Father of genetics” John von Neumann 1903 –1957 “Father of the computer” Alan Mathison Turing “Father of the computer”

Evolutionary ComputingIntroduction5 Evolutionary Computing: the Basic Metaphor EVOLUTION Environment Individual Fitness PROBLEM SOLVING Problem Candidate Solution Quality Quality  chance for seeding new solutions Fitness  chances for survival and reproduction

Evolutionary ComputingIntroduction6 Historical perspective 1: The dawn of EC 1948, Turing proposes “genetical or evolutionary search” 1962, Bremermann optimization through evolution and recombination 1964, Rechenberg introduces evolution strategies 1965, L. Fogel, Owens and Walsh introduce evolutionary programming 1975, Holland introduces genetic algorithms

Evolutionary ComputingIntroduction7 Historical perspective 2: The rise of EC 1985: first international conference (ICGA) 1990: first international conference in Europe (PPSN) 1993: first scientific EC journal (MIT Press) 1997: launch of European EC Research Network (EvoNet)

Evolutionary ComputingIntroduction8 Historical perspective 3: The present of EC 3 major conferences, 10 – 15 small ones 3 scientific core EC journals + 2 Web-based ones papers published in 2001 (estimate) EvoNet has over 150 member institutes uncountable (meaning: many) applications uncountable (meaning: ?) consultancy and R&D firms

Evolutionary ComputingIntroduction9 Common model of evolutionary processes Organic evolution: Population of individuals Individuals have a fitness Variation operators: recombination, mutation Selection towards higher fitness: “survival of the fittest” Open-ended adaptation in a dynamically changing world

Evolutionary ComputingIntroduction10 Darwinism Evolutionary progress towards higher life forms = Optimization according to some fitness-criterion (optimization on a fitness landscape) Basic idea of (rephrased) Darwinism:

Evolutionary ComputingIntroduction11 Darwinian evolution Fitness: capability of an individual to survive and reproduce in an environment Adaptation: the state of being and process of becoming suitable w.r.t. the environment Natural selection: reproductive advantage by adaptation to an environment (survival of the fittest) Phenotypic variability: small, random, apparently undirected deviation of offspring from parents Note: fitness in natural evolution is a derived, secondary measure, i.e., we (humans) assign a high fitness to individuals with many offspring.

Evolutionary ComputingIntroduction12 Adaptive landscape metaphor (S. Wright, 1932) Example landscape for two traits

Evolutionary ComputingIntroduction13 Adaptive landscape metaphor (cont’d) Selection “pushes” population up the landscape Genetic drift: random variations in feature distribution (+ or -) by sampling error Genetic drift can cause the population “melt down” hills, thus crossing valleys and leaving local optima

Evolutionary ComputingIntroduction14 Natural Genetics The information required to build a living organism is coded in the DNA of that organism Genotype (DNA inside) determines phenotype (outside) Genes   phenotypic traits is a many-to-many mapping Small changes in the genetic material give rise to small changes in the organism (e.g., height, hair colour) Within a species, most of the genetic material is the same

Evolutionary ComputingIntroduction15 The DNA Building plan of living beings: DNA Alphabet: Nucleotide bases purines A,G; pyrimidines T,C Structure: Double-helix spiral Information is redundant, purines complement pyrimidines and the DNA contains much rubbish

Evolutionary ComputingIntroduction16 Genetic code Nucleotide base triplets (codons) Amino acids mapping For all natural life on earth, the genetic code is the same !

Evolutionary ComputingIntroduction17 Transcription, translation DNA RNA PROTEIN transcriptiontranslation A central claim in molecular genetics: only one way flow Genotype Phenotype Lamarckism (saying that acquired features can be inherited) is thus wrong!

Evolutionary ComputingIntroduction18 Example: Homo Sapiens Human DNA is organised into chromosomes Human body cells contains 23 pairs of chromosomes which together define the physical attributes of the individual:

Evolutionary ComputingIntroduction19 Reproductive Cells Gametes (sperm and egg cells) contain 23 individual chromosomes rather than 23 pairs Gametes are formed by a special form of cell splitting called meiosis During meiosis the pairs of chromosome undergo an operation called crossing-over

Evolutionary ComputingIntroduction20 Crossing-over during meiosis Chromosome pairs link up and swap parts of themselves: Before After After crossing-over one of each pair goes into each gamete

Evolutionary ComputingIntroduction21 Fertilisation Sperm cell from Father Egg cell from Mother New person cell

Evolutionary ComputingIntroduction22 Mutation Occasionally some of the genetic material changes very slightly during this process (replication error) This means that the child might have genetic material information not inherited from either parent This can be –catastrophic: offspring in not viable (most likely) –neutral: new feature not influences fitness –advantageous: strong new feature occurs

Evolutionary ComputingIntroduction23 Genetic information vocabulary & hierarchy NameFunction#/Organism NucleotideBasic symbol4 CodonUnit encoding an amino acid64 GeneUnit encoding a protein1000’s ChromosomeMeiotic unitfew GenomeTotal information of the organism1

Evolutionary ComputingIntroduction24 Motivation for EC 1 Nature has always served as a source of inspiration for engineers and scientists The best problem solver known in nature is: –the (human) brain that created “the wheel, New York, wars and so on” (after Douglas Adams’ Hitch- Hikers Guide) –the evolution mechanism that created the human brain (after Darwin’s Origin of Species) Answer 1  neurocomputing Answer 2  evolutionary computing

Evolutionary ComputingIntroduction25 Fact 1 Developing, analyzing, applying problem solving methods a.k.a. algorithms is a central theme in mathematics and computer science Fact 2 Time for thorough problem analysis decreases Complexity of problems to be solved increases Motivation for EC 2 Consequence: Robust problem solving technology needed

Evolutionary ComputingIntroduction26 Classification of problem (application) types

Evolutionary ComputingIntroduction27 What can EC do optimization e.g. time tables for university, call center, or hospital design (special type of optimization?) e.g., jet engine nozzle, satellite boom modeling e.g. profile of good bank customer, or poisonous drug simulation e.g. artificial life, evolutionary economy, artificial societies entertainment / art e.g., the Escher evolver

Evolutionary ComputingIntroduction28 Illustration in optimization: university timetabling Enormously big search space Timetables must be good “Good” is defined by a number of competing criteria Timetables must be feasible Vast majority of search space is infeasible

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Evolutionary ComputingIntroduction30 Illustration in design: NASA satellite structure Optimized satellite designs to maximize vibration isolation Evolving: design structures Fitness: vibration resistance Evolutionary “creativity”

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Evolutionary ComputingIntroduction32 Illustration in modelling: loan applicant creditibility British bank evolved creditability model to predict loan paying behavior of new applicants Evolving: prediction models Fitness: model accuracy on historical data Evolutionary machine learning

Evolutionary ComputingIntroduction33 Illustration in simulation: evolving artificial societies Simulating trade, economic competition, etc. to calibrate models Use models to optimize strategies and policies Evolutionary economy Survival of the fittest is universal (big/small fish)

Evolutionary ComputingIntroduction34 Incest prevention keeps evolution from rapid degeneration (we knew this) Multi-parent reproduction, makes evolution more efficient (this does not exist on Earth in carbon) 2 nd sample of Life Illustration in simulation 2: biological interpetations Picture censored

Evolutionary ComputingIntroduction35 Illustration in art: the Escher evolver City Museum The Hague (NL) Escher Exhibition (2000) Real Eschers + computer images on flat screens Evolving: population of pic’s Fitness: visitors’ votes (inter- active subjective selection) Evolution for the people

Evolutionary ComputingIntroduction36 Demonstration: magic square Given a 10x10 grid with a small 3x3 square in it Problem: arrange the numbers on the grid such that –all horizontal, vertical, diagonal sums are equal (505) –a small 3x3 square forms a solution for 1-9

Evolutionary ComputingIntroduction37 Demonstration: magic square Evolutionary approach to solving this puzzle: Creating random begin arrangement Making N mutants of given arrangement Keeping the mutant (child) with the least error Stopping when error is zero

Evolutionary ComputingIntroduction38 Demonstration: magic square Software by M. Herdy, TU Berlin Interesting parameters: Step1: small mutation, slow & hits the optimum Step10: large mutation, fast & misses (“jumps over” optimum) Mstep: mutation step size modified on-line, fast & hits optimum Start: double-click on icon below Exit: click on TUBerlin logo (top-right)