check@sys.titech.ac.jp (Takashi Sugawara) (02/07/91)
Hello, from Japan. I study GAs for TSP. I now uses my own crossover and operators for GA. Today I succeed in 100 cities of TSP. And next I'll try with more than 200 cities. But now I have only my own co-ordinates of cities, so; In article <1991Feb5.195412.3036@informatik.uni-erlangen.de> fritzke@immd2.informatik.uni-erlangen.de (B. Fritzke) writes: >>I'm looking for sample Traveling Salesman Problems (TSP) to test the network >>model I have developed. >>Specifically I look for instances of the Euclidean TSP. This is the case, >>when all the n cities are distributed in the unit square and the distance >>measure is the Euclidiean distance. >> >>I would like to get together with the examples the shortest known path >>length or (even better) the optimal path length. yes, I also need them. I need instances and information of it, too. Please send me or post. Thank you. ```````````````````````````````````````````````````````` )) Takashi Sugawara (( @ @ @ @ > Control Engineering, Tokyo Institute of Technology < ~ JAPAN - e-mail : check@sys.titech.ac.jp Office : 8145-922-1111(ext.2689) Fax : 8145-921-1485 ........................................................
sonia@bastille.berkeley.edu (Sonia Marx) (02/12/91)
In article <CHECK.91Feb7161120@nobert.sys.titech.ac.jp>, check@sys.titech.ac.jp (Takashi Sugawara) writes: > > Hello, from Japan. > > I study GAs for TSP. I now uses my own crossover and operators for GA. Today ^^^ > I succeed in 100 cities of TSP. And next I'll try with more than 200 cities. > But now I have only my own co-ordinates of cities, so; What are GA's? I'm sorry for wasting net bandwidth, but I've seen the term in several postings, and there has never been a translation. Sonia Marx
aighb@castle.ed.ac.uk (Geoffrey Ballinger) (02/12/91)
In article <11010@pasteur.Berkeley.EDU> sonia@bastille.berkeley.edu (Sonia Marx) writes:
-What are GA's? I'm sorry for wasting net bandwidth, but I've seen the term
-in several postings, and there has never been a translation.
GA = Genetic Algorithm. A Genetic Algorithm is a
search/optimisation technique based on an abstraction of evolution. For
an introduction to the area see "The Genetic Algorithm Handbook" by
Lawrence Davis (Van Nostrand Reinhold), or "Genetic Algorithms in
Search, Optimisation and Machine Learning" by David Goldberg
(Addison-Wesley). I hope this helps,
Geoff.
--
Geoff Ballinger, EMAIL: Geoff@Ed.Ac.Uk
Department of Artificial Intelligence,
University of Edinburgh, 80 South Bridge,
Edinburgh, Scotland.sonia@bastille.berkeley.edu (Sonia Marx) (02/13/91)
-In article <8435@castle.ed.ac.uk>, aighb@castle.ed.ac.uk (Geoffrey Ballinger) writes: % In article <11010@pasteur.Berkeley.EDU> sonia@bastille.berkeley.edu (Sonia Marx) writes: % -What are GA's? I'm sorry for wasting net bandwidth, but I've seen the term % -in several postings, and there has never been a translation. % % GA = Genetic Algorithm. A Genetic Algorithm is a % search/optimisation technique based on an abstraction of evolution. For % an introduction to the area see "The Genetic Algorithm Handbook" by % Lawrence Davis (Van Nostrand Reinhold), or "Genetic Algorithms in % Search, Optimisation and Machine Learning" by David Goldberg % (Addison-Wesley). I hope this helps, % % Geoff. % -- % Geoff Ballinger, EMAIL: Geoff@Ed.Ac.Uk % Department of Artificial Intelligence, % University of Edinburgh, 80 South Bridge, % Edinburgh, Scotland. Thanks everybody! I should have known better than to post a question like that to the net -- my mailbox is overflowing! I hope that this benefitted other people out there at least. Sonia Marx
franfh@kossy.jhuapl.edu (Francis P. Horan) (02/13/91)
In article <CHECK.91Feb7161120@nobert.sys.titech.ac.jp> check@sys.titech.ac.jp (Takashi Sugawara) writes: > >In article <1991Feb5.195412.3036@informatik.uni-erlangen.de> fritzke@immd2.informatik.uni-erlangen.de (B. Fritzke) writes: > > >>I'm looking for sample Traveling Salesman Problems (TSP) to test the network > >>model I have developed. > >>Specifically I look for instances of the Euclidean TSP. This is the case, > >>when all the n cities are distributed in the unit square and the distance > >>measure is the Euclidiean distance. > >> > >>I would like to get together with the examples the shortest known path > >>length or (even better) the optimal path length. The table of several TSP city arrangements at the end of this post has data in it used in the 3 references below: [1] "Computational-Complexity Reduction for Neural Network Algorityhms" by Allon Guez, Moshe Kam, James Eilbert. IEEE Transactions on Systems, Man, and Cybernetics, Vol 19, No. 2, March/April 1989. [2] " 'Neural' Computation of Decisions in Optimization Problems". Biological Cybernetics, Vol 52, pp 141-152, 1985. [3] "An effective heuristic algorithm for the travelling-salesman problem," Operation Research., vol 21, pp 48-516, 1973. All of the TSP city arrangements listed below were studied in paper [1]. The "10 City Hopfield arangement" was first described in paper [2], and the the "30 City Lin and Kernighan arangement" was first described paper [3]. We got the data points for these two arrangements over the phone from Hopfield's secretary. The columns are labelled by the top row. Each label has a number corresponding to the number of cities for the problem. Other text describes the axis (X or Y) and the city arrangement. X5 = X coordinates, Square 5 City problem Y5 = Y coordinates, Square 5 City problem X7 = X coordinates, Square 7 City problem Y7 = Y coordinates, Square 7 City problem X10Sq = X coordinates, Square 10 City problem Y10Sq = Y coordinates, Square 10 City problem X30 = X coordinates, 30 City, Lin and Kernighan arrangement, ref [3] Y30 = Y coordinates, 30 City, Lin and Kernighan arrangement, ref [3] X10Hop = X coordinates, 10 City, Hopfield and Tank arrangement, ref [2] Y10Hop = Y coordinates, 10 City, Hopfield and Tank arrangement, ref [2] X5 Y5 X7 Y7 X10Sq Y10Sq X30 Y30 X10Hop Y10Hop 0.0 1.0 0.0 1.0 0.143 1.0 .4384 .692 .22926 .76688 .5 1.0 .25 1.0 0.286 1.0 .4232 .2328 .68318 .50919 1.0 1.0 .50 1.0 0.429 1.0 .7186 .6939 .87456 .64464 1.0 0.0 .75 1.0 0.571 1.0 .3956 .1845 .84747 .35396 0.0 0.0 1.0 1.0 0.714 1.0 .9529 .4058 .39889 .45709 1.0 0.0 0.857 1.0 .6321 .0704 .23631 .13318 0.0 0.0 1.000 1.0 .6094 .2125 .16605 .22603 0.0 1.0 .3693 .5692 .66246 .25021 0.0 0.0 .3325 .6035 .61770 .26247 1.0 0.0 .0774 .8135 .51267 .93921 .5412 .1743 .3966 .1180 .2036 .4527 .7645 .8556 .5043 .0289 .9983 .0065 .6888 .8236 .6012 .6401 .5931 .6716 .7744 .7172 .3720 .8944 .2682 .4146 .6178 .2461 .6836 .5692 .6604 .5272 .6240 .5331 .4605 .8192 .353 .445 .3808 .2468 .8341 .3587