xega

Implementation of a scalable, highly configurable, and e(x)tended architecture for (e)volutionary and (g)enetic (a)lgorithms. Multiple representations (binary, real-coded, permutation, and derivation-tree), a rich collection of genetic operators, as well as an extended processing pipeline are provided for genetic algorithms (Goldberg, D. E. (1989, ISBN:0-201-15767-5)), differential evolution (Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) doi:10.1007/3-540-31306-0), simulated annealing (Aarts, E., and Korst, J. (1989, ISBN:0-471-92146-7)), grammar-based genetic programming (Geyer-Schulz (1997, ISBN:978-3-7908-0830-X)), and grammatical evolution (Ryan, C., O'Neill, M., and Collins, J. J. (2018) doi:10.1007/978-3-319-78717-6). All algorithms reuse basic adaptive mechanisms for performance optimization. Sequential or parallel execution (on multi-core machines, local clusters, and high performance computing environments) is available for all algorithms. See https://github.com/ageyerschulz/xega/tree/main/examples/executionModel.

Last 30 days

This package has been downloaded 431 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 51 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 6,785 times in the last 365 days. That's a lot of interest! Someone might even write a blog post about it. The day with the most downloads was Jul 21, 2024 with 71 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.