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I'm curious, what are the most popular heuristic algorithms? I've been making use of Genetic Algorithms via PyGAD. But I'm curious if there are other heuristic implementations that are flexible to function input.

For example, PyGAD can be used for linear regression and parameter weighting.

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  • $\begingroup$ Are you asking about algorithms or software packages to use these algorithms? $\endgroup$
    – fontanf
    Oct 7 at 15:49
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    $\begingroup$ Both, an example answer would be 'The genetic algorithm is the most popular heuristic model and its most popular python implementation is PyGAD, see <link>' $\endgroup$
    – jbuddy_13
    Oct 11 at 15:39

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Good luck running a metaheuristic popularity contest. Every one has its (ardent) proponents. You might want to take a look at the Evolution Computation Bestiary, a glossary of just the zillions of metaheuristics based (however loosely) on some aspect of evolution. My personal favorite is slime mold optimization, not due to its performance (I've never tried it) but just due to the name.

Since you are already using GAs, let me point out that they may be more flexible than they appear. When they were first introduced, there was always a 1-1 relation between "genes" (positions in a "chromosome") and variables in the original problem. That is still the most common use, but a while back people introduced the idea of a decoding step (a transformation from the chromosome to a solution), which adds some flexibility. In conjunction with the idea of permutation-type chromosomes (where the chromosome is a permutation of integer indices $1,\dots,n$), this made GAs more useful for scheduling and sequencing problems, among others.

I've used GAs for a number of problems, and I can't recall the last time I used any other metaheuristic -- but that's partly because I'm too lazy to research other metaheuristics if I can get a GA to do a decent job. (Problem-specific heuristics are a separate matter.)

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