Transcript Evolutionary Computation

Evolutionary Computation
Module for HON207
02/03/07
DePaul University, HON207
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Introduction
This lecture is an introduction to the
topic of Evolutionary Computation, but
it is not a substitute for the assigned
readings
 We will review key concepts, and we
will introduce a few simple models in R

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DePaul University, HON207
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Topics
What is Evolutionary Computation
(EC)?
 Basic principles
 Fitness
 Simple models of asexual reproduction
 Description of the R programs needed
for the assignments

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DePaul University, HON207
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What is Evolutionary
Computation (EC)?

EC is an emerging field that focuses
on the design and application of
computational models inspired in
Darwinian-like formulations.
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The goals of EC
Developing and understanding better
models of natural evolution
 Engineers try to use evolution
principles to build artifacts (e.g., a
stock trading tool)
 Artificial-life researchers experimenting
with artificial evolutionary worlds

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Basic Concepts
Evolution in a Darwinian sense.
 Individuals in Population(s) compete
for limited resources.
 Dynamically changing populations due
to death/birth
 Offspring resembling parent but not
identical

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DePaul University, HON207
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Genotype / Phenotype
The genotype is the specific genetic
makeup (the specific genome) of an
individual, in the form of DNA.
 The phenotype of an individual
organism is either its total physical
appearance and constitution or a
specific manifestation of a trait.
 For our purpose, we will assume a
one-to-one correspondence between
the two.

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DePaul University, HON207
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Gene/Pheno Space

The geno/pheno space is a vector that
specifies the genetic makeup of an
individual.
trait1, trait2, trait3, …, traitN >
 <hair color, eye color, height, …etc>
<

In Genetics, an allele is any one of a
number of viable DNA codings.
 In
other words, the possible values for the
elements in the vector (e.g., a number
between 0 and 5)
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DePaul University, HON207
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Fitness
In biology fitness is, in general, an ex
post facto measure based on the
individual’s ability to survive and
reproduce. This could be related to the
environment, the make up of the
population, etc.
 In our simplified models, fitness will be
a function solely of individual’s
geno/phenotype.

 e.g.,
02/03/07
f(x)=50-x2
DePaul University, HON207
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Mutation
In biology mutations are changes to
the genetic material
 We need to make some assumptions
on how likely mutations would be in
our model, and how would those
mutations be implemented

 If
there are L genes, we assume a
probability of mutation = 1/L
 The mutation would be the value in the
gene inherited from the parent 
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DePaul University, HON207
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First simulation
1.3 EV on a Simple Fitness Landscape
(page 6)
 Fitness function f(x)=50-x2
 1 trait
 Mutation: change by  = 1
 Population = 10
 100 generations

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DePaul University, HON207
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First simulation


1.3 EV on a Simple Fitness Landscape (page 6)
Fitness function f(x)=50-x2
1 trait
Mutation: change by  = 1
Population = 10
100 generations

We have a function in R that can do this:





EC(M,L,generations,lb,ub,formula,mutrule,
Delta,fix)
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DePaul University, HON207
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