
Show HN: My Book on Evolutionary Algorithms, Written in Python Notebooks - shahinrostami
https://shahinrostami.com/posts/search-and-optimisation/practical-evolutionary-algorithms/preface/
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Folcon
Your patron link to chord is broken which may confuse some people.

This looks really great!

To be honest I tend to tinker a bit in the simulation space so this all is
really interesting. It seems like a github pledge is better for you than
patron at the moment, as you keep all the money, would you prefer that?

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shahinrostami
Thank you for spotting that and letting me know, I'll fix it now!

I'm glad you like it! I'm trying out both GitHub sponsors and Patreon at the
moment to fund more projects, I'm not sure which will stick! So far it looks
like Patreon has better support for giving back to your backers, but GitHub
also doesn't take a cut. I am of course very grateful and flattered - please
choose whichever you prefer!

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Folcon
Well if you plan on mirroring access via discord, then github's better as I
can support you =)...

If you are thinking of making patron only content then that's better.

Hence why I was asking ;)...

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shahinrostami
Ah I see! Let's go with Patreon then :) As it grows I think it will allow for
better engagement from supporters. Plenty of features for sharing updates and
getting feedback that are perhaps worth the cut they take!

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yawgmoth
Cool! Bought a copy. Some questions

\- Have you looked into Latin Hypercube Sampling for your initial population
creation? Or in general, some initial set that tries to cover the space better
than a pseudo-random sample? LHS tries to guarantee some distance between the
points, which can be beneficial for rather non-convex problems.

\- For very high dimensionality, have you considered classification to
identify which dimensions are significant?

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shahinrostami
Hello! I didn't realise this post received any attention, I thought it had
faded away!

With a sufficient population size, taking advantage of better sampling
techniques for initial population generation can make a significant
difference. I've used LHS and modified LHS approaches before, but I wanted to
keep things simple, at least in the earlier parts of the book!

Something I've worked on with a colleague recently is using a more data
science with pre-optimisation exploration
([https://link.springer.com/chapter/10.1007/978-3-030-43722-0_...](https://link.springer.com/chapter/10.1007/978-3-030-43722-0_1)),
hoping to do more work on this soon. With regards to high dimensionality in
the search space, this kind of approach could be useful.

Previously I've been more interested in high-dimensional objective space and
how to deal with them, primarily using progressive preference articulation.

If you have any requests for additional sections in the book I would love to
hear them! Something high up on my list is doing a section or two on my
neuroevolution algorithm, but keeping it at the right level is tricky.

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yawgmoth
I'm not sure if dimensionality reduction fits in the book, but it is one other
thing we are considering in our problem. I am HOPING that with good coverage
of early populations, we can 1. drop an order of magnitude off our dimensions
(probably not) 2. select the most significant dimensions to apply our
evolution techniques.

Do you have any favorite approaches to reduction?

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shahinrostami
It depends on the nature of the problem! Are you able to share any specifics
e.g. context, number and relationship between decision variables, number of
objectives (single or multi?), the optimisation operators in use, and so on?

My main use of dimensionality reduction is to improve visualisation to support
decision making. I've seen PCA and differential evolution approaches in the
decision space to show promising results on benchmarks.

