ERLNEIL-MDP: Evolutionary reinforcement learning with novelty-driven exploration for medical data processing

IF 8.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Swarm and Evolutionary Computation Pub Date : 2024-10-30 DOI:10.1016/j.swevo.2024.101769
Jianhui Lv , Byung-Gyu Kim , Adam Slowik , B.D. Parameshachari , Saru Kumari , Chien-Ming Chen , Keqin Li
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Abstract

The rapid growth of medical data presents opportunities and challenges for healthcare professionals and researchers. To effectively process and analyze this complex and heterogeneous data, we propose evolutionary reinforcement learning with novelty-driven exploration and imitation learning for medical data processing (ERLNEIL-MDP) algorithm, including a novelty computation mechanism, an adaptive novelty-fitness selection strategy, an imitation-guided experience fusion mechanism, and an adaptive stability preservation module. The novelty computation mechanism quantifies the novelty of each policy based on its dissimilarity to the population and historical data, promoting exploration and diversity. The adaptive novelty-fitness selection strategy balances exploration and exploitation by considering policies' novelty and fitness during selection. The imitation-guided experience fusion mechanism incorporates expert knowledge and demonstrations into the learning process, accelerating the discovery of effective solutions. The adaptive stability preservation module ensures the stability and reliability of the learning process by dynamically adjusting the algorithm's hyperparameters and preserving elite policies across generations. These components work together to enhance the exploration, diversity, and stability of the learning process. The significance of this work lies in its potential to revolutionize medical data analysis, leading to more accurate diagnoses and personalized treatments. Experiments on MIMIC-III and n2c2 datasets demonstrate ERLNEIL-MDP's superior performance, achieving F1 scores of 0.933 and 0.928, respectively, representing 6.0 % and 6.7 % improvements over state-of-the-art methods. The algorithm exhibits strong convergence, high population diversity, and robustness to noise and missing data.
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ERLNEIL-MDP:针对医疗数据处理的新奇探索进化强化学习
医疗数据的快速增长为医疗专业人员和研究人员带来了机遇和挑战。为了有效地处理和分析这些复杂的异构数据,我们提出了用于医疗数据处理的新颖性驱动探索和模仿学习的进化强化学习算法(ERLNEIL-MDP),包括新颖性计算机制、自适应新颖性匹配度选择策略、模仿引导的经验融合机制和自适应稳定性保持模块。新颖性计算机制根据每个策略与群体和历史数据的不相似性来量化其新颖性,从而促进探索和多样性。自适应新颖性-适配性选择策略通过在选择过程中考虑策略的新颖性和适配性来平衡探索和利用。模仿引导的经验融合机制将专家知识和示范融入学习过程,加速了有效解决方案的发现。自适应稳定性保存模块通过动态调整算法的超参数和跨代保存精英策略,确保学习过程的稳定性和可靠性。这些组件共同作用,增强了学习过程的探索性、多样性和稳定性。这项工作的意义在于它有可能彻底改变医学数据分析,从而带来更准确的诊断和个性化治疗。在 MIMIC-III 和 n2c2 数据集上的实验证明了 ERLNEIL-MDP 的卓越性能,其 F1 分数分别达到 0.933 和 0.928,比最先进的方法分别提高了 6.0% 和 6.7%。该算法具有收敛性强、群体多样性高以及对噪声和缺失数据的鲁棒性等特点。
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来源期刊
Swarm and Evolutionary Computation
Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
16.00
自引率
12.00%
发文量
169
期刊介绍: Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.
期刊最新文献
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