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

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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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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: 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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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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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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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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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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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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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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Adaptive landscape metaphor (S. Wright, 1932)

Example landscape for two traits

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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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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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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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Genetic code

Nucleotidebase triplets

(codons)

Aminoacids

mapping

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

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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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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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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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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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Fertilisation

Sperm cell from Father Egg cell from Mother

New person cell

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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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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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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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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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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

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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 Computing Introduction 30

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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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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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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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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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)