Results 11 to 20 of about 5,817,365 (365)
Hybrid algorithm of Bayesian optimization and evolutionary algorithm in crystal structure prediction
Science and Technology of Advanced Materials: Methods, 2022 We propose a highly efficient searching algorithm in crystal structure prediction. The searching algorithm is a hybrid of the evolutionary algorithm and Bayesian optimization. The evolutionary algorithm is widely used in crystal structure prediction, and
Tomoki Yamashita +4 more
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An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering †
Algorithms, 2021 Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems.
Daphne Teck Ching Lai, Yuji Sato
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, 2018 Evolutionary algorithms (EAs) are population-based metaheuristics, originally inspired by aspects of natural evolution. Modern varieties incorporate a broad mixture of search mechanisms, and tend to blend inspiration from nature with pragmatic engineering concerns; however, all EAs essentially operate by maintaining a population of potential solutions ...
Michael A. Lones, David Corne
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Multifactorial Evolutionary Algorithm With Online Transfer Parameter Estimation: MFEA-II
IEEE Transactions on Evolutionary Computation, 2020 Humans rarely tackle every problem from scratch. Given this observation, the motivation for this paper is to improve optimization performance through adaptive knowledge transfer across related problems.
K. Bali +3 more
semanticscholar +1 more source
Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs) [PDF]
, 2020 Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models.
Cheng, Ran +4 more
core +3 more sources
Hetero-Dimensional Multitask Neuroevolution for Chaotic Time Series Prediction
IEEE Access, 2020 Chaotic time series prediction has important research and application value, and neural network-based prediction methods have problems such as low accuracy and difficulty in determining the number of nodes in the hidden layer.
Daoqing Zhang, Mingyan Jiang
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International Journal of Adaptive Control and Signal Processing, EarlyView., 2023 Summary In this contribution, we propose a detailed study of interpolation‐based data‐driven methods that are of relevance in the model reduction and also in the systems and control communities. The data are given by samples of the transfer function of the underlying (unknown) model, that is, we analyze frequency‐response data.
Quirin Aumann, Ion Victor Gosea
wiley +1 more source
A Fireworks Algorithm Based on Transfer Spark for Evolutionary Multitasking
Frontiers in Neurorobotics, 2020 In recent years, lots of multifactorial optimization evolutionary algorithms have been developed to optimize multiple tasks simultaneously, which improves the overall efficiency using implicit genetic complementarity between different tasks.
Zhiwei Xu +6 more
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Hybridization of Evolutionary Algorithms [PDF]
, 2011 Evolutionary algorithms are good general problem solver but suffer from a lack of domain specific knowledge. However, the problem specific knowledge can be added to evolutionary algorithms by hybridizing. Interestingly, all the elements of the evolutionary algorithms can be hybridized.
Fister, Iztok +2 more
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Hybrid Multi-Evolutionary Algorithm to Solve Optimization Problems
Applied Artificial Intelligence, 2020 The article presents a Hybrid Multi-Evolutionary Algorithm designed to solve optimization problems. The Genetic Algorithm and Evolutionary Strategy work together to improve the efficiency of optimization and increase resistance to getting stuck to sub ...
Krzysztof Pytel
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