Introduction to Evolutionary Computing I A.E. Eiben Free University Amsterdam http://www.cs.vu.nl/~gusz/ with thanks to the EvoNet Training Committee and its “Flying Circus”
Jan 12, 2016
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”
A.E. Eiben, Introduction to EC I 2 EvoNet Summer School 2002
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)