Version 0.6.0 of the Watchmaker Framework for Evolutionary Computation is now available for download. This release incorporates several minor changes that I’ve been making over the last few months. Consult the changelog for full details, but here are the highlights:
Numerous Improvements to the Evolution Monitor and other Swing Components
The Watchmaker Swing library provides a collection of GUI components that simplify the process of building user interfaces for evolutionary programs. These components have received many improvments for version 0.6.0. As well as controls for manipulating evolution parameters while the program is running, the library also provides an Evolution Monitor component. This provides real-time information about the state of the program, including a view of the fittest candidate so far and a graph showing changes in population fitness over time.
Upgraded to Uncommons Maths 1.2
This means even faster RNGs are available for you to use. It also means that we now use the Uncommons Maths Probability class rather than duplicating it in the framework (this means you may have to change some imports in your code when upgrading from Watchmaker 0.5.x).
Caching Fitness Evaluator
Version 0.6.0 introduces the CachingFitnessEvaluator class. This is a wrapper that provides caching for existing FitnessEvaluator implementations. The results of fitness evaluations are cached so that if the same candidate is evaluated twice, the expense of the fitness calculation can be avoided the second time. The cache uses weak references in order to avoid memory leakage.
Caching of fitness values can be a useful optimisation in situations where the fitness evaluation is expensive and there is a possibility that some candidates will survive from generation to generation unmodified. Programs that use elitism are one example of candidates surviving unmodified. Another scenario is when the configured evolutionary operator does not always modify every candidate in the population for every generation.
Caching of fitness scores is provided as an option rather than as the default Watchmaker Framework behaviour because caching is only valid when fitness evaluations are isolated and repeatable. An isolated fitness evaluation is one where the result depends only upon the candidate being evaluated. This is not the case when candidates are evaluated against the other members of the population.
Mona Lisa Example
After seeing Roger Alsing’s evolution of the Mona Lisa, I was inspired to try to reproduce it using the Watchmaker Framework. I didn’t follow Roger’s methodology but I have come up with something similar. My results aren’t as impressive as his latest efforts but may be interesting anyway. This example was actually included in version 0.5.1 but I didn’t draw attention to it. In 0.6.0 I’ve improved performance and used it to demonstrate the Watchmaker GUI components mentioned above. You can try it for yourself here. Maybe you can come up with a combination of parameters that works better than the defaults I have provided?
Useful Watchmaker Links
- Full Changelog
- API Documentation
- Demo Applets: Travelling Salesman, Biomorphs, Sudoku Solver, Mona Lisa
If you are new to Evolutionary Computation in Java, these previous articles may be of interest: