devnology workshop genpro 2 feb 2011

GenPro Genetic Programming made simple

Rob van der Veer

Bluevoid

Bio

Java Development (Web, OSGi, Android, Swing)

Project Leader (o.a. Scrum)

[email protected]

Genpro preamble

GenPro – 2 feb 2011

Automated help: “Grrr”


Settings vs re-programmable logic


AI to the rescue!

Genetic Programming

Genetic Algorithm

Neural networks

Fuzzy logic

Self learning machines

Emergent behavior

Reinforcement learningAutomated logic

Simulated annealing

Bayesian networks

“computer programs are the best representation of computer programs” - John R. Koza -


Genetic algorithm / programming

1. Generate random solutions

2. Evaluate & score (cumulative error => lower is better)

3. Cross and mutate to generate offspring

4. Repeat step 2 and 3 till score is sufficient

41 32 92 71 80 35 23 78 15 42 99 12

37 29 76 57 45 34 21 12 45 8 98 46

generation x

generation x+1

generate

evaluate

evaluate

cross and mutate

cross and mutate


Program representations in GP

• bits• numerical weight vectors• machine / byte code• constructs• concepts?

Which granularity is better?


Genpro birth


One year later: Spreadsheet Inspiration

• Every cell has a value and a type

3 = A2 + B1 2

1 41

• Every cell might have a formula

• Changing the references (mutation!) changes the outcome


One (call)cell, one method call

A GenPro cell holds:• Type• Value• Target-object (or class)• Target method• Parameters

Integer cc1 = Math.max(a, b)


One (call)cell, one method call

Integer cc1 = Math.max(a, b)

cc1• Integer • Value: 56

Math

max (a, b)

int 56

int32

param a

param b


A grid of cells

i1

37

17.4

Input values

1: 113

2: 212

3: 392

4: 752

ExpectedOutput values

1: 45

2: 100

3: 200

4: 400

O1

x

+


Working with GenPro


Using GenPro: extending Trainer

public abstract class Trainer {

public abstract Setup createSetup();

public abstract TestSetSolutionEvaluator createEvaluator();

}

TrainerVisual extends Trainer

-> autostart of Swing UI


Setup

Defines: how to create programs and run generations of programs

Parts of Setup:• Input and output cells• Type and maximum nr of cells• Elements (constants, libraries)• Generation size• When to stop• When to store solutions


GridSolutionEvaluator extends TestSetSolutionEvaluator

Defines how to evaluate and select programs.

public TestSet createTestSet();

public double scoreOutput(ReferenceCell outputCell, Object calculated, Object expected);

public double scoreGrid(Grid g);

public double scoreGridException(Throwable t);


TestSet

Defines the inputs and outputs values to evaluate a program.

Is actually a list of cellnames with sets of values.

public TestSet(Setup setup, String... cellNames)

public void addCellValues(final Object... objects)


First testcase: exact formula

Convert Fahrenheit to Celsius• input: Fahrenheit• output: Celsius• generates exact formula (score goes to 0)


GenPro inner working


Creating a random solution

Lib1Math

input1Integer

c3Integer

compareTo(Integer)

output1Integer

abs(int)c1

Integer

c2Integer

round(double)

junk DNA, aka intron

Integer c1 = Math.abs(input1);

Integer c2 = Math.round(c1);

Integer c3 = c2.compareTo(c1);

return c2;}

import java.lang.Math;

public Integer calc(Integer input1) {

ineffective DNA

Math.cos(…)Math.abs(…)…

input1

Call targets

Param values

Math.cos(…)Math.abs(…)c1.compareTo…

input1c1input1c1c2

input1c1c2c3


The heart of the machine: Method.invoke()

public void calc() {

// fetch parameter values Object[] paramObjects = new Object[paramCells.length];for (int i = 0; i < paramCells.length; i++) { paramObjects[i] = paramCells[i].getValue();}

// execute method on target object/class Object val = targetMethod.invoke(targetCell.getValue(),

paramObjects);// store valuesetValue(val);

}


Why reflection?

• Strong typed connecting• No compile time ( = faster @ small testsets )

• Easy extension, just add POJO’s• Use existing libraries• Reduces learning curve for user• Convertible to working Java code


Operators / libraries

public class NumberOperations {

public static double multiply(double a, double b) { return a * b; } public static double divide(double a, double b) { return a / b; } …

}

Lib


Back to reality ;)


Egg Boiling!


Second testcase: non-exact formula

Eggweight• input: width & height of egg• output: weight of egg • added lib: formula for volume of Egg

public class Egg {

public static double volume(double l, double b) {

return (0.6057 - 0.0018 * b) * l * b * b;

}

}


Search Space size

• Nr of cells• Nr of operators/methods• Nr of parameters• Nr of data types


Third Test case: search space

• Exact formula (score=0)• F(y)=x4+x3+x2-x


Forth Test case: Roman Figures

• Exact formula (score=0)• Strings to Integers• Does not give a correct solution yet.


Genpro current state


Program representations

• Tree based GP (TGP)• Linear based GP (LGP)• Graph based GP (GGP)

• OO based GP (OOGP)• Reflection based GP

Tree example

// 4 registers in rdouble lgp ( double[] r ) { r[0] = r[3] + 43 r[2] = r[1] + r[0] if( [r2] > 2) r[3] = sin ( r [2] ) r[0] = r[2] + r[3] return r[0];}

Linear example


Runtime Requirements

• GenPro Core: Java 1.5 or Android 1.0• GenPro Examples: Java 1.5 (Swing)• GenPro Android Example: Android 1.5

• Generated programs: Java 1.0, Android 1.0 or J2ME (CLDC 1.0)


Features

• Supported cell types: Integer, Double, Boolean, String

• Operators: – all numeric (+,-,/,*,%),

– boolean (&, ||, !),

– logic operators: none

• Constructs: if, switch (Integer & Double)


Wanted Features

• Cell type mutation (keep the if-cells!)• Runtime compiling of programs• Support for void (state-full objects!),

enumerations and arrays• enumeration switch• for (and while?) loop• support for tournaments (eg: Tic tact toe,

Robocode)


GenPro challenges you!

Real world application of GenPro• 2 days of free help getting your problem

solved.

More developers! • Join the architecture meeting!• Solve the multithreading bug!


Links

GenPro code: code.google.com/p/genpro

GenPro on twitter: @bluevoid

Groups: groups.google.com/group/genpro-gp

A scientists view on reflective GP: algoval.essex.ac.uk/rep/oogp/ReflectionBasedGP.pdf

Koza’s view on GP:

www.genetic-programming.com/sevendiffs.html

Contact Rob: [email protected]


Genpro, the origin of species (not covered on feb 2!)


Example Solution structure

cc2Integer

cc3Integer

Lib1Math

Input1Integer

abs(int)

round(double)

compareTo(Integer)

Output1Integer

cc1Integer

parametersMethod calls


Crossing of solutions

Int

Int

Math Int

Int

Int

Solution A

Int

Int

Math Int

Int

Int

Solution B

crossoverpoint


Creating children of A & B

Int

Int

Int

Int

Parent A

Int

Int

Int

Int

Parent B

Int

Int

Int

Int

Int

Int

Int

Int

Child A~B Child B~A

crossoverpoint


A2

B1

B2

A1

Crossover Reconnecting

Int

Int

Math Int

Int

Int

Child A~B

Int

Int

Math Int

Int

Int

Child B~A


Example Solution structure 2

cc2Integer

cc3Boolean

Lib1Math

Input1Integer

abs(int)

Output1Integer

cc1Double

in code:

setup.setCallCells(3, "cc", Double.class, Integer.class, Boolean.class );


Crossover on mixed value types

A2

B1

Int

Int

Math Int

Int

Dbl

Child B~A

compareTo(Integer)

Mismatch in param type!

Potential mismatch in return type and method call


Crossover in nature: the origin of species

Heart

Lung

“DNA” of Horse

Heart

Lung

Brain

“DNA” of Dog

cross over pointsdo not match!


Solving the species problem

1. prevent mismatch by pre-typing the solution template - specie-fy

2. use converters - Heart-brain bypass?

3. on misconnect: no child - allow specie groups as in nature

4. random reconnect - create new species


Crossover on mixed value types:random reconnect

A2

B1

Int

Int

Math Int

Int

Dbl

Child B~A

compareTo(Integer)

Moved connection


Here be dragons

devnology workshop genpro 2 feb 2011

Education

b cc1 integer value

absinput1 integer c2

param b

genpro genetic programming

returna b

genpro cell

roundc1 integer c3

public void calc