Review
Genetics & Heredity
Elisabetta Manduchi, Joseph D. Romano, Jason H. Moore
Summary: Parametric statistical methods have traditionally been used in genetic analysis of complex traits, but machine learning methods are better suited for detecting and modeling patterns in complex genetic architectures. The goal of AutoML is to eliminate guesswork by automatically identifying the right algorithms and hyperparameters for optimization. This review discusses the promises and challenges of AutoML in genetic analysis and emphasizes the potential for developing novel AutoML methods and software in the field of genetics and genomics.
Article
Computer Science, Artificial Intelligence
Chnoor M. Rahman, Tarik A. Rashid, Aram Mahmood Ahmed, Seyedali Mirjalili
Summary: In this work, a new multi-objective optimization algorithm called multi-objective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of moving graduated students from high school to college, and it produces a set of non-dominated solutions with better accuracy and diversity. Experimental results show that the algorithm outperforms other algorithms in terms of solution quality and processing time.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Automation & Control Systems
Hsien-Chung Wu
Summary: This paper proposes a new approach to solving multiobjective optimization problems using genetic algorithms and cooperative game concepts. By considering the objective functions as players and obtaining suitable weights from the core values of a cooperative game, a set of Core-Pareto optimal solutions can be obtained.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Ting Zhou, Zhongbo Hu, Qinghua Su, Wentao Xiong
Summary: This paper proposes a novel multimodal multiobjective differential evolution algorithm (MMOcDE) that can locate multiple high quality equivalent Pareto optimal sets and obtain a uniformly distributed Pareto front simultaneously.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Mathematics, Interdisciplinary Applications
Jing Liu, Qixing Chen, Xiaoying Tian
Summary: This paper proposes an illustration art design model based on operator and clustering optimization genetic algorithm, as well as a multiobjective optimization genetic algorithm with complex constraints based on group classification. By improving the genetic algorithm, the search efficiency is increased and the complexity of the algorithm is optimized.
Article
Computer Science, Artificial Intelligence
Marlon P. Lima, Ricardo H. C. Takahashi, Marcos A. M. Vieira, Eduardo G. Carrano
Summary: This manuscript presents a novel approach for planning WLANs based on hybrid optimization algorithms. The proposed method optimizes network load balance and signal-to-noise ratio, while considering coverage, customer, and equipment demand constraints. The new representation/decoding scheme, based on subpermutations, reduces the search space dimension and improves computational efficiency. Test results using real data show that the proposed method provides solutions that reduce costs and improve WLAN throughput.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Construction & Building Technology
Jianjian Zhang, Lin Ji, Hong Xian Li
Summary: This study simulated the daylighting, ventilation, and energy consumption of buildings in tropical areas using grasshopper, and found the optimal energy-saving performance parameters. Through optimization design, the refrigeration energy consumption was effectively reduced, achieving the goal of green energy saving.
ADVANCES IN CIVIL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Tomohiro Harada, Enrique Alba, Gabriel Luque
Summary: This study proposes a novel approach to evaluate and analyze the behavior of multi-population parallel genetic algorithms (PGAs) running on a cluster of multi-core processors. By establishing a mathematical model, the numerical and computational behavior of the algorithms are deeply studied, and conclusions regarding performance, running time, and numerical effort are drawn.
APPLIED SOFT COMPUTING
(2022)
Article
Engineering, Civil
Andranik S. Akopov, Levon A. Beklaryan, Manoj Thakur
Summary: Simulation-based approaches have been developed for optimizing complex multiagent systems, such as the proposed multiagent fuzzy transportation system (FTS) using fuzzy logic for maneuvering. A new parallel real-coded genetic algorithm based on fuzzy clustering is proposed to improve maneuverability in the FTS model. This approach aims to approximate optimal solutions by considering specific requirements and interactions in the MAS environment.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Review
Management
Charles Audet, Jean Bigeon, Dominique Cartier, Sebastien Le Digabel, Ludovic Salomon
Summary: In recent years, there has been significant growth in the development of new algorithms for multiobjective optimization, with a large number of performance indicators introduced to measure the quality of Pareto front approximations. A total of 63 performance indicators are reviewed in this work, categorized into four groups based on their properties: cardinality, convergence, distribution, and spread. Applications of these indicators are also presented.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Operations Research & Management Science
Christian von Lucken, Carlos A. Brizuela, Benjamin Baran
Summary: This work presents a new multipopulation framework for the multiobjective evolutionary algorithm based on decomposition. Clustering methods are used to reinforce mating restrictions by splitting the population into multiple subpopulations for independent evolution. The results show the viability of the clustering-based multipopulation approach in enhancing the performance of evolutionary methods for many-objective problems.
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
(2022)
Article
Chemistry, Multidisciplinary
Grzegorz Kusztelak, Adam Lipowski, Jacek Kucharski
Summary: This paper presents a modified memetic genetic algorithm that introduces a cyclic symmetrization operator to improve exploitation by creating a more spherical distribution around the current leader. The algorithm is described, demonstrated, and theoretically analyzed. Its effectiveness is examined using a recognized benchmark of continuous functions test set on a multidimensional cube.
APPLIED SCIENCES-BASEL
(2022)
Article
Environmental Sciences
Mohammad Ehteram, Saad Sh. Sammen, Fatemeh Panahi, Lariyah Mohd Sidek
Summary: The study predicted CO2 emissions in Iran's agricultural sectors using SVM models and MOAs, with the IMM model being identified as the best predictor for CO2 emissions.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Jinyuan Zhang, Hisao Ishibuchi, Linjun He
Summary: This paper proposes a classification-assisted environmental selection (CAES) strategy to reduce the number of function evaluations in MOEAs. The solutions are divided into promising and unpromising classes, and a classifier is used to classify the offspring solutions. Only the promising offspring are evaluated, leading to a reduction in the number of function evaluations. Experimental results show that the proposed CAES strategy effectively reduces the number of function evaluations without severely degrading the search ability of the original MOEAs.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Multidisciplinary Sciences
Jun Wu, Zelin Zhang, Rui Tong, Yuan Zhou, Zhengfa Hu, Kaituo Liu
Summary: In this article, the effectiveness of the 2D structure of timeseries in clustering tasks is investigated. The RP imaging series are found to be valid in recognizing abnormal fluctuations of financial timeseries, with silhouette values of clusters over 0.6 to 1. The 2D models have the lowest instability value of 0 compared to segment methods, verifying that the SIFT features of RP images take advantage of the volatility of financial series for clustering tasks.
