Source code of ZLI Team for the First AI4TSP Competition

This code can be used to reproduce the experiments of the ZLI-Team for the AI for TSP Competition.
(see also: https://github.com/paulorocosta/ai-for-tsp-competition and https://www.tspcompetition.com)
Specifically, this code tackles Track 1 (surrogate-based optimization) from that competition.
In that track, one 55-node instance of the
time-dependent orienteering problem with stochastic weights and time windows (TD-OPSWTW)
has to be solved by using surrogate-based optimization algorithms.
The solution approach combines
(1) dimensionality reduction,
(2) caching of solutions,
(3) modeling the feasiblity of solutions via classification,
(4) self-adaptive evolutionary optimization
and (5) a surrogate model of the fitness function with ensemble-based Gaussian Process Regression.