Module proper_sa

This module provides simulated annealing (SA) as search strategy for targeted property-based testing.

Copyright © 2017 Andreas Löscher and Kostis Sagonas

Version: Jun 2 2023 21:10:48

Behaviours: proper_target.

Authors: Andreas Löscher.

Description

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)

Function Index

get_last_fitness/0returns the fitness of the last accepted solution and how many tests old the fitness is.
reset/0restart the search starting from a random input.

Function Details

get_last_fitness/0

get_last_fitness() -> {integer(), proper_target:fitness()}

returns the fitness of the last accepted solution and how many tests old the fitness is

reset/0

reset() -> ok

restart the search starting from a random input


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