Introduction toEvolutionary Computing I

A.E. EibenFree University Amsterdam

http://www.cs.vu.nl/~gusz/

with thanks to the EvoNet Training Committee and its “Flying Circus”

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Contents

Historical perspective Biological inspiration:

Darwinian evolution (simplified!) Genetics (simplified!)

Motivation for EC The basic EC Metaphor What can EC do: examples of application areas Demo: evolutionary magic square solver

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Fathers of evolutionary computing

Alan Mathison Turing 1912 – 1954 “Father of the computer”

Charles Darwin 1809 – 1882“Father of the evolution theory”

John von Neumann 1903 – 1957“Father of the computer”

Gregor Mendel 1822 – 1884“Father of genetics”

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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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Since then…

• 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)

And today:• 3 major conferences, 10 – 15 small / related 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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Natural Evolution

Given a population of reproducing individuals

Fitness: capability of an individual to survive and reproduce in an environment (caveat: “inverse” measure)

Phenotypic variability: small, random, apparently undirected deviation of offspring from parents

Natural selection: reproductive advantage by being well-suited to an environment (survival of the fittest)

Adaptation: the state of being and process of becoming suitable w.r.t. the environment

Evolution 1: Open-ended adaptation in a dynamically changing world

Evolution 2: Optimization according to some fitness-criterion

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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) Small variations in the genetic material give rise to

small variations in phenotypes (e.g., height, eye color) Genetic differences between parents and children are

due to mutations/recombinations

Fact 1: For all natural life on earth, the genetic code is the same

Fact 2: No information transfer from phenotype to genotype (Lamarckism wrong)

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Motivation for EC

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

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Motivation for EC 2

Developing, analyzing, applying problem solving methods a.k.a. algorithms is a central theme in mathematics and computer science

Time for thorough problem analysis decreases Complexity of problems to be solved increases Consequence: robust problem solving technology

needed

Assumption:Natural evolution is robust simulated evolution is robust

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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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Classification of problem types

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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, and good is defined by a number of competing criteria

Timetables must be feasible and the vast majority of search space is infeasible

NB Example from Napier Univ

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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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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)