Evolutionary Machine Learning: A Comprehensive Survey

Machine Learning (ML) is a process of analyzing data to make predictions or decisions without explicitly being programmed. It involves identifying patterns in large datasets, which can be complex and difficult to understand. In this article, we will explore the basics of ML, its goals, algorithms, and modern frameworks.
Firstly, let’s define ML concepts and goals. ML is about learning from data to perform tasks that are too complex or impossible to solve using a single formula or algorithm. It involves identifying patterns in data, such as spam emails, images, or text, to make predictions or decisions. The main goal of ML is to build accurate models that can generalize well to new data.
Next, we will review various ML algorithms. There are several types of ML algorithms, including kernel-based methods, tree-based methods, and deep learning methods. Kernel-based methods use mathematical functions called kernels to map data into a higher-dimensional space where it is easier to find patterns. Tree-based methods, such as decision trees and random forests, work by recursively partitioning the data into smaller subsets based on their features. Deep learning methods, such as neural networks, are composed of multiple layers of interconnected nodes that learn complex patterns in the data.
Finally, we will discuss modern ML frameworks. These frameworks provide a way to build, train, and deploy ML models more efficiently and effectively. One popular framework is scikit-learn, which provides a wide range of algorithms for classification, regression, clustering, and other tasks. Another important framework is TensorFlow, which allows developers to build and train deep learning models using Python.
Now, let’s talk about the challenges in federated learning (FL). FL involves training ML models on data from multiple sources without compromising the privacy of that data. One of the main challenges in FL is explainability and interpretability. It is difficult to understand how the model arrived at a particular decision, which can be a problem in applications such as healthcare or finance. Another challenge in FL is federated GNNs (graph neural networks), which are used to analyze data that is connected by relationships.
In summary, ML is about building accurate models from complex data without explicitly being programmed. There are various ML algorithms, including kernel-based methods, tree-based methods, and deep learning methods. Modern ML frameworks provide a more efficient and effective way to build, train, and deploy ML models. However, FL faces challenges such as explainability and interpretability, and federated GNNs.