The groundwork is done - taking a break before getting something released!
130 karma · joined June 13, 2019
The groundwork is done - taking a break before getting something released!
- _Algorithm A_ implemented by _Researcher A_ performs different to _Algorithm A_ implemented by _Researcher B_.
- _Algorithm A_ outperforms _Algorithm B_ in _Researcher A's_ study.
- _Algorithm B_ outperforms _Algorithm A_ in _Researcher B's_ study.
That's a simple case... and it can come down to many different factors which are often omitted in the publication. It can drive PhD students mad as they try to reproduce results and understand why theirs don't match!
[1] https://link.springer.com/article/10.1007/s42979-020-00265-1
The data science ecosystem isn’t quite there yet, but you can still do some things easily if you want to.
If you’re interested, you may want to check out my book on the topic https://datacrayon.com/shop/product/data-analysis-with-rust-...
npm ERR! code E404Fixing the Paypal button issue will be more tricky! I just tried it in Firefox and it's fine, but I'm seeing temperamental issues in Safari. Will look into it further - thanks for the heads up.
These add a non-repayable, tax-free maintenance grant known as a 'stipend'. In 2018/19 this is worth a minimum of £14,777 and it can be used towards living costs. https://www.prospects.ac.uk/postgraduate-study/phd-study/phd-studentships2. Wanted to try something different... last software engineer post was 2010. I became very interested in Evolutionary Computation, so I went for a sponsored PhD position.
Uh oh! You have used a non-permitted word (style)` error: api errors (status 401 Unauthorized): The given API token does not match the format used by crates.io.
Tokens generated before 2020-07-14 were generated with an insecure random number generator, and have been revoked. You can generate a new token at https://crates.io/me.
For more information please see https://blog.rust-lang.org/2020/07/14/crates-io-security-advisory.html. We apologize for any inconvenience.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.
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!
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_...), 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.