Evolutionary Machine Learning: A Survey

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@Article{Telikani:2021:ACMComputSurv,

• author = "Akbar Telikani and Amirhessam Tahmassebi and Wolfgang Banzhaf and Amir H. Gandomi",
• title = "Evolutionary Machine Learning: A Survey",
• journal = "ACM Computing Surveys",
• year = "2021",
• volume = "54",
• number = "8",
• pages = "Article 161",
• month = oct,
• keywords = "genetic algorithms, genetic programming, Machine learning, Artificial intelligence, AI, Evolutionary computation, learning optimization, swarm intelligence",
• URL = "https://ro.uow.edu.au/test2021/3261/",
• URL = "https://dl.acm.org/doi/fullHtml/10.1145/3467477",
• DOI = "doi:10.1145/3467477",
• size = "35 pages",
• abstract = "Evolutionary Computation (EC) approaches are inspired by nature and solve optimization problems in a stochastic manner. They can offer a reliable and effective approach to address complex problems in real-world applications. EC algorithms have recently been used to improve the performance of Machine Learning (ML) models and the quality of their results. Evolutionary approaches can be used in all three parts of ML: preprocessing (e.g., feature selection and resampling), learning (e.g., parameter setting, membership functions, and neural network topology), and postprocessing (e.g., rule optimization, decision tree/support vectors pruning, and ensemble learning). This article investigates the role of EC algorithms in solving different ML challenges. We do not provide a comprehensive review of evolutionary ML approaches here; instead, we discuss how EC algorithms can contribute to ML by addressing conventional challenges of the artificial intelligence and ML communities. We look at the contributions of EC to ML in nine sub-fields: feature selection, resampling, classifiers, neural networks, reinforcement learning, clustering, association rule mining, and ensemble methods. For each category, we discuss evolutionary machine learning in terms of three aspects: problem formulation, search mechanisms, and fitness value computation. We also consider open issues and challenges that should be addressed in future work.",

}

Genetic Programming entries for Akbar Telikani Amirhessam Tahmassebi Wolfgang Banzhaf A H Gandomi

Citations