Suchergebnisse - Learning to learn evolutionary algorithm

Learning to Learn Evolutionary Algorithm: A Learnable Differential Evolution von Liu, Xin, Sun, Jianyong, Zhang, Qingfu, Wang, Zhenkun, Xu, Zongben

“… With the advancement of artificial intelligence, especially deep learning and reinforcement learning, it is becoming appealing to rethink the design and development of evolutionary algorithm (EA …”
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Evolutionary multitasking fuzzy cognitive map learning von Shen, Fang, Liu, Jing, Wu, Kai

“… that can share with each other. For the purpose of making use of similar structure patterns among different tasks, we introduce the evolutionary multitasking framework to learn different FCMs at one time by taking each FCM learning problem as a task …”
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Learning of a Decision-Maker's Preference Zone With an Evolutionary Approach von Aggarwal, Manish

“… A new evolutionary-learning algorithm is proposed to learn a decision maker (DM)'s best solution on a conflicting multiobjective space …”
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Recruitment-imitation mechanism for evolutionary reinforcement learning von Lü, Shuai, Han, Shuai, Zhou, Wenbo, Zhang, Junwei

“… Reinforcement learning, evolutionary algorithms and imitation learning are three principal methods to deal with continuous control tasks …”
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Adaptive evolutionary programming based on reinforcement learning von Zhang, Huaxiang, Lu, Jing

“… A novel algorithm named RLEP (Evolutionary Programming based on Reinforcement Learning) is proposed. In this algorithm, each individual learns its optimal mutation operator based on the immediate and delayed performance of mutation operators …”
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Learning protein fitness models from evolutionary and assay-labeled data von Hsu, Chloe, Nisonoff, Hunter, Fannjiang, Clara, Listgarten, Jennifer

“… Machine learning-based models of protein fitness typically learn from either unlabeled, evolutionarily related sequences or variant sequences with experimentally measured labels …”
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A Multiobjective Sparse Feature Learning Model for Deep Neural Networks von Gong, Maoguo, Liu, Jia, Li, Hao, Cai, Qing, Su, Linzhi

“… Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations …”
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SMILE: systems metabolomics using interpretable learning and evolution von Sha, Chengyuan, Cuperlovic-Culf, Miroslava, Hu, Ting

“… for understanding and diagnosing disease development and progression. Machine learning methods have seen increasing adoptions in metabolomics thanks to their powerful prediction abilities. However, the “black-box …”
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Harnessing a Novel Machine-Learning-Assisted Evolutionary Algorithm to Co-optimize Three Characteristics of an Electrospun Oil Sorbent von Wang, Boqian, Cai, Jiacheng, Liu, Chuangui, Yang, Jian, Ding, Xianting

“… Herein, we describe a novel systematic optimization strategy, namely, machine-learning-assisted differential evolution, which combines machine learning and the evolutionary algorithm together …”
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A survey of evolutionary algorithms for supervised ensemble learning von Cagnini, Henry E. L., Das Dôres, Silvia C. N., Freitas, Alex A., Barros, Rodrigo C.

“… This paper presents a comprehensive review of evolutionary algorithms that learn an ensemble of predictive models for supervised machine learning …”
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Learning to learn with an evolutionary strategy applied to variational quantum algorithms von Lucas, Friedrich, Maziero, Jonas

“… In this article, we introduce a novel optimization approach named ``Learning to Learn with an Evolutionary Strategy'' (LLES). LLES unifies …”
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A survey on evolutionary machine learning von Al-Sahaf, Harith, Bi, Ying, Chen, Qi, Lensen, Andrew, Mei, Yi, Sun, Yanan, Tran, Binh, Xue, Bing, Zhang, Mengjie

“… Evolutionary computation is an umbrella of population-based intelligent/learning algorithms inspired by nature, where New Zealand has a good international reputation …”
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An efficient evolutionary algorithm to optimize the Choquet integral von Islam, Muhammad Aminul, Anderson, Derek T., Petry, Fred, Elmore, Paul

“… Often, a fusion solution has parameters and the goal is to learn them from data. Herein, we propose efficient evolutionary algorithm (EA …”
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Learning of Fuzzy Cognitive Maps With Varying Densities Using A Multiobjective Evolutionary Algorithm von Chi, Yaxiong, Liu, Jing

“… To this end, we first model the FCM learning problem as a multiobjective optimization problem, and then propose a multiobjective evolutionary algorithm, labeled as MOEA-FCM, to learn FCM models …”
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Evolutionary Direction Learning With Multivariate Gaussian Probabilistic Model for Multiobjective Optimization von Wang, Xianpeng, Zhang, Jingchuan, Tang, Lixin, Liu, Yaxue

“… ) to learn knowledge and guide evolutionary search has become a popular research topic. However, existing knowledge learning (KL …”
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DEPP: Deep Learning Enables Extending Species Trees using Single Genes von Jiang, Yueyu, Balaban, Metin, Zhu, Qiyun, Mirarab, Siavash

“… ), an algorithm that learns to extend species trees using single genes without prespecified models …”
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Playing to learn: case-injected genetic algorithms for learning to play computer games von Louis, S.J., Miles, C.

“… We use case-injected genetic algorithms (CIGARs) to learn to competently play computer strategy games …”
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Multilabel Classification via Co-Evolutionary Multilabel Hypernetwork von Sun, Kai Wei, Lee, Chong Ho, Wang, Jin

“… ) where label correlations are explicitly represented. We then propose a co-evolutionary learning algorithm to learn …”
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Model-based evolutionary algorithms: a short survey von Cheng, Ran, He, Cheng, Jin, Yaochu, Yao, Xin

“… To equip the EAs with learning abilities , recently, various model-based evolutionary algorithms (MBEAs) have been proposed …”
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Multi-Objective Neural Evolutionary Algorithm for Combinatorial Optimization Problems von Shao, Yinan, Lin, Jerry Chun-Wei, Srivastava, Gautam, Guo, Dongdong, Zhang, Hongchun, Yi, Hu, Jolfaei, Alireza

“… ) models with neural evolutionary algorithms. This type of method is inspired by biological evolution and uses different genetic operations to evolve neural networks …”
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