Introduction to Evolutionary Computing I

1. Introduction toEvolutionary 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”

2. 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 A.E. Eiben,Introduction to EC I 2EvoNet Summer School 2002

3. 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” Fathers of evolutionary computing A.E. Eiben,Introduction to EC I 3EvoNet Summer School 2002

4. 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 A.E. Eiben,Introduction to EC I 4EvoNet Summer School 2002

5. 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 A.E. Eiben,Introduction to EC I 5EvoNet Summer School 2002

6. NaturalEvolution • 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 A.E. Eiben,Introduction to EC I 6EvoNet Summer School 2002

7. 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) A.E. Eiben,Introduction to EC I 7EvoNet Summer School 2002

8. 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 A.E. Eiben,Introduction to EC I 8EvoNet Summer School 2002

9. 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 A.E. Eiben,Introduction to EC I 9EvoNet Summer School 2002

10. EVOLUTION Environment Individual Fitness PROBLEM SOLVING Problem Candidate Solution Quality Evolutionary Computing: the Basic Metaphor Fitness  chances for survival and reproduction Quality  chance for seeding new solutions A.E. Eiben,Introduction to EC I 10EvoNet Summer School 2002

11. Classification of problem types A.E. Eiben,Introduction to EC I 11EvoNet Summer School 2002

12. 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 A.E. Eiben,Introduction to EC I 12EvoNet Summer School 2002

13. 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 A.E. Eiben,Introduction to EC I 13EvoNet Summer School 2002

14. Illustration in design: NASA satellite structure Optimized satellite designs to maximize vibration isolation Evolving: design structures Fitness: vibration resistance Evolutionary “creativity” A.E. Eiben,Introduction to EC I 14EvoNet Summer School 2002

15. A.E. Eiben,Introduction to EC I 15EvoNet Summer School 2002

16. 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 A.E. Eiben,Introduction to EC I 16EvoNet Summer School 2002

17. 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) A.E. Eiben,Introduction to EC I 17EvoNet Summer School 2002

18. Illustration in simulation 2:biological interpetations 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 Picture censored A.E. Eiben,Introduction to EC I 18EvoNet Summer School 2002

19. 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 A.E. Eiben,Introduction to EC I 19EvoNet Summer School 2002

20. 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 A.E. Eiben,Introduction to EC I 20EvoNet Summer School 2002

21. Demonstration: magic square 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 A.E. Eiben,Introduction to EC I 21EvoNet Summer School 2002

22. Demonstration: magic square • 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) A.E. Eiben,Introduction to EC I 22EvoNet Summer School 2002