New metaheuristic approaches for optimal stimuli selection
Niemensivu, Timi (2024-08-26)
New metaheuristic approaches for optimal stimuli selection
(26.08.2024)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
avoin
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2024083067504
https://urn.fi/URN:NBN:fi-fe2024083067504
Tiivistelmä
In psychological testing, specifically with repeated testing of the same subjects, the
optimal choice of used stimuli is essential. In repeated testing, two similar test
versions are necessary since the test subject always learns something from the test
itself, which means conducting the same test twice would lead to errors in estimates.
Other reasons for the need for two test versions include research settings where
interest is in the effect of a certain stimuli characteristic.
Optimal stimuli selection has received surprisingly little attention from the academic
community, and many state-of-the-art optimization methods have not been used
to tackle the issue. In this thesis, two new metaheuristic approaches for stimuli
selection are proposed. The proposed methods will be based on iterated local search
and scatter search metaheuristics. The new methods are compared to the simulated
annealing-based method, which has previously seen its fair share of use.
The methods were compared with various real-life and simulated datasets in terms of
optimality of solutions and used computational time. Resulting test versions were
also compared by inspecting descriptive statistics, as the mathematically optimal
solution is not guaranteed optimal in terms of practical research use. A comparison
of the methods showed that scatter search seemed the best at finding the optimum
with the drawback of the computing times getting out of hand in the larger dataset.
Simulated annealing showed its strengths as a good all-rounder, while the iterated
local search was fasted with the least-optimal solutions.
optimal choice of used stimuli is essential. In repeated testing, two similar test
versions are necessary since the test subject always learns something from the test
itself, which means conducting the same test twice would lead to errors in estimates.
Other reasons for the need for two test versions include research settings where
interest is in the effect of a certain stimuli characteristic.
Optimal stimuli selection has received surprisingly little attention from the academic
community, and many state-of-the-art optimization methods have not been used
to tackle the issue. In this thesis, two new metaheuristic approaches for stimuli
selection are proposed. The proposed methods will be based on iterated local search
and scatter search metaheuristics. The new methods are compared to the simulated
annealing-based method, which has previously seen its fair share of use.
The methods were compared with various real-life and simulated datasets in terms of
optimality of solutions and used computational time. Resulting test versions were
also compared by inspecting descriptive statistics, as the mathematically optimal
solution is not guaranteed optimal in terms of practical research use. A comparison
of the methods showed that scatter search seemed the best at finding the optimum
with the drawback of the computing times getting out of hand in the larger dataset.
Simulated annealing showed its strengths as a good all-rounder, while the iterated
local search was fasted with the least-optimal solutions.