1. Introduction to Evolutionary Computation Temi avanzati di Intelligenza Artificiale - Lecture 1 Prof. Vincenzo Cutello Department of Mathematics and Computer Science University of Catania

2. Introduction to Evolutionary Computation - Lecture 1 2 Evolution  What is Evolution ?

3. Introduction to Evolutionary Computation - Lecture 1 3 "Disclaimer"  You may whish to treat this as an abstract idea only  It does not matter (in the context of Evolutionary Computation) !

4. Introduction to Evolutionary Computation - Lecture 1 4 Darwinian Evolution  Four Postulates 1. Individuals within species are variable 2. Some of the variations are passed on to offspring 3. In every generation, more offspring are produced than can survive 4. The survival and reproduction of individuals are not random: The individuals who survive and go on to reproduce, or who reproduce the most, are those with the most favourable variations. They are naturally selected. On the Origin of Species by Means of Natural Selection (Darwin 1859)

5. Introduction to Evolutionary Computation - Lecture 1 5 Nature of Natural Selection Based on "Evolutionary Analysis (Freeman & Herron, 2001)"  Natural Evolution acts... On Individuals, but the Consequences occur in the population On Individuals, not groups On Phenotypes, but evolution consist of changes in the Genotype On exixting traits, but can produce new traits  Evolution... Is backward looking Is not perfect Is nonrandom Is not progressive

6. Introduction to Evolutionary Computation - Lecture 1 6 Why are we Interested ?  'Results' of Evolution are 'Creative', 'Surprising', 'Unexpected' 'Highly adapted' to 'Environmental Niches'  God or Evolution ?  Can a program 'create things like this' ?

7. Introduction to Evolutionary Computation - Lecture 1 7 Why are we interested (contd..) ?  Unsupervised !  No 'conscious' design  No knowledge involved  Instead: Reproductive Fitness  But !  Natural Evolution had an extremely long time (3.7 Billion Years!)  Natural Evolution acts in parallel

8. Introduction to Evolutionary Computation - Lecture 1 8 Evolutionary Algorithms  Algorithms that are inspired by natural evolution  Four Main Elements: Group of Individuals - Population Source of Variation - Genetic Operators Reproductive Fitness - Fitness Survival of the Fittest - Selection  Search Process Trial and Error Recipe for chosing next trial

9. Introduction to Evolutionary Computation - Lecture 1 9 EA Examples 1: Optimization  Airfoil Optimization  Other Examples Scheduling Function Optimization Chemical Process Optimization

10. Introduction to Evolutionary Computation - Lecture 1 10 EA Examples 2: Exploration  Evolutionary Art  Other Examples Electronic Hardware Design Robot Control

11. Introduction to Evolutionary Computation - Lecture 1 11 Sex !  Skippers mating, from www.chaparraltree.com/ mn/insects.shtml

12. Introduction to Evolutionary Computation - Lecture 1 12 Some Terms from Genetics  DNA Very large linear self-replicating molecules found in all living cells, the physical carrier of Genetic Information (Deoxyribonucleic Acid)  Chromosome A single, very long molecule of DNA  Gene The basic unit of inheritance, (...) a length of DNA which exerts its influence on an organisms form and function by encoding and directing the synthesis of a protein (...)  Allele One of a number of alternative forms of a gene that can occupy a given genetic locus on a chromosome.

13. Introduction to Evolutionary Computation - Lecture 1 13 Mutation as a Source of Variation  Mitosis: Nuclear division in Cells  Mutations: Errors during Mitosis Point Mutations: simple copy errors - create new alleles Duplication: duplicate stretch of DNA - creates extra genetic material others...  Most Mutations are Neutral !

14. Introduction to Evolutionary Computation - Lecture 1 14 Sexual Reproduction  Additional Steps - Meiosis Combination of chromosome sets from both parents Additional Division

15. Introduction to Evolutionary Computation - Lecture 1 15 Recombination in Sexual Reproduction  Mixing of genetic material Mixing chromosomes Mixing genes on single chromosomes (crossover) Creates new combination of existing alleles  This is why......you can inherit your mother's eyes, and your father's nose  Sexual Reproduction Can combine beneficial mutations that arise in different individuals Can elimiate disadvantageous mutations quickly

16. Introduction to Evolutionary Computation - Lecture 1 16 Other Aspects of Natural Evolution in EC  Punctuated Equilibrium  Viruses  Co-Evolution  Genetic Engineering  Non-Mendelian Inheritance  Dominant and Recessive Genes

17. Introduction to Evolutionary Computation - Lecture 1 17 Course Overview  Part 1: Basics Representations, Selection, Search Operators  Part 2: Other Issues Niching, Co-Evolution, Constraint Handling, Multi- Objective Problems,...  Part 3: Theory Background Knowledge, Basic Results  Throughout: Tutorials Tutorials, Exercices, Demos

18. Introduction to Evolutionary Computation - Lecture 1 18 References and Resouces for this Lecture  Books Hartl, Daniel L. Essential Genetics Jones and Bartlett Publishers, 1996. Introductory genetics text (Barnes Library, q QH 430) (Advanced) Freeman, Scott and Herron, Jon. C. Evolutionary Analysis 2nd edition, Prentice-Hall 2001. Good book on evolution. (Barnes Library, QH366.2) (Advanced) Stearns, Steven C and Hoekstra, Rolf. F. Evolution. An Introduction Oxford University Press, 2000. (Barnes Library, QH366.2) (Advanced) Lawrence, Eleanor Henderson's Dictionary of Biological Terms 10th edn. Longman Scientific and Technical, 1989. For Definitions  Web Resources Introduction to evolutionary Biology (Basic) Introduction to evolutionary Biology  http://www.talkorigins.org/faqs/faq-intro-to-biology.html An Introduction to Genetic Analysis Online Book (Advanced) An Introduction to Genetic Analysis  http://www.ncbi.nlm.nih.gov/books/bv.fcgi?call=bv.View..ShowTOC&rid=iga.T OC