Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Automated design of specialized variation operators using a generation hyper heuristic for the multi objective quadratic assignment problem(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) Morales Paredes, Adrián Isaí; Terashima Marín, Hugo; emimmayorquin; Falcón Cardona, Jesús Guillermo; School of Engineering and Sciences; Campus Monterrey; Coello Coello, Carlos ArtemioThe development of specialized, domain-specific operators has significantly enhanced the performance of evolutionary algorithms for solving optimization problems. However, creating such operators often requires substantial effort from human experts, making the process slow, resource-intensive, and heavily reliant on domain knowledge. To overcome these limitations, generation hyper-heuristics provide a framework for automating the design of variation operators by evolving combinations of heuristic components without direct expert input. In this work, a generation hyper-heuristic method based on grammatical evolution using the hypervolume indicator (HV) as part of its selection mechanism to automatically design variation operators (crossover and mutation) tailored to the multi-objective quadratic assignment problem (mQAP)—a challenging combinatorial optimization problem with many real-world applications—is proposed. The proposed method was used to generate variation operators following a grammar-defined search space. This generation was guided by six mQAP instances featuring 10, 20, and 30 variables with two and three objectives, leveraging MOEA/D as its multi-objective optimizer. During the generation process, the hyper-heuristic exhibited consistent improvements in the HV of the population throughout the evolutionary process, demonstrating its ability to evolve increasingly effective operators over time. To validate the hyper-heuristic output, the generated operators were evaluated on sixteen unseen and diverse mQAPinstances. From experimental results, the evolved operators consistently outperformed standard ones regarding median HV in all test instances. Statistical tests further indicate that these evolved operators possess strong exploration and exploitation capabilities for problems with permutation-based solution representations and behave distinctly from conventional operators. Pairwise comparisons confirmed their superiority over human-designed recombination operators such as PMX and CX in HV performance. These results highlight the potential of automated operator design in effectively solving complex combinatorial optimization problems, such as the mQAP. Beyond this specific case, the proposed framework contributes to the field of automated operator design by introducing a new methodology applicable to multi-objective combinatorial optimization scenarios.
- Hyper-heuristic Model Based on Neural Networks for Solving the Metaheuristic Composition Optimisation Problem in Continuous Domains(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-12) Tapia Avitia, José Manuel; Terashima Marín, Hugo; emimmayorquin; Pillay, Nelishia; Ortiz Bayliss, José Carlos; Amaya Contreras, Iván Mauricio; School of Engineering and Sciences; Campus Monterrey; Cruz Duarte, Jorge MarioMetaheuristics (MHs) have been proven to be powerful algorithms for solving highly non-linear and intricate optimisation problems over discrete, continuous, or mixed domains, with applications ranging from basic sciences to applied technologies. Nowadays, the literature is prolific with MHs based on outstanding ideas, but the researchers often recombine elements from other methods. To avoid the frenetic tendency of proposing methods more focused on metaphors than operations, a standard model has been proposed to customise population-based MHs, which uses simple heuristics or search operators extracted from well-known metaheuristics. The framework corresponding to this model can be found as Customising Optimising Metaheuristic via Hyper-heuristic Search (CUSTOMHyS), which facilitates implementing models that explore a heuristic space. Still, they are limited by the nature of the metaheuristics used in such models, as such algorithms does not consider the information gained from previous explorations to enhance the tailoring process. A field of action and improvement that has not been explored is the model implementation to take advantage of previous results and learns from them to boost the performance of the tailoring process. For that reason, we propose a hyper-heuristic model based on neural networks, which is trained with processed sequences of heuristics to identify patterns that one can use for generating modified metaheuristics. Being more specific, the task assigned to the neural network is to predict the simple heuristic from the collection or heuristic space to apply next, considering a sequence of heuristics already applied to the low-level problem. Using the neural networks, the challenge is to define how to generate metaheuristics with a high performance for tackling a family of optimisation problems. This research work propose a novel methodology that decomposes the metaheuristics into several subsequences of heuristics to train the neural network models. To prove the feasibility of the proposed model and training methodology, it is compared against generic well-known basic metaheuristics and other heuristic-based approaches, such as the unfolded MHs. The results evidence that the proposed model outperform an average of 86% of all scenarios; in particular, 91% of basic and 81% of unfolded approaches. Plus, it is worth to highlight the configurable capability of the proposed model: several experiments are carried out to explore a few control variables and show their effects in the model. It proves to be exceptionally versatile regarding the computational budget. After exploring and finding a suitable configuration, we perform an extended analysis of the training computational cost, and a study of the metaheuristics generated by the model. Moreover, we analyse the usage of previously generated metaheuristics on an unseen problem via a few strategies. The proposed model and its metaheuristics show their adaptation capabilities to unseen problems, proving to be a good alternative for real-world application problems.
- Feature transformations for improving the performance of selection hyper-heuristics on job shop scheduling problems(Instituto Tecnológico y de Estudios Superiores de Monterrey) Garza Santisteban, Fernando; Terashima Marín, Hugo; Özcan, Ender; School of Engineering and Sciences; School of Engineering and Sciences; Campus Monterrey; Amaya Contreras, IvanSolving Job Shop (JS) scheduling problems is a hard combinatorial optimization problem. Nevertheless, it is one of the most present problems in real-world scheduling environments. Throughout the recent computer science history, a plethora of methods to solve this problem have been proposed. Despite this fact, the JS problem remains a challenge. The domain it- self is of interest for the industry and also many operations research problems are based on this problem. The solution to JS problems is overall beneficial to the industry by generating more efficient processes. Authors have proposed solutions to this problem using dispatch- ing rules, direct mathematical methods, meta-heuristics, among others. In this research, the application of feature transformations for the generation of improved selection constructive hyper-heuristics (HHs) is shown. There is evidence that applying feature transformations on other domains has produced promising results; Also, no previous work was found where this approach has been used for the JS domain. This thesis is presented to earn the Master’s degree in Computer Science of Tecnolo ́gico de Monterrey. The research’s main goals are: (1) the assessment of the extent to which HHs can perform better on JS problems than single heuristics, and that they are not specific to the instances used to train them; and (2), the degree to which HHs generated with feature transformations are revamped. Experiments were carried out using instances of various sizes published in the literature. The research involved profiling the set of heuristics chosen, ana- lyzing the interactions between the heuristics and feature values throughout the construction of a solution, and studying the performance of HHs without transformations and by using two transformations found in the literature. Results indicate that for the instances used, HHs were able to outperform the results achieved by single heuristics. Regarding feature transforma- tions, it was found that they induce a scaling effect to feature values throughout the solution process, which produces more stable HHs, with a median performance comparable to HHs without feature transformations, but not necessarily better. Results are conclusive in terms of the objectives of this research. Nevertheless, there are several ideas that could be explored to improve the HHs, which are outlined and discussed in the final Chapter of the thesis. The following major contributions are derived from this research: (1) applying a se- lection constructive HH approach, with feature transformations, to the JS domain; (2) the rationale behind the JS subproblem dependance in terms of the solution paths followed by the heuristics, which has a great impact in the training process of the HHs; (3) a method to deter- mine the most suitable parameters to apply feature transformations, which could be extended for other domains of combinatorial optimization problems; and (4) a framework for studying HHs in the Job Shop domain.