evolutionary computingintroduction1 introduction, part i positioning of ec and the basic ec metaphor...

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

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

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

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”

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

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

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)

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

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

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:

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.

Evolutionary Computing Introduction 12

Adaptive landscape metaphor (S. Wright, 1932)

Example landscape for two traits

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

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

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

Evolutionary Computing Introduction 16

Genetic code

Nucleotidebase triplets

(codons)

Aminoacids

mapping

For all natural life on earth, the genetic code is the same !

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!

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:

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

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

Evolutionary Computing Introduction 21

Fertilisation

Sperm cell from Father Egg cell from Mother

New person cell

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

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

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

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

Evolutionary Computing Introduction 26

Classification of problem (application) types

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

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

Evolutionary Computing Introduction 29

Evolutionary Computing Introduction 30

Illustration in design: NASA satellite structure

Optimized satellite designs to maximize vibration isolation

Evolving: design structures

Fitness: vibration resistance

Evolutionary “creativity”

Evolutionary Computing Introduction 31

Evolutionary Computing Introduction 32

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 Computing Introduction 33

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 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

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

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

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

Evolutionary Computing Introduction 38

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)