Merge branch 'master' of https://github.com/manugond/Genetic-Algorithm

README.md

@@ -20,3 +20,38 @@ The advt alternatives for a company include TV, newspaper and radio. The cost of
 # ToDO
 * [ ] Resolve the mutation function in SimpleGA.java.
 * [x] Resolve the random initialization in C++ program.
+
+# Implementation of TSP
+ As per TSP problem the intial and final city should be the same but in this program only one unique path is created wihout repeating any city, this is because generated unique paths can be compared with cost by adding the initial vertex into the path's ending vertex and checking its validity. (Might update this code in future)
+
+
+# Input Case for TSP
+  In program's Graph input is hard coder into the class constructor. Here's a 17 city input for the TSP
+
+                       0 633 257  91 412 150  80 134 259 505 353 324  70 211 268 246 121
+                   633   0 390 661 227 488 572 530 555 289 282 638 567 466 420 745 518
+                   257 390   0 228 169 112 196 154 372 262 110 437 191  74  53 472 142
+                    91 661 228   0 383 120  77 105 175 476 324 240  27 182 239 237  84
+                   412 227 169 383   0 267 351 309 338 196  61 421 346 243 199 528 297
+                   150 488 112 120 267   0  63  34 264 360 208 329  83 105 123 364  35
+                    80 572 196  77 351  63   0  29 232 444 292 297  47 150 207 332  29
+                   134 530 154 105 309  34  29   0 249 402 250 314  68 108 165 349  36
+                   259 555 372 175 338 264 232 249   0 495 352  95 189 326 383 202 236
+                   505 289 262 476 196 360 444 402 495   0 154 578 439 336 240 685 390
+                   353 282 110 324  61 208 292 250 352 154   0 435 287 184 140 542 238
+                   324 638 437 240 421 329 297 314  95 578 435   0 254 391 448 157 301
+                    70 567 191  27 346  83  47  68 189 439 287 254   0 145 202 289  55
+                   211 466  74 182 243 105 150 108 326 336 184 391 145   0  57 426  96
+                   268 420  53 239 199 123 207 165 383 240 140 448 202  57   0 483 153
+                   246 745 472 237 528 364 332 349 202 685 542 157 289 426 483   0 336
+                   121 518 142  84 297  35  29  36 236 390 238 301  55  96 153 336   0 
+
+To add this as input copy the TSP_Input_2 into the class constructor. This should be able to print the optimal path. I ran this using 1000 initial population size with 5 generations and it gave optimal output about 3052. However optimal output for this case is 2085 as said in https://people.sc.fsu.edu/~jburkardt/datasets/tsp/tsp.html . There's issue that if population size is large or number of generations increased then program will halt (Due to memory issue).
+
+However initializing the population to 20000 in this case without doing any crossover we get 2513 as optimal path in the initial population which can be said close to 2085.
+Here's the output of that:-
+
+               ==================Initial Population==============
+
+         Fittest Individual:
+         Path: 10 2 11 5 15 3 6 17 12 9 16 14 7 4 13 1 8 Fitness: 2513