You might be familiar with the Travelling Salesman Problem. After all, it’s one of those classics that one can’t avoid hearing about. It’s not an easy problem to solve, in fact, it’s NP-Hard, so no polynomial time that solves it exists (unless P = NP, but that’s a different topic). 711 more words

## Tags » Travelling Salesman Problem

#### Code Reviews, Are They Match for Catching Bugs?

I came across an instance where code reviews are expected to catch bugs. Why did it passed code review when in fact there were bugs in it? 466 more words

#### GA Utilizing Efficient Operators in TSP

Through the data collected in the above two pages, it can be reasonably be concluded that center inverse mutation in unison with the inversely linear roulette wheel selection and the random crossover point yield the best result with a higher number of generations. 97 more words

#### In the Comparison of Genetic Operators For Solving the Traveling Salesman Problem: Selection

In comparing selection methods, for the sake of comparison it was in our best interest to leave the least to randomness except in the selection method. 61 more words

#### In the Comparison of Genetic Operators For Solving the Traveling Salesman Problem: Mutation

In attempt to statistically compare the operators, the input graph and the initial population was kept the same for each trial. The numbers displayed below are the average of 10 trials conducted with the same input graph but a different initial population. 90 more words

#### Trying to resolve the Travelling Salesman Problem in Salesforce

Below is a screen showing one attempt at finding the best route to visit several accounts using the Artificial Bee Colony algorithm. The best route is defined as the route with minimalĀ total sumĀ of the distances between accounts. 270 more words