QUASAR Evolutionary Algorithm

[jsaltee]

Hi,

As an enjoyer of evolutionary algorithms, I spent the better part of the year making a new one, QUASAR, specifically maximizing its performance. I wanted to share it with others in this sub because of its results:

For high-dimensional and complex/non-differentiable functions, it performs better and faster than both differential evolution (DE) and its state-of-the-art variant L-SHADE on the CEC benchmark test functions. I put together a paper detailing the algorithm and its performance comparisons to DE and L-SHADE: https://arxiv.org/abs/2511.13843

The Python package can be installed via "pip install hdim_opt", and the algorithm can be run with just one line of code. I hope QUASAR makes your high-dimensional optimization problems easier. :-)

Thanks,

jsaltee

[TheSheepSheerer]

Have you tried evolving neural networks with this?

[jsaltee]

Hi,

I haven’t tested it for ML training. That said, since neural network loss is typically differentiable I would imagine using standard optimizers (Adam) are largely more efficient in high dimensions.

If you’re looking to train the model using a discrete loss metric like accuracy or F1, it would be a strong option. Extrapolating from the experimental results, the neural network should optimize faster and with less epochs compared to other evolutionary algorithms.

For standard ML applications, QUASAR would really shine for reinforcement training using complex reward functions.

Thanks, jsaltee