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_address]

Genpro preamble

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 learning Automated logic Simulated annealing Bayesian networks “ computer programs are the best representation of computer programs” - John R. Koza -

Representations outside GP
if-then production rules
Horn clauses
decision trees
Bayesian networks
propositional logic
formal grammars
binary decision diagrams
frames
conceptual clusters
concept sets
numerical weight vectors (for neural nets)
vectors of numerical coefficients for polynomials or other fixed expressions (for adaptive systems)
genetic classifier system rules
fixed tables of values (as in reinforcement learning)
linear chromosome strings (as in the conventional genetic algorithm)

Genetic algorithm / programming
Generate random solutions
Evaluate & score (cumulative error => lower is better)
Cross and mutate to generate offspring
Repeat step 2 and 3 till score is sufficient
generation x generation x+1 generate evaluate evaluate 41 32 92 71 80 35 23 78 15 42 99 12 37 29 76 57 45 34 21 12 45 8 98 46 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 int 32 param a param b

A grid of cells i1 37 17.4 Input values 1: 113 2: 212 3: 392 4: 752 Expected Output 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 Lib1 Math input1 Integer output1 Integer 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) { Math.cos(…) Math.abs(…) … input1 Call targets Param values Math.cos(…) Math.abs(…) c1.compareTo … input1 c1 input1 c1 c2 input1 c1 c2 c3 c3 Integer compareTo(Integer) abs(int) c1 Integer c2 Integer round(double) junk DNA, aka intron ineffective DNA

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 = t argetMethod.invoke(targetCell.getValue(), paramObjects);
// store value
setValue(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
// 4 registers in r double 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 Tree 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_address]

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

Example Solution structure cc2 Integer cc3 Integer Lib1 Math Input1 Integer abs(int) round(double) compareTo(Integer) Output1 Integer cc1 Integer parameters Method calls

Crossing of solutions cross over point Int Int Math Int Int Int Solution A Int Int Math Int Int Int Solution B

Creating children of A & B Int Int Int Int Parent A Int Int Int Int Parent B Child A~B Child B~A cross over point Int Int Int Int Int Int Int Int

Crossover Reconnecting A2 B1 B2 A1 Int Int Math Int Int Int Child A~B Int Int Math Int Int Int Child B~A

Example Solution structure 2 in code: setup.setCallCells(3, "cc", Double .class, Integer.class , Boolean .class ); cc2 Integer cc3 Boolean Lib1 Math Input1 Integer abs(int) Output1 Integer cc1 Double

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 points do not match!

Solving the species problem
prevent mismatch by pre-typing the solution template - specie-fy
use converters - Heart-brain bypass?
on misconnect: no child - allow specie groups as in nature
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

by Devnology

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Devnology Workshop Genpro 2 feb 2011 Presentation Transcript