文件列表:

routine2_final.h (10333, 2005-04-19)
afxwin.h (1169, 1999-09-08)
genbin.h (1382, 2004-08-24)
graph.h (13510, 2004-07-12)
indexing.h (2504, 2005-04-19)
random.h (2300, 2004-08-16)
readgraph.h (3050, 2003-01-25)
alg_vari2_final.cpp (10089, 2005-04-19)
Makefile (158, 2005-04-19)

/*********************************************************************** Readme.file Copyright (C) 2005 Jianyong Sun and Qingfu Zhang 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 for more details. You should have received a copy of the GNU General Public License along with this program (file COPYING); if not, write to the Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. You can contact the authors at: Jianyong Sun and Qingfu Zhang Dept. of Computer Science, University of Essex, UK {jysun,qzhang}@essex.ac.uk Updated information about this software will be available at the following www address: http://cswww.essex.ac.uk/staff/qzhang %========================================================================= Contents of this file: 1. Introduction 2. List of files 3. How to compile edamcp 4. How to run edamcp 5. Graph file format %===================================================================== % 1. Introduction %===================================================================== This directory contains an implementation of EDA with guided mutation for maximum clique problem. The algorithm is described in: "Evolutionary Algorithm with Guided Mutation for the Maximum Clique Problem," IEEE Trans. on Evolutionary Computation, 9(2), pp. 192-200, 2005. This features of the algorithm (EA/G) include: 1. EDA recombination operator: guided mutation 2. Search Space Partition Strategy 3. Repair operator by Marchiori. 4. Two kinds of restart strategies based on different search situtations. The guided mutation is proposed by incorporating the location infor- mation (the actual position in the search space) and the global sta- tisitcal information, which is represented by a univariate marginal distribution model. The search space is partitioned into several disjoint subspaces. The algorithm searches several limited subspaces in each procedure. In the algorithm, first a solution is randomly generated, and the re- pair operator by Marchiori is used to improve it to be a maximal cli- que. More details about the implementation are given in the source files. Your comments and suggestions are welcome. If this code is useful to you or you make any improvement to it, please let us know. %===================================================================== % 2. List of files %===================================================================== You should find in this directory the following files: README - This file routine2_final.h - Subroutines of the algorithm alg_vari2_final.cpp- the main genbin.h - file for reading graph readgraph.h random.h - random number generator indexing.h - sort subroutine graph.h - repair operator MAKEFILE - make Look the comment lines at the beginning of each file for a more detailed description of its contents. %===================================================================== % 3. How to compile EDAMCP %===================================================================== To compile EPPSTEIN you should just be in this directory and do make This will generate in this same directory the object files alg_vari2_final.o and the main program alg_vari2_final Up to know the program has only been tested on Pentium machines running Linux. %===================================================================== % 4. How to run EDAMCP %===================================================================== To use EDAMCP you should have a file with your graph in the format described below, and do alg_vari2_final
where is the name of the file where your graph is described, in the format described below. This argument is compulsory. is a positive real number. It is guided mutation parameter. It is usually set to 0.9 for a good performance.
is the best result of all DIMACS workshop participants run alg_vari2_final, you can get the following information: Usage: alg_vari2_final
#--------------------------------------------------------------------# Usage: This program is used for Maximum Clique problem-------# Parameters:
-----------# is the input file, e.g., c-fat200-5.clq.b ----------# ---------------- # is the parameters for guided mutation technology--------#
is the best result of all DIMACS workshop participants--# #--------------------------------------------------------------------# %===================================================================== % 5. Graph file format %===================================================================== The graph file format used in this implementation is inspired on the format defined for the 1st and 2nd DIMACS Implementation Challenges described at http://dimacs.rutgers.edu/Challenges/index.html %===================================================================== % End of README file %===================================================================== ************************************************************************/

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