lecture 16 - Informatics: Indiana University · Cell division Meiosis Diploid cell's genome is...
Transcript of lecture 16 - Informatics: Indiana University · Cell division Meiosis Diploid cell's genome is...
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!!!S S np21 S
x x x1 2 np
NCode:
Development
Syntactic Operations
al
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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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riatio
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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
Traditional Genetic Algorithm
011001co
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
Traditional Genetic Algorithm
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
Traditional Genetic Algorithm
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
Traditional Genetic Algorithm
011001co
de
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
1) Reproduction: New population is generateda) Selection
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
1) Reproduction: New population is generateda) Selection
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
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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
biologicallyInspired
computing
[email protected]://informatics.indiana.edu/rocha/i-bic
INDIANAUNIVERSITY
Informatics luis rocha 2015
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.
readings