Machine Learning and Evolutionary Algorithms: Theory and Applications

Dear Colleagues, 

The Special Issue on "Machine Learning and Evolutionary Algorithms: Theory and Applications" aims to highlight the latest advancements in the field of machine learning and its interaction with evolutionary algorithms.

Machine learning is a branch of artificial intelligence where computers learn to perform tasks without explicit programming. It involves algorithms that analyze data, identify patterns, and make predictions or decisions based on their experience. Machine learning algorithms involve training a model by using a dataset, tuning its parameters to minimize errors, and evaluating its performance. Machine learning algorithms have been successfully employed in different real-world applications. Their effectiveness relies on quality data, proper feature engineering, and avoiding overfitting.

Evolutionary computation is a line of research into artificial intelligence inspired by the principles of biological evolution. It encompasses a family of algorithms that mimic evolution to solve complex problems. The process begins with an initial population of potential solutions represented as individuals. Genetic operators like mutation and crossover generate new candidate solutions in successive generations. Over time, the population evolves, converging towards better solutions. Evolutionary algorithms are commonly employed in optimization, search, and decision-making problems where traditional mathematical programming methods are impractical or inefficient. Their versatility and ability to handle complex, multi-dimensional, and non-linear problems make them valuable in diverse real-world applications.

This Special Issue features a diverse collection of innovative studies that explore the increased synergy between machine learning and evolutionary algorithms for solving complex real-world problems. The research papers delve into various domains, including but not limited to:

• Optimization of machine learning and evolutionary algorithms;
• Complexity analysis of both machine learning and evolutionary algorithms;
• Evolutionary optimization under uncertainty;
• Evolutionary supervised/unsupervised/semi-supervised learning;
• Hyper-parameter optimization;
• New evolutionary operators;
• Large-scale optimization;
• Many-objective optimization;
• Constrained single- and multi-objective optimization;
• Data-driven optimization;
• Surrogate-assisted evolutionary optimization;
• Machine learning for data processing;
• Hybrid algorithms and (meta)heuristics;
• Feature selection and pattern recognition;
• Computer vision;
• Image processing;
• Multi-task optimization;
• Machine learning and evolutionary computation for solving real-world applications.

Overall, developing new evolutionary algorithms assisted by machine learning strategies, using evolutionary algorithms to optimize machine learning models, exploring the combination of evolutionary algorithms with deep learning or reinforcement learning methods, and expanding the possibilities for innovative applications in artificial intelligence will all be of interest to this Special Issue.

Dr. Saúl Zapotecas-Martínez
Guest Editor

Manuscript Submission Information

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• optimization of machine learning
• evolutionary algorithms
• new evolutionary operators
• large-scale optimization
• many-objective optimization
• computer vision
• image processing