Copyright © 2017 Andreas Löscher and Kostis Sagonas
Version: Sep 22 2024 06:06:44
Behaviours: proper_target.
Authors: Andreas Löscher.
This module provides simulated annealing (SA) as search strategy for targeted property-based testing. SA is a local search meta-heuristic that can be used to address discrete and continuous optimization problems.
SA starts with a random initial input. It then produces a random input in the neighborhood of the previous one and compares the fitnessof both. If the new input has a higher fitness than the previous one, it is accepted as new best input. SA can also accepts worse inputs with a certain probbability. (more information)get_last_fitness/0 | returns the fitness of the last accepted solution and how many tests old the fitness is. |
reset/0 | restart the search starting from a random input. |
get_last_fitness() -> {integer(), proper_target:fitness()}
returns the fitness of the last accepted solution and how many tests old the fitness is
reset() -> ok
restart the search starting from a random input
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