Results 11 to 20 of about 5,527,579 (363)
Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities [PDF]
Swarm and Evolutionary Computation, 2023 Evolutionary algorithms (EA), a class of stochastic search methods based on the principles of natural evolution, have received widespread acclaim for their exceptional performance in various real-world optimization problems.
Yanjie Song +11 more
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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
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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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An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems
IEEE Transactions on Evolutionary Computation, 2020 In the last two decades, a variety of different types of multiobjective optimization problems (MOPs) have been extensively investigated in the evolutionary computation community.
Ye Tian +3 more
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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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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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Evolutionary algorithm design based on evolutionary efficiency factor
Journal of Hebei University of Science and Technology, 2018 With the development of electronic chip technology, circuit systems become gradually high integrated and intelligent. Under the interface of complex electromagnetic field environment, the requirements for the stability and reliability of information ...
Huicong WU, Jie YU
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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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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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