evolutionary computingintroduction1 introduction, part i positioning of ec and the basic ec metaphor...
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Evolutionary Computing Introduction 1
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
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Evolutionary Computing Introduction 2
Borg Vog on s
Bio top
Art
L ife Scien ces Social Scien ces
M ath em atics Physics
Softw are En g in eerin g
Neural Nets Evo lu tion ary Com pu tin g Fu zzy System s
Com pu tation al In telligence etc
Com pu ter Science etc
Exact Sciences etc
Science Po litics Spo rts etc
Society Ston es & Seas etc
Earth etc
Un iverse
You are here
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Evolutionary Computing Introduction 3
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
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Evolutionary Computing Introduction 4
Fathers of evolutionary computing
Charles Darwin 1809 - 1882“Father of the evolution theory”
Gregor Mendel 1822-1884“Father of genetics”
John von Neumann 1903 –1957“Father of the computer”
Alan Mathison Turing 1912 - 1954 “Father of the computer”
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Evolutionary Computing Introduction 5
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
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Evolutionary Computing Introduction 6
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
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Evolutionary Computing Introduction 7
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)
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Evolutionary Computing Introduction 8
Historical perspective 3: The present of EC
• 3 major conferences, 10 – 15 small ones
• 3 scientific core EC journals + 2 Web-based ones
• 750-1000 papers published in 2001 (estimate)
• EvoNet has over 150 member institutes
• uncountable (meaning: many) applications
• uncountable (meaning: ?) consultancy and R&D firms
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Evolutionary Computing Introduction 9
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
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Evolutionary Computing Introduction 10
Darwinism
Evolutionary progress towards higher life forms =
Optimization according to some fitness-criterion(optimization on a fitness landscape)
Basic idea of (rephrased) Darwinism:
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Evolutionary Computing Introduction 11
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.
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Evolutionary Computing Introduction 12
Adaptive landscape metaphor (S. Wright, 1932)
Example landscape for two traits
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Evolutionary Computing Introduction 13
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
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Evolutionary Computing Introduction 14
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
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Evolutionary Computing Introduction 15
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
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Evolutionary Computing Introduction 16
Genetic code
Nucleotidebase triplets
(codons)
Aminoacids
mapping
For all natural life on earth, the genetic code is the same !
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Evolutionary Computing Introduction 17
Transcription, translation
DNA RNA PROTEINtranscription translation
A central claim in molecular genetics: only one way flow
Genotype Phenotype
Genotype Phenotype
Lamarckism (saying that acquired features can be inherited) is thus wrong!
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Evolutionary Computing Introduction 18
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:
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Evolutionary Computing Introduction 19
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
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Evolutionary Computing Introduction 20
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
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Evolutionary Computing Introduction 21
Fertilisation
Sperm cell from Father Egg cell from Mother
New person cell
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Evolutionary Computing Introduction 22
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
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Evolutionary Computing Introduction 23
Genetic information vocabulary & hierarchy
Name Function #/Organism
Nucleotide Basic symbol 4
Codon Unit encoding an amino acid 64
Gene Unit encoding a protein 1000’s
Chromosome Meiotic unit few
Genome Total information of the organism 1
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Evolutionary Computing Introduction 24
Motivation for EC 1Nature has always served as a source ofinspiration 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 neurocomputingAnswer 2 evolutionary computing
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Evolutionary Computing Introduction 25
Fact 1Developing, 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
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Evolutionary Computing Introduction 26
Classification of problem (application) types
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Evolutionary Computing Introduction 27
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
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Evolutionary Computing Introduction 28
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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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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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
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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)
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Evolutionary Computing Introduction 34
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)
2nd sample of Life
Illustration in simulation 2:biological interpetations
Pictu
re c
enso
red
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Evolutionary Computing Introduction 35
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
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Evolutionary Computing Introduction 36
Demonstration: magic square
• Given a 10x10 grid with a small 3x3 square in it• Problem: arrange the numbers 1-100 on the
grid such that – all horizontal, vertical, diagonal sums are
equal (505)– a small 3x3 square forms a solution for 1-9
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Evolutionary Computing Introduction 37
Demonstration: magic square
Evolutionary approach to solving this puzzle: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
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Demonstration: magic square
Application
• 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)