For the past week I've been working almost exclusively on a branch of the flocking algorithm used in FishBirds to create a Predator vs Prey program, however it has proved to be much more fruitful than that.
Since starting last friday I have learned how to implement effective natural selection and semi random evolution like this:
- Predator dies constantly.
- More prey is introduced when under a certain number.
- Predator and preys values (speed, agility, weight, sight) are decided randomly with a bell curve around set values.
- Some Predators are born better and hence do much better, and stay alive much longer.
- Some prey are born better at escaping predators and hence also stay alive much longer.
I did however find this system lacking in its complexity and method of "evolution" so I decided to try my hand at a genetic algorithm. I wanted something where the offspring was given values similar to the parent, but that could also drastically deviate from them. So I put together a moving mean. And implemented 2 different types of reproduction, time and "well fed". This more detailed list:
- Predators die constantly, and die faster the smaller the flock is.
- Predators reproduce when a certain amount of prey is eaten in a certain timespan and when they are of reproducing age (this is to stop the predator slamming into a group and then all its children reproduce causing a huge increase in number of predators).
- Predators spawn one offspring, and stay alive (this is to for competition between generations).
- Prey reproduces by staying alive for certain amount of time. Prey then has 2 offspring, and dies.
- The moving mean: The normal of the gaussian distribution of random values for the offspring are the current values of the parent. This ensures a good development of genetics and keeps the gene pool relatively diverse.
Problems that I have encountered:
- If the flock is under a certain number and there are a certain number of predators. The predators will continue to live on the small amount of food that the prey provide.
- Performance. In a perfect world I would be able to have a flock size of anything. Unfortunately the components of a computer, and Processing have limits. I have been pushing these and I think its time to move on to something more powerful, at least for a language (Discussion in my next post)
- The flock is limited at the moment. It stops multiplying once it reaches a certain size. This was implemented to reduce performance issues and hence has stopped me producing a more realistic simulation.
- I couldn't work out how to implement a method of generation counting. I'm sure with more time I could work out how to do it, but I don't want to spend any longer on this project.
I've published a final video to Vimeo.
In conclusion I think I am now significantly more adept at coding than I was last week.
I had a more in depth exploration of OOP, and became much more experienced in balancing systems. I learned how to implement some more advanced random statistical tools to give more useful data. And I learned how to implement genetic algorithms.
No comments:
Post a Comment