Fetching latest commit…
Cannot retrieve the latest commit at this time
|Failed to load latest commit information.|
==================================================== = HSGEP: Gene Expression Programming in Haskell = = Version 0.1.3 = = Author: Matthew Sottile (email@example.com) = ==================================================== ** This code is released under the BSD3 Open Source License ** 0.0: Credits ------------ Contributors: - Matthew Sottile (firstname.lastname@example.org) - Dmitrij Naumov 1.0: Introduction ----------------- This package implements the Gene Expression Programming algorithm invented by Candida Ferreira. See the following paper for a good, concise explanation of the method: Ferreira, C., 2001. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems, Vol. 13, issue 2:87-129. http://www.gene-expression-programming.com/webpapers/abstracts.asp#01 GEP is an evolutionary algorithm for solving optimization problems. The introduction to the paper cited above provides a good explanation of what differentiates GEP from GP and GAs. 2.0: Background --------------- This project is an ancestor of an earlier effort to build a generic framework for using GEP, originally in Java. The move to a functional language (originally SML, now Haskell) was because: - At its core, GEP is focused on manipulating symbolic sequences and tree structures. List and user defined data types in Haskell are very well suited to this. - Functional languages naturally support functions being first class citizens, being passed around as arguments to functions. While this abstraction is possible using object interfaces and hierarchies (which is precisely what the Java version used), it felt more cumbersome to manage and code up. - Pattern matching and strict type checking provide very strong checks on the core of the library to ensure that some classes of bugs are not present. For example, being able to guarantee that a pattern match is exhaustive at compile time is preferable to potential runtime errors that may result if such compilation time checks are not performed. 3.0: Usage ---------- At this point, the best places to look for documentation on using the library is: - The Haddock documentation. - Looking at examples. The most mature example currently included in the released code is the regression example. In this example, a set of data points are provided and the optimization phase seeks to evolve a polynomial composed of basic arithmetic (+-*/), sqrt, and exponentiation operations that best fits the data points. The example provided is fairly simplistic, and fails to include useful things like the ability to evolve constants as part of the polynomials. In any case, it is sufficient to demonstrate the library. To run a regression example, you can use example input parameter and data files from the Examples/Regression directory. For example, after building the code, you can run the example "test1" as: ./dist/build/HSGEP_Regression/HSGEP_Regression -i ./Examples/Regression/test1.in -f ./Examples/Regression/test1.csv The current code will then evolve a solution that maximizes the fitness function (goodness of fit to the given data points), and will print it out. The example will also attempt to connect to a machine running a Maxima server to perform polynomial simplification to turn the long string of basic arithmetic operators into a more useful polynomial. This is likely going to not work for most people, so either ignore the error, or tweak the code to either disable it or run the Maxima server code somewhere. 4.0: FAQ -------- - Q: Is this library intended to be more than a toy? A: Yes. It has been a testbed for me to get used to some of the details related to releasing a properly packaged library to the world on Hackage, so some of the core has been neglected while I did things related to build, organization, and documentation. - Q: What are the plans for near-term new versions? A: Lots. - If a plotting library is available (e.g.: Chart), produce plots of fitness over time. - Add more examples, such as the CA Density classification task. - Performance improvements. - Go parallel -- there are many opportunities for parallelism during the run of the algorithm, so it would be worth taking advantage of them. - Abstract out some of the patterns currently residing in each example, such as the process of expressing individuals as structures. The ultimate goal is to make the end-user code that uses the library as simple as possible, so absorbing as much of this into the core of the library will be a good step in that direction.