Article
Computer Science, Artificial Intelligence
Emrullah Sonuc, Ender Ozcan
Summary: Metaheuristics, which provide high-level guidelines for heuristic optimization, have been successfully applied to complex problems. However, their performance varies depending on the initial settings and problem characteristics. Therefore, there is a growing interest in designing adaptive search methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Guo-Zhong Fu, Hong-Zhong Huang, Yan-Feng Li, Jie Zhou
Summary: A novel operator selection framework based on GD and MS is proposed to improve diversity and convergence. Experimental results show that the new algorithm outperforms other algorithms in terms of diversity and convergence.
Article
Computer Science, Artificial Intelligence
Liang Chen, Hanyang Wang, Darong Pan, Hao Wang, Wenyan Gan, Duodian Wang, Tao Zhu
Summary: In this paper, a dynamic multiobjective evolutionary algorithm (DMOEA) with an adaptive response mechanism selection strategy is proposed. The proposed algorithm combines an adaptive response mechanism selection (ARMS) strategy and a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It is tested on two groups of test instances and compared with other algorithms, and the results show its effectiveness.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jinhua Zheng, Qishuang Wu, Juan Zou, Shengxiang Yang, Yaru Hu
Summary: Responding quickly to environmental changes is crucial in solving dynamic multi-objective optimization problems (DMOPs). Most existing methods perform well on predicting individuals but struggle with improving the accuracy of the predicted population. This paper proposes an approach called RVCP, which combines an adjusted reference vector with a multi-objective evolutionary algorithm to predict the population and effectively tackle DMOPs.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Huantong Geng, Ke Xu, Yanqi Zhang, Zhengli Zhou
Summary: This study proposes a novel classification tree based adaptive operator selection strategy and designs a new differential evolution algorithm to improve the performance of multi-objective evolutionary algorithms. The experimental results demonstrate that the proposed approach outperforms other variants in benchmark tests.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Lin Shi, Yanyan Tan, Zeyuan Yan, Lili Meng, Li Liu
Summary: This study proposes a multiobjective evolutionary algorithm based on decomposition, which divides weight vectors into groups and assigns different reproduction operators to each group to handle complex multiobjective optimization problems. Comparative experiments have shown that this strategy has better performance.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Wu Lin, Qiuzhen Lin, Junkai Ji, Zexuan Zhu, Carlos A. Coello Coello, Ka-Chun Wong
Summary: This paper proposes a novel bicriteria assisted adaptive operator selection strategy for decomposition-based multiobjective evolutionary algorithms. By using two operator pools focusing on exploitation and exploration, and two criteria emphasizing convergence and diversity, a good balance between exploitation and exploration during evolutionary search can be achieved. The experimental results show that the proposed B-AOS outperforms existing state-of-the-art adaptive operator selection methods and can significantly improve performance on benchmark problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Engineering, Multidisciplinary
Noha Hamza, Ruhul Sarker, Daryl Essam, Saber Elsayed
Summary: The number of research works on dynamic constrained optimization problems has been increasing rapidly over the past two decades. However, no research on dynamic problems with changes in the coefficients of the constraint functions has been reported. In this paper, a new evolutionary framework with multiple novel mechanisms is proposed to deal with such problems, and the results demonstrate its significant contribution in achieving good quality solutions, high feasibility rates, and fast convergence in rapidly changing environments.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Engineering, Environmental
Paolo Maranzano, Philipp Otto, Alessandro Fasso
Summary: This paper proposes a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hidden dynamic geostatistical models (f-HDGM). The algorithm simultaneously selects the relevant spline basis functions and regressors for modeling the fixed effects, automatically shrinking irrelevant functional coefficients or the entire function for an irrelevant regressor. It is based on an adaptive LASSO penalty function with weights obtained from unpenalized f-HDGM maximum likelihood estimators.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2023)
Article
Engineering, Mechanical
Qiang He, Zheng Xiang, Peng Ren
Summary: In recent years, the dynamic multiobjective optimization problems have attracted great attention, and reusing experiences to establish prediction models has proven to be useful. However, existing methods overlook the importance of environmental selection. This research proposes a new algorithm based on environmental selection and transfer learning to effectively deal with dynamic multiobjective optimization problems.
NONLINEAR DYNAMICS
(2022)
Article
Computer Science, Information Systems
Yingbo Xie, Junfei Qiao, Ding Wang, Baocai Yin
Summary: The paper proposes a novel multiobjective optimization evolutionary algorithm, MOEA/D-IMA, based on improved adaptive dynamic selection strategies and elite archive strategy to enhance population diversity and convergence; experimental results show that MOEA/D-IMA significantly improves optimization performance when dealing with MOPs.
INFORMATION SCIENCES
(2021)
Article
Multidisciplinary Sciences
Matthew Putnins, Ioannis P. Androulakis
Summary: The evolution of complex genetic networks is shaped by adaptive and non-adaptive forces, both of which play critical roles in the development of a species and are intrinsically linked. Adaptive forces, influenced by the environment, result in selective pressure, while non-adaptive forces are not influenced by the environment.
Article
Computer Science, Artificial Intelligence
Hui Wang, Zichen Wei, Gan Yu, Shuai Wang, Jiali Wu, Jiawen Liu
Summary: This paper proposes a two-stage many-objective evolutionary algorithm, TS-DGPD, which accelerates convergence and maintains population diversity by using cosine distance and Lp norm, and increases selection pressure using dynamic generalized Pareto dominance. Experimental results show that the algorithm performs well in terms of convergence and diversity.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Automation & Control Systems
Jakob Drefs, Enrico Guiraud, Joerg Luecke
Summary: In this study, we combine two popular optimization approaches, variational optimization and evolutionary algorithms, to derive a novel variational approach for generative models. By using truncated posteriors as variational distributions and interpreting latent states as genomes of individuals, we apply evolutionary algorithms to optimize the variational bounds efficiently. The proposed approach shows significant improvements in competitive benchmarks for image denoising and inpainting, highlighting the importance of optimization methods for generative models.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Automation & Control Systems
Jakob Drefs, Enrico Guiraud, Joerg Luecke
Summary: We propose a combination of variational optimization and evolutionary algorithms for learning algorithms for generative models. This method is realized by using truncated posteriors as the family of variational distributions, and interpreting the variational parameters as genomes of individuals. The evolutionary algorithms effectively optimize the variational bound, and the approach shows promising results in various generative models and image processing tasks.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Frederic Lardeux, Jorge Maturana, Eduardo Rodriguez-Tello, Frederic Saubion
Article
Computer Science, Artificial Intelligence
Frederic Lardeux, Eric Monfroy, Eduardo Rodriguez-Tello, Broderick Crawford, Ricardo Soto
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Computer Science, Artificial Intelligence
Jintong Ren, Jin-Kao Hao, Eduardo Rodriguez-Tello, Liwen Li, Kun He
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Mathematics
Ricardo Soto, Broderick Crawford, Rodrigo Olivares, Cesar Carrasco, Eduardo Rodriguez-Tello, Carlos Castro, Fernando Paredes, Hanns de la Fuente-mella
Article
Computer Science, Artificial Intelligence
Miguel Angel Rodriguez-Garcia, Jesus Sanchez-Oro, Eduardo Rodriguez-Tello, Eric Monfroy, Abraham Duarte
Summary: The research focuses on the bandwidth optimization problem of embedding a graph in a two-dimensional grid, with CSP models showing remarkable performance in small to medium instances, while BVNS is capable of achieving equivalent or similar results in short run-time for small instances.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Sergio Cavero, Eduardo G. Pardo, Abraham Duarte, Eduardo Rodriguez-Tello
Summary: In this paper, the authors tackle the Cyclic Bandwidth Sum Problem by proposing a multistart procedure that includes a new greedy constructive algorithm and an intensification strategy based on the Variable Neighborhood Search metaheuristic. The proposed algorithm is evaluated on previously studied instances as well as new instances, and the results show its effectiveness.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Manuel Lozano, Eduardo Rodriguez-Tello
Summary: In this paper, a population-based iterated greedy algorithm is proposed to solve the S-labeling problem. In the construction phase, a novel greedy algorithm is used, while the destruction phase involves a strength that gradually decreases and a restart operator based on convergence detection. Experimental results demonstrate that the proposed algorithm achieves better solution quality compared to state-of-the-art optimizers and other competing algorithms for this problem.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Computer Science, Information Systems
Valentina Narvaez-Teran, Gabriela Ochoa, Eduardo Rodriguez-Tello
Summary: Search Trajectory Networks (STNs) are introduced as a tool for analyzing the behavior of metaheuristics in relation to their exploration ability, focusing on a specific combinatorial optimization problem. Two algorithms are analyzed using STNs for the cyclic bandwidth sum minimization problem, and a novel grouping method is proposed for both continuous and combinatorial spaces.
Proceedings Paper
Computer Science, Artificial Intelligence
Arthur Chambon, Frederic Lardeux, Frederic Saubion, Tristan Boureau
PATTERN RECOGNITION APPLICATIONS AND METHODS (ICPRAM 2019)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Jintong Ren, Jin-Kao Hao, Eduardo Rodriguez-Tello
ARTIFICIAL EVOLUTION, EA 2019
(2020)
Article
Computer Science, Information Systems
Jintong Ren, Jin-Kao Hao, Eduardo Rodriguez-Tello
Article
Computer Science, Information Systems
Eduardo Rodriguez-Tello, Valentina Narvaez-Teran, Frederic Lardeux