Introduction to Evolutionary Computation

Temi avanzati di Intelligenza Artificiale - Lecture 1 Prof. Vincenzo Cutello

Department of Mathematics and Computer Science University of Catania

Evolution

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

"Disclaimer"

 You may whish to treat this as an abstract idea only  It does not matter (in the context of Evolutionary Computation) !

Introduction to Evolutionary Computation Lecture 1 3

Darwinian Evolution

 Four Postulates 1.

2.

Individuals within species are variable 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) Introduction to Evolutionary Computation Lecture 1 4

Nature of Natural Selection

Based on "Evolutionary Analysis (Freeman & Herron, 2001)"

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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 Introduction to Evolutionary Computation Lecture 1 5

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' ? Introduction to Evolutionary Computation Lecture 1 6

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 Introduction to Evolutionary Computation Lecture 1 7

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 Introduction to Evolutionary Computation Lecture 1 8

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EA Examples 1: Optimization

Airfoil Optimization  Other Examples   Scheduling Function Optimization  Lecture 1 9

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EA Examples 2: Exploration

Evolutionary Art  Other Examples   Electronic Hardware Design Robot Control Introduction to Evolutionary Computation Lecture 1 10

Sex !

 Skippers mating, from www.chaparraltree.com/ mn/insects.shtml

Introduction to Evolutionary Computation Lecture 1 11

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.

Introduction to Evolutionary Computation Lecture 1 12

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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 !

Introduction to Evolutionary Computation Lecture 1 13

Sexual Reproduction

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

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 Introduction to Evolutionary Computation Lecture 1 15

Other Aspects of Natural Evolution in EC

 Punctuated Equilibrium  Viruses  Co-Evolution  Genetic Engineering  Non-Mendelian Inheritance  Dominant and Recessive Genes Introduction to Evolutionary Computation Lecture 1 16

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 Introduction to Evolutionary Computation Lecture 1 17

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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. Prentice-Hall 2001. Good book on evolution. (Barnes Library, QH366.2) (Advanced)

Evolutionary Analysis

2nd edition,   Stearns, Steven C and Hoekstra, Rolf. F.

Evolution. An Introduction

University Press, 2000. (Barnes Library, QH366.2) (Advanced) Oxford Lawrence, Eleanor

Henderson's Dictionary of Biological Terms

Longman Scientific and Technical, 1989. For Definitions 10th edn. Web Resources  

Introduction to evolutionary Biology (Basic)

 http://www.talkorigins.org/faqs/faq-intro-to-biology.html

An Introduction to Genetic Analysis Online Book (Advanced)

 http://www.ncbi.nlm.nih.gov/books/bv.fcgi?call=bv.View..ShowTOC&rid=iga.T

OC Introduction to Evolutionary Computation Lecture 1 18