An interactive Swift Playground about Genetic Algorithms, designed for Xcode 9.3 as an iOS / UIKit playground. This is my winning entry for the WWDC 2018 scholarship application. The playground is a practical and highly customizable learning experience, where you may discover how a well suited Genetic Algorithm can be used to solve the famous Travelling Salesman Problem (TSP), among other important uses.
To run the playground, you will need a compter running macOS and a recent version of Xcode.
- In a command line, type
git clone https://github.com/joelrorseth/World-Tour/ - Double click / open
World-Tour.playground.
The playground consists of three pages, an introduction, the implementation page, then finally the visualization and tweaking page. The playground guides you to a working implementation of a genetic algorithm that successfully optimizes a solution for the famous Travelling Salesman Problem.
The second page embeds all of the major components of the working genetic algorithm into the playground, provided alongside explanations and implementation details. The code provided works as is, but the point is to provide a modularized sandbox to test out your own implementations, tricks, or efficient improvements (maybe try an Elitist selection strategy?). For example, there are many different ways to implement the Crossover process. More importantly, the performance of the algorithm will depend on these functions, in combination with defining parameters such as the population size or mutation probability.
The view controller in the live view is a visualization of an instance of your genetic algorithm implementation. Depending on the parameters and instructions you programmed in the page (or didn't), you should be able to click Run and see a discernible improvement in successive generations. The algorithm is tasked with finding the Salesman's shortest path across a number of Canadian cities, so your algorithm is seeking smaller and smaller total Tour (sequence of cities) distances with each generation. Click any generation in the table view to expand the sequence for the optimal Tour.
The third page presents a simplified, interactive representation of the Tour total distance problem that the previous page proposed. In the playground page, you can try out different parameters for the algorithm to be used in this visualization. When you run the page, a map of Canada will be presented in the live view. Click around on the map to drop pins, which are locations the Travelling Salesman will travel to. When you have finalized his tour, click Start to start the genetic algorithm. The final, optimal path will be animated onto the map when the computation has terminated.
MIT License
Copyright (c) [2018] [Joel Rorseth]
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.