lau15

<p>15 abstract cities for which a traveling salesman solution is sought.
Solution: A path with a length of 291.</p>

datasets

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)), grammatical evolution
(Ryan, C., O'Neill, M., and Collins, J. J. (2018) <doi:10.1007/978-3-319-78717-6>),
and grammatical differential evolution (O'Neill, M. and Brabazon, A. (2006) in
Arabinia, H. (2006, ISBN:978-193-241596-3).
All algorithms reuse basic adaptive mechanisms for performance optimization.
For 'xega''s architecture, see Geyer-Schulz, A. (2025) <doi:10.5445/IR/1000187255>.
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>.

lau15: The problem environment lau15

Description

Usage

Arguments

Format

References

See Also

Examples