Info atics biologically-inspired computing luisrocha2015 ......biologically Inspired computing...

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biologicallyInspired

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Informatics luis rocha 2015

biologically-inspired computinglecture 18


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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) Graded

Lab 4: April 8th

Genetic Algorithms (Assignment 4) Due: April 22nd

Lab 5: April 22nd

Ant Clustering Algorithm (Assignment 5) Due May 4th

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

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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Kyle Nealyhighlights

Lab 3: CA & BN

Matthew Remmel

Darlan Farias



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highlightsLab 3: CA & BN

Jonathan Stout Rafael Paiva

Lorander Saggu


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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


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Types of encoding

Binary encodings Typically fixed-length

Many-letter encoding Larger alphabet (e.g. graph-generation grammars)

Real-valued encodings Genes take real values

Tree Encodings Genetic programming

Indirect Encodings Modeling Phenotype development or post-

transcription processes L-Systems, Dynamical systems, evolutionary robotics



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Cellular automatahomogenous lattice of state-determined systems

xx-1 x+1

Cellular Automata

xt

Density Task (a.k.a majority classification problem)

#Lattices of 149 Binary Cells (599, 999)#Rules of Radius 3 (7 Cells in

Neighborhood)#Task: Organize to< All 1's if Initial Configuration (IC) has more

1 Cells< All 0's if IC has more 0 Cells

xt

x

12827 NK

Possible neighborhood states

38128 104.32 NKK

Possible CA transition functions



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Cellular automataencoding in GA with binary encoding

xt

x12827 NKPossible neighborhood states

0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0

0 0 0 0 0 1 0 1

0 0 0 0 0 1 1 0

0 0 0 0 1 0 0 1

0 0 0 0 1 0 1 0

0 0 0 0 1 1 0 0

0 0 0 0 1 1 1 0

0 0 0 1 0 0 0 1

0 0 0 1 0 0 1 0

0 0 0 1 0 1 0 1

0 0 0 1 0 1 1 1

0 0 0 1 1 0 0 0

0 0 0 1 1 0 1 0………

0

0

1

0

1

0

0

0

1

0

1

1

0

0

010010101100100

010010101100100

010010101100100

010010101100100

010010101100100

Used in the evolutionary search by GA (elite selection)

x x x1 2 np

φCode:

!!!

S S np21 S

Selection

Variation

Genotype

Phenotype

Traditional Genetic Algorithm

011001

code

Pop of rules



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Evolving CA rulesWith genetic algorithms

#Das, Mitchell and Crutchfield< Used Genetic Algorithm to

evolve rules for this task

Das,R., Mitchell,M., Crutchfield,J.P., [1994]. "A genetic algorithmdiscovers particle-based computation in cellular automata". In:Parallel Problem Solving from Nature - PPSN III. Davidor,Y.,

Schwefel,H.-P., Manner,R. (Eds.), Springer-Verlag, pp. 344-353.

Typical Result:Block Expansion

Regulardomains

{1+}

{10+}

{0+}

Particles



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to evolve photos with numerical encodingsevolutionary algorithms


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real and integer encoding

1) Genotypes contain real or integer values1) Crossover is performed in the same way2) Mutation assigns a random number in a given interval

2) More computationally demanding for Reals3) Attention to crossover points

1) Conversion to binary avoids crossover issues, but longer genotypes

In genetic algorithms

0.3 1.7 3.8 1.7 6 1.2 3.2 6.4 2.8 0

0.3 1.7 3.8 1.7 2.9 1.2 3.2 6.4 2.8 0

n Genes

54 108 25 201 11 192

x y r R G B254 18 100 1 141 12 254 18 100 1 141 12…

Agent Chromosome/Genotype (Population of p agents)

n circles


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evolving computer programs

Fogel, Owens and Walsh (1966) Artificial Intelligence through simulated evolution. Wiley.

Evolution of finite-state machines

John Koza (1992) at Stanford University Genetic Programming: On the programming of

computers by means of Natural Selection. MIT Press.

Tree encodings (no clear genotype)


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genetic programming

1) Generate Random population of tree/programs

2) Evaluate Fitness Function for each program

Desired I/O, simplicity, speed3) 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

The workings

f(x1)

f(x2)

f(x3)

…

f(xi)

*

PI

R

R

-

* √

C

R*[(PI*C)-√R]

+

R

D

*

√

PI

R+(√PI*D)

/

PI

PI

PI

/

/ /

PI PI

+

R

D

*

√

PI

/

PI D

PIPI PI

/

/ /

PI

+

R *

√

PI



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A symbolic regression toolEureqa

Eureqa: http://ccsl.mae.cornell.edu/eureqa


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Gene expression programming

Proposed by Candida Ferreira Program trees are encoded in fixed-length linear genotypes Genotypes

Open-reading frame architecture Stop signal not necessarily at end of genotype

Non-coding genes are possible Can include genetic operators

Genes contain two types of symbols Functions (only at the head) and terminals

Multigenic solutions Assembled from non-coding operations between various open-reading frames

Including a genotype/phenotype map in GP

C. FERREIRA [2001]. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems, 13 (2): 87-129.



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evolving morphologies and robots with indirect encodingsKarl Sim´s simulations and The Golem Project

http://demo.cs.brandeis.edu/golem/

http://www.youtube.com/watch?v=oCXzcPNsqGA



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evolutionary designobjective function may be subjective

"Once a Darwinian process gets going in a world, it has an open-ended power to generate surprising consequences: us, for example" Richard Dawkins

Biomorphs


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Next lectures

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

Basic Concepts, Algorithms, and Applications. Chapman & Hall. Chapter 3, all sections Sections 7.8 (evolving L-Systems), 8.3.2 (biomorphs) Chapter 5, all sections Section 7.7, 8.3.1,8.3.6,8.3.8-10

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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