TSP_USING_GA

Done as part of the Final Project Evaluation for 19BIO201 - Intelligence of Biological Systems - 3, It is bioinformatics project

• It starts with a population of a random size and random pathways (the first city is the last one)
• The user chooses the number of generations to run before the genetic algorithm starts.
• By adding pathways through crossover, mutations, and random routes, the population is doubled at the end of each generation.
• Only half of the greatest will survive to the following generation, according to the survival of the fittest theory.
• In order for the algorithm to avoid becoming stuck in a local minimum solution, new paths are built at each iteration.
• Routes through crossover genetic function are produced at every generation
• Routes through mutation genetic function are also produced at each generation.