Abstract
Complex optimization problems are often associated to large search spaces and consequent prohibitive execution times in finding the optimal results. This is especially relevant when dealing with dynamic real problems, such as those in the field of power and energy systems. Solving this type of problems requires new models that are able to find near-optimal solutions in acceptable times, such as metaheuristic optimization algorithms. The performance of these algorithms is, however, hugely dependent on their correct tuning, including their configuration and parametrization. This is an arduous task, usually done through exhaustive experimentation. This paper contributes to overcome this challenge by proposing the application of sequential model algorithm configuration using Bayesian optimization with Gaussian process and Monte Carlo Markov Chain for the automatic configuration of a genetic algorithm. Results from the application of this model to an electricity market participation optimization problem show that the genetic algorithm automatic configuration enables identifying the ideal tuning of the model, reaching better results when compared to a manual configuration, in similar execution times.
This work has received funding from FEDER Funds through COMPETE program and from National Funds through (FCT) under project MAS-Society (PTDC/EEI-EEE/28954/2017). This work has been supported by National Funds through FCT - Portugal and CAPES - Brazil, under project 2019.00141.CBM Desenvolvimento de Técnicas de Inteligência Artificial para a Otimização de Sistemas de Distribuição de Energia Elétrica and by the R&D Project “Continental Factory of Future, (CONTINENTAL FoF)/POCI-01-0247-FEDER-047512”, financed by the European Regional Development Fund (ERDF), through the Program “Programa Operacional Competitividade e Internacionalização (POCI)/PORTUGAL 2020”, under the management of aicep Portugal Global – Trade & Investment Agency.
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Oliveira, V. et al. (2022). Automatic Configuration of Genetic Algorithm for the Optimization of Electricity Market Participation Using Sequential Model Algorithm Configuration. In: Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A. (eds) Progress in Artificial Intelligence. EPIA 2022. Lecture Notes in Computer Science(), vol 13566. Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_21
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