Results 11 to 20 of about 786,097 (340)
Evolutionary Multi-Objective Membrane Algorithm
IEEE Access, 2020 Recent advances in evolutionary algorithms based on membrane computing have shown that the mechanism of membrane computing is an effective way to solve optimization problems. In this work, we propose a new evolutionary multi-objective algorithm that uses
Chuang Liu +3 more
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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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, 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
openaire +2 more sources
Treatment planning with a 2.5 MV photon beam for radiation therapy
Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022 Abstract Purpose The shallow depth of maximum dose and higher dose fall‐off gradient of a 2.5 MV beam along the central axis that is available for imaging on linear accelerators is investigated for treatment of shallow tumors and sparing the organs at risk (OARs) beyond it.
Navid Khaledi +5 more
wiley +1 more source
Evolving Evolutionary Algorithms using Linear Genetic Programming [PDF]
Evolutionary Computation, MIT Press, Vol. 13, Issue 3, pp.
387-410, 2005, 2021 A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem, and the Quadratic Assignment ...
arxiv +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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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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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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International Journal of Distributed Sensor Networks, 2022 The Fruit Fly Optimization Algorithm is a swarm intelligence algorithm with strong versatility and high computational efficiency. However, when faced with complex multi-peak problems, Fruit Fly Optimization Algorithm tends to converge prematurely.
Ru-Yu Wang +3 more
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