Dissertation defense (November 23, 2020): Marcello Alberto Soares Serqueira

Student: Marcello Alberto Soares Serqueira

Title: HBRKGA: A Population-based Hybrid Approach to Hyperparameter Optimization for Neural Networks

Advisor: Eduardo Bezerra da Silva (advisor), Pedro Henrique González Silva (co-advisor).

Committee: Eduardo Bezerra da Silva (president),  Pedro Henrique González Silva (CEFET/RJ), Diego Brandão (CEFET/RJ), Igor Machado Coelho (UFF).

Day/Time: November 23, 2020 / 14h

Room: https://meet.google.com/xjd-mbbe-jsr

Abstract: In recent years, large amounts of data have been generated, and computer power has kept growing. This scenario has led to a resurgence in the interest in artificial neural networks. One of the main challenges in training effective neural network models is finding
the right combination of hyperparameters to be used. Indeed, the choice of an adequate approach to search the hyperparameter space directly influences the accuracy of the resulting neural network model. Common approaches for hyperparameter optimization are Grid
Search, Random Search, and Bayesian Optimization. There are also population-based methods such as CMA-ES. In this paper, we present HBRKGA, a new population-based approach for hyperparameter optimization. HBRKGA is a hybrid approach that combines
the Biased Random Key Genetic Algorithm with a Random Walk technique to search the hyperparameter space efficiently. Several computational experiments on eight different datasets were performed to assess the effectiveness of the proposed approach. Results
showed that HBRKGA could find hyperparameter configurations that outperformed (in terms of predictive quality) the baseline methods in six out of eight datasets while showing a reasonable execution time.


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