1. 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”

2. A.E. Eiben, Introduction to EC I2 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

3. A.E. Eiben, Introduction to EC I3 EvoNet Summer School 2002 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”

4. A.E. Eiben, Introduction to EC I4 EvoNet Summer School 2002 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

5. A.E. Eiben, Introduction to EC I5 EvoNet Summer School 2002 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

6. A.E. Eiben, Introduction to EC I6 EvoNet Summer School 2002 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

7. A.E. Eiben, Introduction to EC I7 EvoNet Summer School 2002 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)

8. A.E. Eiben, Introduction to EC I8 EvoNet Summer School 2002 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

9. A.E. Eiben, Introduction to EC I9 EvoNet Summer School 2002 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

10. A.E. Eiben, Introduction to EC I10 EvoNet Summer School 2002 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

11. A.E. Eiben, Introduction to EC I11 EvoNet Summer School 2002 Classification of problem types

12. A.E. Eiben, Introduction to EC I12 EvoNet Summer School 2002 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

13. A.E. Eiben, Introduction to EC I13 EvoNet Summer School 2002 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

14. A.E. Eiben, Introduction to EC I14 EvoNet Summer School 2002 Illustration in design: NASA satellite structure Optimized satellite designs to maximize vibration isolation Evolving: design structures Fitness: vibration resistance Evolutionary “creativity”

15. A.E. Eiben, Introduction to EC I15 EvoNet Summer School 2002

16. A.E. Eiben, Introduction to EC I16 EvoNet Summer School 2002 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

17. A.E. Eiben, Introduction to EC I17 EvoNet Summer School 2002 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)

18. A.E. Eiben, Introduction to EC I18 EvoNet Summer School 2002 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) 2 nd sample of Life Illustration in simulation 2: biological interpetations Picture censored

19. A.E. Eiben, Introduction to EC I19 EvoNet Summer School 2002 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

20. A.E. Eiben, Introduction to EC I20 EvoNet Summer School 2002 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

21. A.E. Eiben, Introduction to EC I21 EvoNet Summer School 2002 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

22. A.E. Eiben, Introduction to EC I22 EvoNet Summer School 2002 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)