Evolutionary machine learning: A survey
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 …
problems in a stochastic manner. They can offer a reliable and effective approach to address …
A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects
Clustering is an essential tool in data mining research and applications. It is the subject of
active research in many fields of study, such as computer science, data science, statistics …
active research in many fields of study, such as computer science, data science, statistics …
A hybrid genetic-fuzzy ant colony optimization algorithm for automatic K-means clustering in urban global positioning system
X Ran, N Suyaroj, W Tepsan, J Ma, X Zhou… - … Applications of Artificial …, 2024 - Elsevier
This paper introduces an innovative automatic K-means clustering algorithm, namely HGA-
FACO, which seamlessly integrates the noise algorithm, Genetic Algorithm (GA), Ant Colony …
FACO, which seamlessly integrates the noise algorithm, Genetic Algorithm (GA), Ant Colony …
[LIVRE][B] Introduction: tools and challenges in derivative-free and blackbox optimization
In this introductory chapter, we present a high-level description of optimization, blackbox
optimization, and derivative-free optimization. We introduce some basic optimization …
optimization, and derivative-free optimization. We introduce some basic optimization …
Automatic clustering algorithms: a systematic review and bibliometric analysis of relevant literature
Cluster analysis is an essential tool in data mining. Several clustering algorithms have been
proposed and implemented, most of which are able to find good quality clustering results …
proposed and implemented, most of which are able to find good quality clustering results …
A survey on evolutionary machine learning
Artificial intelligence (AI) emphasises the creation of intelligent machines/systems that
function like humans. AI has been applied to many real-world applications. Machine …
function like humans. AI has been applied to many real-world applications. Machine …
Efficient agglomerative hierarchical clustering
Hierarchical clustering is of great importance in data analytics especially because of the
exponential growth of real-world data. Often these data are unlabelled and there is little prior …
exponential growth of real-world data. Often these data are unlabelled and there is little prior …
A survey on nature inspired metaheuristic algorithms for partitional clustering
The partitional clustering concept started with K-means algorithm which was published in
1957. Since then many classical partitional clustering algorithms have been reported based …
1957. Since then many classical partitional clustering algorithms have been reported based …
A survey on feature selection approaches for clustering
The massive growth of data in recent years has led challenges in data mining and machine
learning tasks. One of the major challenges is the selection of relevant features from the …
learning tasks. One of the major challenges is the selection of relevant features from the …
Advances in meta-heuristic optimization algorithms in big data text clustering
This paper presents a comprehensive survey of the meta-heuristic optimization algorithms
on the text clustering applications and highlights its main procedures. These Artificial …
on the text clustering applications and highlights its main procedures. These Artificial …