Art
You need to provide overriding for the `NonDifferentiable` with the type of your individual, i.e. `Organism`. Basically, it's a wrapper type that keeps fitness function with its parameter & result...
I added an example of supervised learning problem by GA optimization of multi-layer perceptrons, see the notebook in [examples/MLP.ipynb](https://github.com/wildart/Evolutionary.jl/blob/master/examples/MLP.ipynb) It shows how to fill some missing functionality, when the individuals...
Thanks for bringing the topic. I've briefly looked at MPGA description and performance, and it seems like a good addition to the package. In any case, if you are willing...
I do not think that `DistributedArrays` is an answer. It covers only multi-core scenario. Even basic julia parallel computing routines are enough for scatter-gather computations that are required for evolutionary...
> but that wouldn't be easy at all, since the ga function would need major changes. Which you already doing in #43 :wink: Anyway, I think the right approach would...
The #49 should provide a easier way of implementing parallelized versions of existing algorithms by introducing a series of new states with appropriate parallel `update_state!` implementations.
> calculate the fitness for many individuals at once. That might work. Currently, the fitness evaluation done by [`value` call](https://github.com/wildart/Evolutionary.jl/blob/8555f347e3a18a0a64b9d4d39d9b26c6b8fe8c2d/src/es.jl#L107) with the objective and the individual parameters. If a broadcast...
I added a simple override for multi-threaded fitness evaluation: https://wildart.github.io/Evolutionary.jl/dev/tutorial/#Parallelization. Look up the dev part of documentation for information on creating additional overrides for parallel fitness evaluation: https://wildart.github.io/Evolutionary.jl/dev/dev/#Parallelization. - Note:...
`converged(result)`
Look at the [OptimizationResults](https://wildart.github.io/Evolutionary.jl/dev/tutorial/#Obtaining-results-1) interface, you may find what you need there.