Year: 2023
Type: dissertation
Abstract: This thesis presents significant contributions in the field of iterative stochastic heuristics. Various aspects related to the comparison and improvement of optimisation algorithms are addressed. Firstly, a methodology is proposed to fairly compare the performance of algorithms run on different machines, ensuring an equitable allocation of computational resources. Additionally, a methodology based on stochastic dominance is introduced to compare the performance of optimisation algorithms as random variables. Furthermore, the relationship between Hamming distance and the quadratic assignment problem is analysed. A general early stopping method for learning policies in episodic problems, which does not require specific problem information, is developed. In summary, this thesis contributes to the understanding and improvement of iterative stochastic heuristics in the field of optimisation.
Author: Arza González, Etor
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7025329171