Simple GA implementation using C++ & Java. If you get error due to large population in DevC++ then cosider running this here: https://www.onlinegdb.com/online_c++_compiler
For DevC++ users, to get the C++11 support: Goto Tools -> Compiler Options -> Check the 'Add following commands when calling the compiler -> Add the following text
-std=c++11
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The advt alternatives for a company include TV, newspaper and radio. The cost of each medium with audience converge is given below:-
TV Newspaper Radio Cost per advt 2000 600 400 Audience/Advt. 100000 40000 18000
Local newspaper limits the number of advt from a single company to 10, moreover in order to balance advertising among the 3 types of media no more than half of total no. of advts should occur on radio, and at least 10% should occur on television. The weekely advt. budget is $18200, how many advts. should be run in each 3 types of media to meximize the total audience.
- Construct a Genetic Algorithm based approach to solve TSP (Travelling Salesman Problem).
In program's Graph input is hard coded into the class constructor for testing. 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
As per GA, it starts with minimizing objective function which is in this case total distance covered. 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 . 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:
Fitness: 2513
This concludes that optimization problems like TSP can be solved with GA efficiently as it can search from multiple points in search space.