ECJ

An Evolutionary Computation and Genetic Programming System by Sean Luke
Copyright 2001 by Sean Luke, All Rights Reserved

License Agreement

Documentation

This distribution comes with several pieces of documentation:

• A high-level overview of the packages and classes in the system. Read this thoroughly first.
• A primer about parameters and parameter files.
• A lot of class documentation.
• A PDF file showing the ECJ class hierarchy as of ECJ 4.
• A PDF file detailing the Object state of ECJ when running Symbolic Regression as of ECJ 4.

Tutorials

Here are some tutorials to get you up to speed. As I have time, I'll produce tutorials on the other various odd features of the system.

1. Tutorial 1: Build a Genetic Algorithm for the MaxOnes Problem. Introduces the basics of building a simple ECJ problem, high-level ECJ constructs, the concepts behind the parameter files, and how to make an evaluation function.
2. Tutorial 2: Build a Genetic Algorithm for an Integer Problem. Shows how to use a new representation (integer vectors), parent parameter files, multiple threads, custom breeding pipelines and breeding operators, and other good stuff.

Quick Examples

Some runs which result in the ideal individual being found (in most cases). First, cd to the ec directory. Then add the parent directory of the ec directory to your CLASSPATH. Then issue javac -O *.java */*.java */*/*.java */*/*/*.java Alternatively you can run the makefile.

Now you're ready. Here are some problems to try.

(GA) Vector Sum (pop=100, 10 genes)

java ec.Evolve -file app/sum/sum.params

(GA) Rastrigin (pop=1000, 100 genes)

java ec.Evolve -file app/ecsuite/ecsuite.params

(GA) Rosenbrock (pop=1000, 100 genes)

java ec.Evolve -file app/ecsuite/ecsuite.params -p eval.problem.type=rosenbrock

(GA) Sphere (pop=1000, 100 genes)

java ec.Evolve -file app/ecsuite/ecsuite.params -p eval.problem.type=sphere

(GA) Step (pop=1000, 100 genes)

java ec.Evolve -file app/ecsuite/ecsuite.params -p eval.problem.type=step

(GA) Noisy-Quartic (pop=1000, 100 genes)

java ec.Evolve -file app/ecsuite/ecsuite.params -p eval.problem.type=noisy-quartic

(GP) Symbolic Regression, no ercs

java ec.Evolve -file app/regression/noerc.params

(GP) Symbolic Regression with ercs

java ec.Evolve -file app/regression/erc.params

(GP) Artificial Ant, Santa Fe Trail

java ec.Evolve -file app/ant.params

(GP) Artificial Ant, Los Altos Hills Trail

java ec.Evolve -file app/ant.params -p eval.problem.file=app/ant/losaltos.trl

(GP) Boolean 3 Multiplexer (new fast form)

java ec.Evolve -file app/multiplexer/3.params

(GP) Boolean 6 Multiplexer (new fast form)

java ec.Evolve -file app/multiplexer/6.params

(GP) Boolean 11 Multiplexer (new fast form)

java ec.Evolve -file app/multiplexer/11.params

(GP) Boolean 3 Multiplexer (original form)

java ec.Evolve -file app/multiplexerslow/3.params

(GP) Boolean 6 Multiplexer (original form)

java ec.Evolve -file app/multiplexerslow/6.params

(GP) Boolean 11 Multiplexer (original form)

java ec.Evolve -file app/multiplexerslow/11.params

(GP) 8x8 Lawnmower

java ec.Evolve -file app/lawnmower/noadf.params

(GP) 8x8 Lawnmower with 2 ADFs

java ec.Evolve -file app/lawnmower/adf.params

(GP) Even 4-Parity

java ec.Evolve -file app/parity/parity.params -p eval.problem.even=true

(GP) Odd 4-Parity

java ec.Evolve -file app/parity/parity.params -p eval.problem.even=false

(GP) Odd 10-Parity

java ec.Evolve -file app/parity/parity.params -p eval.problem.even=false -p eval.problem.bits=10 -p gp.fs.0.size=14

(GP) Even 3-Parity with 2 ADFs

java ec.Evolve -file app/parity/adf.params -p eval.problem.even=true

(GP) Odd 3-Parity with 2 ADFs

java ec.Evolve -file app/parity/adf.params -p eval.problem.even=false

(GP) Simple Edge Encoding for Tomita Language Problem 3

java ec.Evolve -file app/edge/3.params

After a run, various statistical information is written to the out.stat file. All the GP examples use a population size of 1000, and tournament selection. All other parameters are Koza-I/II standard.

Contacting the author