I came across Genetic Algorithm (GA) the other day when I was doing the project. It is typically adopted to solve the shortest path routine problem or design and optimize the structure of proteins. It is a very smart algorithm inspired by the biological system.
I will try to describe the idea behind the concept briefly: In some problems there are many possible solutions, and we look for the best one. To find this very best solution it is like creating the chromosome of the genes in the most optimized order. To find out the best (-ish) combination, one way is comparing ALL possible combinations, which is impractical in some cases. So instead of listing all solutions and comparing them, a sub-group of solutions (population) are created, and then we pick two out of them as the parents. The better the solution was, the higher chance it can be selected. Then the two chromosomes crossover (exchange genes) to “breed” new populations. There is a chance of mutation for the new population as well. The new population are usually more “advanced” than their parent population (not necessarily better than their parents). Then new parents are picked out again to breed new populations and so on.
Typically the genes to order in the chromosome are binary, but we can also do that for integer numbers and other values. Please find this tutorial for more encoding methods.
To demonstrate the implementation of GA in LabVIEW I downloaded the coordinates of 31 cities of China and tried to find out the shortest path routine of them. So here is the .gif demo (the labels for X and Y axises should be “Latitude” and “Longitude”). Please note that this may not be the BEST solution. But in term of the number iterations we ran it, it is good enough.
Shortest path routine for 31 cities in China
You can download this code here:
. Please rename it to .zip file and unzip it.
Let me know your score 😉
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