ellenGP is a genetic programming tool for symbolic regression and multi-class classification that incorporates epigenetic learning and uses a stack-based, linear representation.
This code formed the basis of research during my dissertation
Please note that most of the current development for ellen is happening in the ellyn repo, which is a Python-wrapped version of this code base.
There are some library dependencies, including eigen.
The files have been built in Visual Studio C++ 2010 and in linux with gcc and the intel c++ compiler.
ellenGP uses a stack-based, syntax-free, linear genome for constructing candidate equations.
It is built to include different evolutionary methods for system identification adapted from literature. The options include normal tournament selection, deterministic crowding, and age-pareto fitness selection. All algorithm choices are mangaged by one parameter file.
How to Build
I've built the project in Visual Studio 2010 professional as well as C++ Express (which is free from Microsoft), and in linux with g++ and the intel c++ compiler using the make files. If you use VS 2010 Express, the OpenMP files (which were removed from VS 2010) need to be added to the VS path.
There are two external library dependencies:
- boost libraries - a set of multi-purpose c++ libraries, needed for RunTrialsMPI only
- eigen - a c++ template library for linear algebra
In addition to downloading those packages, the paths to them need to be modified in the Makefiles for ellenGP and RunTrials.
How to run ellenGP
Run ellenGP like this:
ellenGP sampleparams.txt sampledata.txt
As you can see, ellenGP takes two arguments: a parameter file and a data file. The parameter file includes all of the run-time settings for your search. The data file includes all your experimental data. See the sampleparams.txt and sampledata.txt files to see how formatting works.
How to run RunTrials
RunTrials will run ellenGP for many trials. It uses OpenMP to parallelize the trials. Here is the syntax:
RunTrials takes one input file (sampletrials.txt). The trials input file contains three columns:
[#trials] [parameterfile] [datafile] There is also an MPI version, RunTrialsMPI, which uses the same syntax, but parallelizes the threads over a cluster rather than the cores of a single node (computer).
for example, ... 100 ../in/sampleparams.txt ../in/sampledata.txt
These are the simple instructions for running ellenGP.
RunTrialsMPI is the same as RunTrials except it is written to be compiled on the clusters (the TACC cluster Stampede as well as the Umass HPCC cluster). MakefileTACC and MakefileUMG has the compilation notes. It has been built using intel icpc and the MPI compiler mvapich2 from OSU, as well as g++ with mpicxx.
Here is a comprehensive list of all of the options that you can include in the parameter file.
|g||100||number of generations|
|limit_evals||0||limit point evals instead of number of generations|
|max_evals||0||max point evaluations|
|| Generation Settings | |
sel | 1 | 1: tournament 2: deterministic crowding 3: lexicase selection 4: age-fitness pareto algorithm
PS_sel | 1 | objectives for pareto survival. 1: age + fitness; 2: age+fitness+generality; 3: age+fitness+complexity; 4: class fitnesses (classification ONLY); 5: class fitnesses+ age (classification ONLY)
tourn_size | 2 | number of individuals in each tournament
rt_rep | 0 | rate of reproduction
rt_cross | 0.8 | rate of crossover
rt_mut | 0.2 | rate of mutation
cross | 3 | 1: ultra 2: one point1 3: sub-tree
mutate | 2 | 1: point mutation; 2: subtree mutation
cross_ar | 0.025 | crossover alternation rate (ultra only)
mut_ar | 0.025 | mutation alternation rate
align_dev | 0 | on or off; adds gaussian alignment deviation to crossover
elitism | 0 | save best individual each generation
stop_condition | 1 | if on, run will terminate when an fitness < 1e-6 is reached.
init_validate_on | 0 | initial fitness validation of starting population
|| Data Options
train | 0 | split data into training and validation sets
train_pct | 0.5 | percent of data to be used in training
shuffle_data | 0 | shuffle the data before splitting into training and validation
pop_restart | 0 | restart run from previous population specified by pop_restart_path
pop_restart_path | "" | filename of restart population with path
|| Results and Printing Options
resultspath | "" | path where results are saved
print_every_pop | 0 | save printout of population at every generation
print_genome | 0 | prints genome for visualization in paraview
print_novelty | 0 | print number of unique output vectors
print_homology | 0 | print genetic homology in programs
num_log_pts | 0 | number of log points to print (0 means print each generation)
|| Classification Options
classification | 0 | defines a classification, rather than regression, problem
class_bool | 0 | interpret class labels as bit-string conversion of boolean stack output
class_m3gp | 0 | use mahalanobis distance classification fitness
class_prune | 0 | prunes the dimensions of the best individual each generation
|| Problem information
intvars | none | variables in data file to use in programs
cvals | none | seed the initial population with certain constant values
seeds | none | seed partial solutions, e.g. (x+y)
AR | 0 | include auto-regressive output variables
AR_n | 1 | order of auto-regression (number of time-steps back)
AR_lookahead | 0 | just predict one output ahead
ERC | 1 | ephemeral random constants
ERCints | 0 | make the ERCs integer valued rather than floats
maxERC | 1 |
minERC | -1 |
numERC | 1 |
|| Fitness Settings
fit_type | 1 | 1: mean absolute error, 2: corr, 3: combo, 4: VAF
norm_error | 0 | normalize error by the standard deviation of the target data being u
max_fit | 1.00E+20 | maximum fitness possible
min_fit | 1.00E-20 | minimum fitness possible
estimate_fitness | 0 | coevolve fitness estimators
FE_pop_size | 0 | fitness estimator population size
FE_ind_size | 0 | number of fitness cases for FE to use
FE_train_size | 0 | trainer population size
FE_train_gens | 0 | number of generations between trainer selections
FE_rank | 0 | use rank for FE fitness rather than error
estimate_generality | 0 | estimate how well the solutions generalize using the validation portion of the fitness estimator G_sel | 0 | which fit_type to use to test generality G_shuffle | 0 | shuffles data each generation op_list | n v + - * / | available operators: n v + - * / sin cos log exp sqrt = ! < <= > >= if-then if-then-else & | weight_ops_on | 0 | weight the operators differently op_weight | empty | weights of the operators specified in op_list min_len | 3 | minimum program length max_len | 20 | maximum length a program is allowed to be max_len_init | max_len | option to specify different max length for initial population init_trees | 0 | initialize genotypes as syntactically valid trees rather than randomized stacks complex_measure | 2 | 1: genotype size 2: symbolic size 3: effective genotype size || Hill Climbing Settings || parameters pHC_on | 0 | parameter hill climbing each generation pHC_its | 1 | number of iterations || epigenetics eHC_on | 0 | epigenetic hill climbing eHC_its | 1 | number of iterations eHC_prob | 0.1 | probability of a gene being switched eHC_init | 0.5 | percent of expressed genes in initial genotypes eHC_slim | 0 | minimize point evaluations as much as possible eHC_mut | 0 | do mutation rather than hill climbing || Pareto Archive Settings prto_arch_on | 0 | prto_arch_size | 20 | || Island model islands | 0 | use multiple island populations, one for each core. island_gens | 100 | number of generations between shuffling of the island populations || Lexicase Options lexpool | 1 | Fraction of population to use in lexicase selection events lex_class | 0 | For a classification problem, use separate class fitnesses as cases lex_metacases | none | Specify extra cases for selection. Options: age, complexity lex_eps_std | 0 | use epsilon lexicase with eps = standard deviation of error lex_eps_error | 0 | use epsilon lexicase with error-based epsilons lex_eps_target | 0 | use epsilon lexicase with error-based epsilons lex_eps_target_mad | 0 | use epsilon lexicase with median absolute deviation, target-based epsilons lex_eps_error_mad | 0 | use epsilon lexicase with median absolute deviation, error-based epsilons lex_epsilon | 0.1 | value of epsilon (ignored for mad and std versions)
ellenGP Copyright (C) 2014 William La Cava
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License (License.txt) for more details.