Ruby on Rails + React implementation of an application, based on Genetic Algorithms, representing possible lives of startups. The algorithm improves startup choices over generations, to achieve the most successful outcome possible; in a map where investors, product launches, team members, sad news and sales, among other options, appear.
This application, is running in www.geneticstartups.com
Within the field of Artificial Intelligence (AI), Genetic Algorithms (GA) are grouped in the larger class of evolutionary algorithms. And are often used as a search heuristic, to generate solutions to optimization problems.
Genetic Algorithms use techniques inspired by natural evolution, such as selection, crossover and mutation; in order to evolve a random population of possible solutions into better ones, over generations.
Startups are surrounded with huge uncertainty and have limited resources and time to find product/market-fit and become sustainable businesses. Besides, the life of a startup is full of challenges and tough choices. As founders, we must be very careful choosing one path over another, when making decisions.
In these applications, we generate random maps that represent the space of possible choices for the life of the startup. Since finding the best path possible is key to success, we have developed a Genetic Algorithm that improves choices over generations.
There are different types of elements (aka "actions") that we might encounter in the map, each with different possible values and a global score. For example:
There are also different probabilities for each action to appear, depending on the quarter of the map.
Learn more about the map in the dedicated information tab "The map" at www.geneticstartups.com.
Our web implementation provides several features, ranging from "creating new maps" to "telling a story".
We also provide some room for exploration and customization, through the settings:
Learn more about usage in the dedicated information tab "Using the application" at www.geneticstartups.com.
Our evolutionary algorithm starts by defining a random population of individual potential solutions at the beginning.
Each one of those potential solutions (each individual) is defined as a binary array, represented as a set of genes (each either a "0" or a "1"). These genes are grouped in chromosomes, encoding both the start cell and the movements of each individual.
After the initial random population is created, the elements are evaluated and sorted based on their "fitness". The best candidate is selected and displayed.
Then, a new generation has to be created. Every new generation is calculated by performing 3 operations: selection, crossover and mutation.
Learn more about our implementation in the "Algorithm details" tab of the info section at www.geneticstartups.com.
Below a front-end and a back-end diagram are displayed. Please refer to the architecture piece in the info section of the app for more details.
- This project was created with:
rails new . --webpack=react - The homepage controller was later created with:
rails g controller pages home - Jest was installed running:
bin/yarn add jest babel-jest
- Seems it is necessary to run
rails db:migrateprior to the tests (or disable the DB config). - Then, run:
rails test- Can also run tests in specific files:
rails test <test_file.rb>(e.g.rails test test/models/map_model_test.rb)
- Can also run tests in specific files:
- To run React/JS tests:
bin/yarn test
rails s
- First build it:
docker build -t <imageName> . - Then run it:
docker run -p 3000:3000 <imageName>- Running in background and redirecting logs example:
nohup docker run -p 3000:3000 romenrg/geneticstartups:0.0.1 > ../logs/logs.log 2>&1 & disown
- Running in background and redirecting logs example:
Any constructive contributions (e.g. PRs or issues) are welcome. Please feel free to propose changes following the contributing guideline.
Refer to the parent repository for implementations in different technologies / frameworks.