1. Evolutionary Computation (P. Koumoutsakos) 1 What is Life  Key point : Ability to reproduce.  Are computer programs alive ? Are viruses a form of life ?  Key point : Interelatedness  All living organisms are related to one another - common ancestor  BUT  Organisms come to differ from one another in function, form and complexity through  EVOLUTION

2. Evolutionary Computation (P. Koumoutsakos) 2 Evolution - Components  Inheritance : passing of characteristics from parent to offspring  Variation/Mutation : offsprings are not exact copies of parents  Selection : Differential favoring of some organisms over others

3. Evolutionary Computation (P. Koumoutsakos) 3 Life & Evolution  Life : an evolutionary process on earth  Evolution : Helps our understanding of what is important in life and how living systems come to function  Case Study : Why living organisms do not have (m)any metallic parts ?

4. Evolutionary Computation (P. Koumoutsakos) 4 Evolution and Scientific Inquiry Ø There is a grandeur in this view of life, with its several powers, having been originally breathed into a few forms or into one; and that, whilst this planet has gone cycling on according to the fixed law of gravity, from so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved… Ø …I have called this principle, by which each slight variation, if useful, is preserved, by the term Natural Selection. Charles Darwin,- “The origin of species”

5. Evolutionary Computation (P. Koumoutsakos) 5 Evolution & Optimization  Evolution : Survival of the “fittest “ in a given environment.  Optimization : Identify the best possible design given a certain environment and initial conditions. Organisms evolve via inheritance, recombinantion, mutation, selection by the Environment. Parameters of a design/function are evolved via inheritance, recombinantion, mutation, selection so as to optimize a cost function.

6. Evolutionary Computation (P. Koumoutsakos) 6 Evolution & Optimization Organisms inheritance, recombinantion, mutation, Selection Environment. Parameters inheritance, recombinantion, mutation, Selection Cost Function

7. Evolutionary Computation (P. Koumoutsakos) 7 Why Evolutionary Computation ? Biomimetics vs. Evolutionary Design : Instead of imitating the final product of biological systems, imitate the process by which they are designed.. Design must be environment and initial conditions specific.

8. Evolutionary Computation (P. Koumoutsakos) 8 Why Evolutionary Computation ? Evolution, Biology and Artificial Life By imitating evolution we may learn something about natural evolutionary principles As we study individual behavior of members of a population we may learn something about self-organizing principles, a few things about society organizations and possibly a few things about ourselves.

9. Evolutionary Computation (P. Koumoutsakos) 9 Why Evolutionary Computation ? The No-Free Lunch Theorem (Wolpert, McReady 1996) There cannot exist any algorithm for solving all optimization problems that is on average superior to any competitor.

10. Evolutionary Computation (P. Koumoutsakos) 10 Why Evolutionary Computation ? Optimization in an Engineering Environment Automation - Use of commercial codes & empirical formulas Optimizer Empirical formulas Commercial Codes Cost

11. Evolutionary Computation (P. Koumoutsakos) 11 Why Evolutionary Computation ? If there is a traditional method that works do not use EA’s. BUT Linearisation or over-simplification is usually used so that traditional methods are applicable.

12. Evolutionary Computation (P. Koumoutsakos) 12 Why Evolutionary Computation ? Adaptivity in design

13. Evolutionary Computation (P. Koumoutsakos) 13 When Evolutionary Computation ? CPU Knowledge of the problem Neural Networks Experts First Principles EVOLUTIONARY ALGORITHMS GRADIENT ALGORITHMS HYBRIDS ???

14. Evolutionary Computation (P. Koumoutsakos) 14 A Generic Evolution Algorithm Initialise a Population. 1.Compute Fitness of the individuals. 2.Select Parents/Survivors on the basis of Fitness 3.Extend the population by : cloning, mutation, crossover GO TO STEP 1

15. Evolutionary Computation (P. Koumoutsakos) 15 The 1+1 Evolution Strategy The (1+1) - ES I. Rechenberg, 1964 …… Generation 0 Generation 1 Generation 2 Generation 200

16. Evolutionary Computation (P. Koumoutsakos) 16 The 1+1 Evolution Strategy - Examples Function fitting by adjusting the coefficients of polynomials.

17. Evolutionary Computation (P. Koumoutsakos) 17 The 1+1 Evolution Strategy - Examples

18. Evolutionary Computation (P. Koumoutsakos) 18 I. EVOLUTION STRATEGIES B contains information for the evolution path - Correlations of successful mutations - PCA of paths The environment is identified through mutation/success Covariance Matrix Adaptation ES - (N. Hansen)