Finds a nearoptimal solution to a "open" variation of the TSP with fixed start points using a GA
TSPOFS_GA Fixed Start Open Traveling Salesman Problem (TSP) Genetic Algorithm (GA)
Finds a (near) optimal solution to a variation of the TSP by setting up
a GA to search for the shortest route (least distance for the salesman
to travel from a FIXED START to the other cities exactly once without
returning to the starting city)
Summary:
1. A single salesman starts at the first point and travels to each of
the remaining cities but does not close the loop by returning to
the city he started from
2. Each city is visited by the salesman exactly once
Note: The Fixed Start is taken to be the first XY point
Input:
USERCONFIG (structure) with zero or more of the following fields:
 XY (float) is an Nx2 matrix of city locations, where N is the number of cities
 DMAT (float) is an NxN matrix of point to point distances/costs
 POPSIZE (scalar integer) is the size of the population (should be divisible by 4)
 NUMITER (scalar integer) is the number of desired iterations for the algorithm to run
 SHOWPROG (scalar logical) shows the GA progress if true
 SHOWRESULT (scalar logical) shows the GA results if true
 SHOWWAITBAR (scalar logical) shows a waitbar if true
Input Notes:
1. Rather than passing in a structure containing these fields, any/all of
these inputs can be passed in as parameter/value pairs in any order instead.
2. Field/parameter names are case insensitive but must match exactly otherwise.
Output:
RESULTSTRUCT (structure) with the following fields:
(in addition to a record of the algorithm configuration)
 OPTROUTE (integer array) is the best route found by the algorithm
 MINDIST (scalar float) is the cost of the best route
Usage:
tspofs_ga
or
tspofs_ga(userConfig)
or
resultStruct = tspofs_ga;
or
resultStruct = tspofs_ga(userConfig);
or
[...] = tspofs_ga('Param1',Value1,'Param2',Value2, ...);
Example:
% Let the function create an example problem to solve
tspofs_ga;
Example:
% Request the output structure from the solver
resultStruct = tspofs_ga;
Example:
% Pass a random set of userdefined XY points to the solver
userConfig = struct('xy',10*rand(50,2));
resultStruct = tspofs_ga(userConfig);
Example:
% Pass a more interesting set of XY points to the solver
n = 50;
phi = (sqrt(5)1)/2;
theta = 2*pi*phi*(0:n1);
rho = (1:n).^phi;
[x,y] = pol2cart(theta(:),rho(:));
xy = 10*([x y]min([x;y]))/(max([x;y])min([x;y]));
userConfig = struct('xy',xy);
resultStruct = tspofs_ga(userConfig);
Example:
% Pass a random set of 3D (XYZ) points to the solver
xyz = 10*rand(50,3);
userConfig = struct('xy',xyz);
resultStruct = tspofs_ga(userConfig);
Example:
% Change the defaults for GA population size and number of iterations
userConfig = struct('popSize',200,'numIter',1e4);
resultStruct = tspofs_ga(userConfig);
Example:
% Turn off the plots but show a waitbar
userConfig = struct('showProg',false,'showResult',false,'showWaitbar',true);
resultStruct = tspofs_ga(userConfig);
1.3  Major overhaul of input/output interface. 

1.2  Minor cosmetic updates. 

1.1  Added 3D capability. 
SL B (view profile)
User friendly. Used in batch processing type program and only slight modifications were necessary. Saved me a lot of time.