Alright finished another round of #wakegp tests. It seems my invented method for parsimony pressure is effective and also has positive impact on fitness. This confirms findings or opinions of other researchers that bloat has negative impact on fitness.

Now started another round which will end in ~11h to see the effect of deletion mutation on fitness and program size, if any. Current experiments suggest that deletion mutation has no effect on either, not this side or the other side.

PS: I just hope when I want to publish the paper, not having a bachelor won't be problematic. However I don't have much of a problem about my paper getting rejected. Publishing the paper is a side effect with benefit. Results are my goals.

#EvolutionaryML #EC #AI #ML #MachineLearning #GeneticProgramming #LinearGeneticProgramming
#EvolutionaryMachineLearning #GP #bioinspiredcomputing #BioinspiredML #academia #academic #academics

I like productivity! So I've found the best #FFT size for #Wakegp. Some experiments about the #parsimonypressure method I have done shows effectiveness of my simple method. Now I'm experimenting on that to find out the sweet spot.

Regarding instructions, I already have done experiments. And the results were unexpected. Adding functions like Sine, Cosine, Logarithm and Square root have negative impact on fitness.

There are still a lot of parameters which I need to fine tune. Mutation rates, number of demes, tournment size, reproduction rate, crossover rate and a lot more. And I also need to experiment with #LexicaseSelection

After all these and when I have runs producing accurate enough programs, I should learn how to optimize the found programs to "summarize" them. I really hope compilers like #LLVM and #GCC already do so. I have doubts but I think most likely, they already have heuristics to summarize programs while keeping the logic.

God willing, when #repebble(the new #pebble #smartwatch) comes to market, I could try to see if I can use it there. Then #repebble would be the first industry application of my research.

cc @lspector

Edit: Regarding the simple parsimony pressure method I invented, I'm pretty sure already someone has invented it but I haven't found it in the literature. Just like tons of other things I invent or discover in Math or CS and then realize it has already been discovered or invented.

#MachineLearning #EvolutionaryMachineLearning #EvolutionaryML #ML #ArtificialIntelligence #AI #wakeworddetection #wake_word_detection #hotworddetection
#GeneticProgramming #LinearGeneticProgramming #MachineLearning

One idea I'm thinking about for #GeneticProgramming

In GP, we usually need to evolve multiple subpopulations named "demes" rather than one big population as the converge on smaller population happens faster. Then we have small, infrequent migrations between subpopulations, like 5% of population of each subpopulation/deme.

#GP #GeneticProgramming #EvolutionaryAlgorithms #EA #GeneticAlgorithm #EvolutionaryMachineLearning #AI #ML #EML #research #researching #Question #Idea

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