1 code implementation • 21 Mar 2024 • Yifan He, Claus Aranha
In this study, we use Genetic Programming (GP) to compose new optimization benchmark functions.
1 code implementation • 9 Oct 2023 • Alexandre Mascarenhas, Yuri Lavinas, Claus Aranha
Using this framework, we investigate components that were proposed in several popular DOP algorithms.
no code implementations • 31 Jul 2023 • Yuri Lavinas, Marcelo Ladeira, Gabriela Ochoa, Claus Aranha
In terms of decision space behavior, we see a diverse set of the trajectories of the STNs in the analytical artificial problems.
no code implementations • 22 Dec 2022 • Fabio Tanaka, Claus Aranha
While our approach is more realistic and doesn't require an arbitrary separation of processes during evolution, it also makes the problem more complex because the search space for this single genome becomes larger and any mutation to the genome affects "brain" and the "body" at the same time.
no code implementations • 21 Oct 2022 • Luiz Fernando Silva Eugênio dos Santos, Claus Aranha, André Ponce de Leon F de Carvalho
We apply the knowledge of urban settings established with the study of Land Use and Transport Interaction (LUTI) models to develop reward functions for an agent-based system capable of planning realistic artificial cities.
1 code implementation • 8 Sep 2022 • Yifan He, Claus Aranha, Tetsuya Sakurai
We compare the proposed method with PushGP, as well as a method using subprograms manually extracted by a human.
no code implementations • 19 May 2022 • Mohiuddeen Khan, Claus Aranha
This communication game requires the playing agents to be very sophisticated to win.
1 code implementation • 25 Mar 2022 • Yuri Lavinas, Marcelo Ladeira, Gabriela Ochoa, Claus Aranha
This study introduces a new methodology to investigate the effects of the final configuration of an automatically designed algorithm.
1 code implementation • 27 Jan 2022 • Yuri Lavinas, Claus Aranha, Gabriela Ochoa
Understanding the search dynamics of multiobjective evolutionary algorithms (MOEAs) is still an open problem.
1 code implementation • 21 Dec 2021 • Yuri Lavinas, Marcelo Ladeira, Claus Aranha
MOEA/D with Partial Update can mitigate common problems related to population size choice with better convergence speed in most MOPs, as shown by the results of hypervolume and number of unique non-dominated solutions, the anytime performance and Empirical Attainment Function indicates.
1 code implementation • 13 Sep 2021 • Yuri Lavinas, Abe Mitsu Teru, Yuta Kobayashi, Claus Aranha
Thus, our adaptive mechanism mitigates problems related to the choice of the number of weight vectors in MOEA/D, increasing the final performance of MOEA/D by filling empty areas of the objective space while avoiding premature stagnation of the search progress.
1 code implementation • 19 Nov 2020 • Felipe Vaz, Yuri Lavinas, Claus Aranha, Marcelo Ladeira
Finding good solutions for Multi-objective Optimization (MOPs) Problems is considered a hard problem, especially when considering MOPs with constraints.
1 code implementation • 15 Mar 2020 • Yifan He, Claus Aranha
Portfolio optimization is a financial task which requires the allocation of capital on a set of financial assets to achieve a better trade-off between return and risk.
1 code implementation • 20 Jan 2020 • Yuri Lavinas, Claus Aranha, Marcelo Ladeira, Felipe Campelo
Recent studies on resource allocation suggest that some subproblems are more important than others in the context of the MOEA/D, and that focusing on the most relevant ones can consistently improve the performance of that algorithm.
no code implementations • WS 2019 • Yoshinobu Kano, Claus Aranha, Michimasa Inaba, Fujio Toriumi, Hirotaka Osawa, Daisuke Katagami, Takashi Otsuki, Issei Tsunoda, Shoji Nagayama, Dol{\c{c}}a Tellols, Yu Sugawara, Yohei Nakata
no code implementations • 11 Jun 2019 • Icaro Marcelino Miranda, Claus Aranha, Marcelo Ladeira
The analysis of electroencephalogram (EEG) waves is of critical importance for the diagnosis of sleep disorders, such as sleep apnea and insomnia, besides that, seizures, epilepsy, head injuries, dizziness, headaches and brain tumors.
1 code implementation • 19 Apr 2019 • Fabio Henrique Kiyoiti dos Santos Tanaka, Claus Aranha
In this paper we propose the use of Generative Adversarial Networks (GAN) to generate artificial training data for machine learning tasks.
no code implementations • 25 Dec 2018 • Yusei Miura, Tetsuya Sakurai, Claus Aranha, Toshiya Senda, Ryuichi Kato, Yusuke Yamada
We compared our crystallization image recognition method with a high precision method using Inception-V3.
2 code implementations • 18 Jul 2018 • Felipe Campelo, Lucas S. Batista, Claus Aranha
We introduce the MOEADr package, which offers many of these variants as instantiations of a component-oriented framework.