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Transcript
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.
3.
4.
Individuals within species are variable
Some of the variations are passed on to offspring
In every generation, more offspring are produced than can survive
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)"

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
EAAirfoil
Examples
1:
Optimization
Optimization


Other Examples



Scheduling
Function Optimization
Chemical Process Optimization
Introduction to Evolutionary Computation Lecture 1
9
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


Chromosome


A single, very long molecule of DNA
Gene


Very large linear self-replicating molecules found in all living cells,
the physical carrier of Genetic Information (Deoxyribonucleic Acid)
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
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




This is why...


Mixing chromosomes
Mixing genes on single chromosomes (crossover)
Creates new combination of existing alleles
...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


Part 2: Other Issues


Niching, Co-Evolution, Constraint Handling, MultiObjective Problems, ...
Part 3: Theory


Representations, Selection, Search Operators
Background Knowledge, Basic Results
Throughout: Tutorials

Tutorials, Exercices, Demos
Introduction to Evolutionary Computation Lecture 1
17
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)


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