A nature-inspired approach to computing that attempts to mimic evolution for code. This method has fallen out of favour of late because it is just not very good in practice, (e.g. Brauer et al. 2002) at least as naïvely implemented. The kind of problems that it seems like it might to solve, symbolic regression, have alternative that do pretty good, like neural automata, neural transformers, or Bayesian Symbolic regressions (Jin et al. 2020).
Nonetheless there is some interesting theory here, some interesting history and it is possibly the right tool for some jobs.
To consider, connection to adversarial learning, connections to optimisation theory, particle filters, importance sampling…
Hence, this notebook.
TBC, maybe.
Incoming
PyGAD
evotorch
References
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