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Genetic Algorithm for Traveling Salesman Problem with Crossover and Mutation in C Code

Implement a genetic algorithm for the Traveling Salesman Problem in C code with customizable operators for crossover and mutation. The code includes functions for encoding genotypes, rank scaling, and constraint optimization. Utilize the GOAT package for MATLAB for minimization and maximization tasks.

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Genetic Algorithm for Traveling Salesman Problem with Crossover and Mutation in C Code

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  1. GA Applications • Peaks function • C- code • GOAT package for MATLAB • minimization and maximization • Traveling Salesman Problem • genotype and phenotype encoding • customizing operators • rank scaling • Hillis Sorting Problem • Sequence Alignment • Floating point GAs • Constraint optimization • Multi-objective optimization • The schemata theorem

  2. Components of binary GA in Feature Selection R2 = Goodness of fit Problem:max R2 Selected Population 0.1 Fitness Population Selection 110101 111111 000000 f1 = 0.60 f2 = 0.30 f3 = 0.10 110101 110101 000000 0.3 0.6 Crossover point 111100 000011 111111 000000 Crossover Selected gene Mutated gene Mutation 111111 111110

  3. Uniform Crossover 5 24 131 534 603 Parent 1 19 33 255 334 508 Parent 2 19 33 131 534 603 Child 1 Child 2 5 24 255 334 508 • Mutation 5 24 131 534 603 Parent Child 5 24 344 534 603 Genetic Operators

  4. Traveling Salesman Problem

  5. void main(int argc, char *argv[]) {char mombassa[80], root[80]; data b; double alpha, beta; //user data int num_cities; MATRIX distances; Container box; //user data to objective function in box double (* fptr) (data*, VECTOR); //function pointer to objective fnctn genotype pop; fptr = Salesman3; MatrixAllocate(&distances, 500, 500); userData(&b, &box); // tells pointer of userdata in data struct for b Read_User_Data(&alpha, &beta, &num_cities, distances); box.pop = &pop; box.alpha = alpha; box.beta = beta; box.num_cities = num_cities; box.distances = distances; if (argc == 2) strcpy( mombassa, argv[1]); Allocate_GA(&pop, &b, argc, mombassa, root, fptr); b.print_flag=0; Loop_GA(&b, &pop, root, fptr); Write_User_Data(&b, &pop, root, fptr); De_Allocate_GA(&pop, &b, root, fptr); MatrixFree(distances, 500); }

  6. double Salesman2(data *a, VECTOR x) { int i, isum=0;double tour= 0, pen1=0, pen2=0; double alpha, beta;int num_cities, one, two, help; Container * box = (Container *)(a->ud); alpha = box->alpha; beta = box->beta; num_cities = box->num_cities; help = num_cities/2*(num_cities-1); if (num_cities%2 == 1) help = help+num_cities%2; for (i = 0; i < num_cities-1;i++) { one = (int) x[i]; two = (int) x[i+1]; tour = tour + box->distances[one][two]; } one = (int) x[num_cities-1]; two = (int) x[0]; tour = tour + box->distances[one][two]; for (i = 0; i < num_cities;i++) isum += (int) x[i]; if (isum!=help) pen1=alpha; getche(); box->penn1=pen1; box->penn2=pen2; return tour + pen1; }

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