2.2 KiB
Ideas
- Improve tournament selection testing
- Refactor models testings
- Add more context to errors (at least the method/struct name) (https://dave.cheney.net/2016/04/27/dont-just-check-errors-handle-them-gracefully)
- Add more example usage
- Implement operators described in http://www.ppgia.pucpr.br/~alceu/mestrado/aula3/IJBB-41.pdf
- Implement Particle Swarm Optimization
- http://deap.readthedocs.io/en/master/
- http://pyevolve.sourceforge.net/intro.html#ga-features
- http://www.dmi.unict.it/mpavone/nc-cs/materiale/moscato89.pdf
- Serialize with http://labix.org/gobson, maybe
Code style
Guidelines
- Keep names short
- Aim for 100 characters per line
Variable declaration
// Good
x := 42
// Bad
var x = 42
Naming convention
Please inspire yourself from the existing algorithms before implementing, the naming conventions are easy to grasp.
Parallelism and random number generation caveat
Genetic algorithms are notorious for being embarrassingly parallel. Indeed, most calculations can be run in parallel because they only affect part of the GA. Luckily Go provides good support for parallelism. As some gophers may have encountered, the math/rand module can be problematic because there is a global lock attached to the random number generator. The problem is described in this StackOverflow post. This can be circumvented by providing each population with it's own random number generator.
Talking about parallelism, there is a reason why the populations are run in parallel and not the individuals. First of all for parallelism at an individual level each individual would have to be assigned a new random number generator, which isn't very efficient. Second of all, even though Golang has an efficient concurrency model, spawning routines nonetheless has an overhead. It's simply not worth using a routine for each individual because operations at an individual level are often not time consuming enough.
Performance
go test -bench . -cpuprofile=cpu.profgo tool pprof -pdf gago.test cpu.prof > profile.pdf