This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

GA.M: A Matlab routine for function maximization using a Genetic Algorithm

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Michael B. Gordy ()

Additional information is available for the following registered author(s):

Abstract

Implements a Genetic Algorithm for Maximization a la Dorsey and Mayer, Journal of Business and Economic Statistics, January 1995, 13(1)

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help file. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: ftp://all.repec.org/RePEc/cod/html/Matlab/gordy.si
File Format: text/plain
File Function: Software information
Download Restriction: no
File URL: ftp://all.repec.org/RePEc/cod/html/Matlab/gordy.m
File Format: text/plain
File Function: program
Download Restriction: no

Publisher Info
Software component provided by in its series Matlab codes with number ga.

Download reference. The following formats are available: HTML, plain text, BibTeX, RIS (EndNote), ReDIF
Size:
Programming language: Matlab
Requires: Matlab 4.2 or later
Date of creation:
Date of revision: 12 Feb 1996
Handle: RePEc:cod:matlab:ga

Contact details of provider:

For technical questions regarding this item, or to correct its listing, contact: (Thomas Krichel).

Related research
Keywords:

Other versions of this item:

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)
  1. Markus Poschke, 2007. "Employment Protection, Firm Selection, and Growth," IZA Discussion Papers 3164, Institute for the Study of Labor (IZA). [Downloadable!]
    Other versions:
  2. Paul McNelis & John Duffy, 1998. "Approximating and Simulating the Stochastic Growth Model: Parameterized Expectations, Neural Networks, and the Genetic Algorithm," GE, Growth, Math methods 9804004, EconWPA, revised 04 May 1998. [Downloadable!]
    Other versions:
  3. Alicia Gazely & Jane Binner & Graham Kendall, 2004. "Co-evolution vs. Neural Networks; An Evaluation of UK Risky Money," Computing in Economics and Finance 2004 258, Society for Computational Economics. [Downloadable!]
  4. Markus Poschke, 2006. "The regulation of entry and aggregate productivity," Economics Working Papers ECO2006/21, European University Institute. [Downloadable!]
  5. Christopher R. Knittel & Konstantinos Metaxoglou, 2008. "Estimation of Random Coefficient Demand Models: Challenges, Difficulties and Warnings," NBER Working Papers 14080, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
Statistics
Access and download statistics

Did you know? IDEAS also covers the most complete directory of Economics departments and institutes, EDIRC.

This page was last updated on 2008-8-5.


This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.