lecture 16 - Informatics: Indiana University · Cell division Meiosis Diploid cell's genome is...

[email protected]://informatics.indiana.edu/rocha/i-bic

biologicallyInspired

computing

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Informatics luis rocha 2015

!!!S S np21 S

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Development

Syntactic Operations

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biologically-inspired computinglecture 16


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course outlook

Assignments: 35% Students will complete 4/5 assignments based on

algorithms presented in class Lab meets in I1 (West) 109 on Lab Wednesdays

Lab 0 : January 14th (completed) Introduction to Python (No Assignment)

Lab 1 : January 28th

Measuring Information (Assignment 1) Graded

Lab 2 : February 11th

L-Systems (Assignment 2) Graded

Lab 3: March 25th

Cellular Automata & Boolean Networks (Assignment 3) Due: April 1st

Lab 4: April 8th

Genetic Algorithms (Assignment 4) Due: April 22nd

Sections I485/H400


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Readings until now

Class Book Nunes de Castro, Leandro [2006]. Fundamentals of Natural

Computing: Basic Concepts, Algorithms, and Applications. Chapman & Hall. Chapters 1, 2, 3.1-3.4, 7.1-7.5, 8.1-8.2, 8.3.10

Lecture notes Chapter 1: “What is Life?” Chapter 2: “The Logical Mechanisms of Life” Chapter 3: “Formalizing and Modeling the World” Chapter 4: “Self-Organization and Emergent

Complex Behavior” posted online @ http://informatics.indiana.edu/rocha/i-

bic Other materials

Flake’s [1998], The Computational Beauty of Nature. MIT Press Chapters 20


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final project schedule

Projects Due by May 4th in Oncourse

ALIFE 15 (14) Actual conference due date: 2016 http://blogs.cornell.edu/alife14nyc/

8 pages (LNCS proceedings format) http://www.springer.com/computer/lncs?SGWI

D=0-164-6-793341-0 Preliminary ideas overdue!

Individual or group With very definite tasks assigned per

member of group

ALIFE 15


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the genetic code at work

P Reproduction< DNA Polymerase

P Transcription< RNA Polymerase

P Translation< Ribosome

P Coupling of AA’s toadaptors< Aminoacyl Synthetase

Figures from Eigen [1992] . Steps Towards Life.

DNA Replication/Synthesis

Transcription

Translation


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genetic information at workComplexity galore

Many intermediate levels subject to control Development, environment, epigenetic, operational


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genetic information at workregulatory networks

Expression of genes controlled by the products of other genes (proteins and RNAs)

Regulatory networks whose (sel-organizing) dynamics are modulated by various factors, inlcuidng information stored in DNA tape

Gene regulatory network controlling early Arabidopsis flower development (F. Welmer et al 2006, PLOSGEN)


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Cell division

Meiosis Diploid cell's genome is

replicated once and split twice

produces four haploid (germ) cells each with half the chromosomes

Sexual reproduction combines germ cells from two individuals to produce diploid (zygocyte) cells

Vertical genetic information transmission Offspring with a new

genotype Mitosis

Eukaryotic cell separates its duplicated genotype into two identical halves somatic cells in

multicellular organisms Horizontal genetic

information expression development

Crossovers may occur in meiosis

Genetic information transmission



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genetic information at workgenotype/phenotype

GenotypeDNA

RNAtranscription

translation(code)

amino acidchains

Development, regulation

phenotypeorganism

environmental ramifications

mitosis

mei

osis

repl

icat

ion

Germ cell line

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Can this be generalized any further?


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modeling genetic-based (open-ended) evolution

Evolutionary Operation Box (1957)

Perturbations to continuous variables followed by selection to improve industrial productivity

Evolution Strategies Rechenberg (1960’s), Schweffel (1970’s)

To optimize real-valued parameters in wind-tunnel experiments Real-valued genotypes under variation and selection

Evolutionary Programming Fogel, Owens, and Walsh (1966)

Evolution of tables of state-transition functions (diagrams) under mutation and selection

Artificial ecosystems Conrad and Pattee (1970)

Population of artificial cells evolving with genotype and phenotype

Other early evolution-inspired algorithms and models Barricelli CA-like model(1957), game-strategy model (1963)

Symbiogenetic evolution Friedman (1957, 1959), Bledsoe (1961), Bremmermann

(1962) Genetic Algorithms

John Holland (1960’s and 1970’s) Adaptation in Natural and Artificial Systems, University of

Michigan Press, 1975. (MIT Press, second edition 1992)

History of evolutionary computation


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optimization

Search algorithms based on the mechanics of Natural Selection Holland, Conrad, Fogel Based on distinction between a machine and a description of a

machine Solution alternatives for optimization problems

0dxdf

0dxdf

f

x

Direct analysis depends onKnowing the functionExistence of derivativescontinuity

“hill-climbing”“hop” on the function and move along the steepest direction until a local extrema is found

Enumerative SearchSearch point by point

Random Searchdirectionless

Objective function

via genetic algorithms



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fitness landscapesexamples


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genetic algorithms

Work with an encoding of the parameter set, not the parameters themselves solution alternative encoded as descriptions

Search a population of points in parallel Not a single point

Uses objective function directly Not derivatives

Uses probabilistic transition rules Statistical bias Not deterministic rules

Advantages Samples the space widely

like an enumerative or random algorithm, but more efficiently Directed search

Not so easily stuck on local extrema Population search Variation mechanisms to search new points



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computational evolutionartificial genotype/phenotype mapping

x x x1 2 np

φCode:

!!!

S S np21 S

Selection

Variation

Genotype

Phenotype

Traditional Genetic Algorithm

011001

code

GenotypeDNA

RNAtranscription

translation(code)

amino acidchains

development

phenotypeorganismenvironmental

ramifications

Inhe

rited

va

riatio

n

Search algorithms based on the mechanics of Natural Selection

Based on distinction between a machine and a description of a machineSolution alternatives for optimization problems


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coding

Solution space encoded as finite-length string over a finite alphabet E.g. {0, 1}

GA’s exploit coding similarities Searches the code space, not the solution space

In genetic algorithms

x x x1 2 np

φCode:

!!!S S np21 S

Selection

Variation

Genotype

Phenotype


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de


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genetic algorithms

1) Generate Random population of bit-strings Chromosomes/Genotypes

• A candidate solution to a problem Genes

Single or short blocks of adjacent bits Allele

Actual value of a gene2) Evaluate Fitness Function for each decoded solution3) Reproduce next generation

Genotypes of solutions with higher fitness value reproduce with higher probability

4) Go back to 2)

x x x1 2 np

φCode:

!!!

S S np21 S

Selection

Variation

Genotype

Phenotype


011001

code

The workings

0 1 1 1 0 1 0 0 1 0

Genes f(xi)


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probabilistic selection

Solution space encoded as finite-length string over a finite alphabet E.g. {0, 1}

GA’s exploit coding similarities Searches the code space, not the solution space

Searches the space with many alternatives in parallel Avoids getting trapped in local optima Higher probability of finding better solutions

Not random search Search towards regions with likely improvement Better solutions reproduce more often

Does not work in very rugged, chaotic, uncorrelated landscapes

biased population generation

x x x1 2 np

φCode:

!!!

S S np21 S

Selection

Variation

Genotype

Phenotype


011001


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reproduction

1) Reproduction: New population is generateda) Selection

Select two parent chromosomes from a population according to their fitness Biased roulette wheel according to fitness of solution Elite group Tournament

x x x1 2 np

φCode:

!!!S S np21 S

Selection

Variation

Genotype

Phenotype


011001co

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selection

f(x1)

f(x2)

f(x3)

0 1 1 1 0 1 0 0 1 0

0 1 0 1 1 1 0 1 0 1


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reproduction


Select two parent chromosomes from a population according to their fitness

b) Variation: Crossover With a crossover probability produce offspring pair by

recombining parents.

Variation: crossover

0 1 1 1 0 1 0 0 1 0

0 1 0 1 1 1 0 1 0 1

Parents

0 1 1 1 0 1 0 0 1 0

0 1 0 1 1 1 0 1 0 1 0 1 1 1 0 1 0 0 1 0

0 1 0 1 1 1 0 1 0 1

0 1 0 1 0 1 0 0 1 0

0 1 1 1 1 1 0 1 0 1


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reproduction


Select two parent chromosomes from a population according to their fitness

b) Variation: Crossover With a crossover probability produce offspring pair by

recombining parents. c) Variation: Mutation

With a mutation probability mutate (bit flip) new offspring at each bit position

Variation: mutation

Parents

0 1 1 1 0 1 0 0 1 0

0 1 0 1 1 1 0 1 0 10 1 1 1 0 1 0 0 1 0

0 1 0 1 1 1 0 1 0 1

0 1 0 1 0 1 0 0 1 0

0 1 1 1 1 1 0 1 0 1

0 1 0 0 0 1 0 0 1 1

0 1 1 1 1 0 0 1 0 1

0 0 1 1 0 1 0 0 1 0

0 1 0 1 0 1 0 1 0 1


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genetic algorithms

1) Generate Random population of bit-strings

2) Evaluate Fitness Function for each decoded solution

3) Reproduce next generation Selection by fitness Variation

crossover and mutation Fill new population

4) Go back to 2) until stop criteria is met Desired fitness Specified number of

generations Convergence

Lack of variability in population and/or fitness Tends to a peak

The workings

f(x1)

f(x2)

f(x3)

0 1 1 1 0 1 0 0 1 0

0 1 1 1 1 0 0 1 1 1

1 0 0 1 0 1 0 1 1 0…

f(xi)

Parents0 1 1 1 0 1 0 0 1 0

0 1 0 1 1 1 0 1 0 1

0 1 1 1 0 1 0 0 1 0

0 1 0 1 1 1 0 1 0 1

0 1 0 1 0 1 0 0 1 0

0 1 1 1 1 1 0 1 0 1

0 1 0 0 0 1 0 0 1 1

0 1 1 1 1 0 0 1 0 1

0 0 1 1 0 1 0 0 1 0

0 1 0 1 0 1 0 1 0 1



computing

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computational evolutionartificial genotype/phenotype mapping

x x x1 2 np

φCode:

!!!

S S np21 S

Selection

Variation

Genotype

Phenotype


011001

code

GenotypeDNA

RNAtranscription

translation(code)

amino acidchains

development

phenotypeorganismenvironmental

ramifications

Inhe

rited

va

riatio

n

Search algorithms based on the mechanics of Natural Selection

Based on distinction between a machine and a description of a machineSolution alternatives for optimization problems


computing


INDIANAUNIVERSITY


Next lectures

Class Book Nunes de Castro, Leandro [2006]. Fundamentals of Natural Computing:

Basic Concepts, Algorithms, and Applications. Chapman & Hall. Chapter 2, 7, 8 Appendix B.3.2-3 - Turing Machines, Computational complexity Chapter 3, all sections Sections 7.8, 8.3.2, 8.3.10

Lecture notes Chapter 1: “What is Life?” Chapter 2: “The logical Mechanisms of Life” Chapter 3: Formalizing and Modeling the World Chapter 4: “Self-Organization and Emergent Complex

Behavior” Chapter 5: “Reality is Stranger than Fiction”

posted online @ http://informatics.indiana.edu/rocha/i-bic Optional materials

Flake’s [1998], The Computational Beauty of Life. MIT Press Chapter 20

Scientific American: Special Issue on the evolution of Evolution, January 2009.

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lecture 16 - Informatics: Indiana University · Cell division Meiosis Diploid cell's genome is...

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