Abstract Machine learning (ML) is a new-age thriving technology, which facilitates
computers to read and interpret from the previously present data automatically. It makes use …

Since its inception in 1995, Differential Evolution (DE) has emerged as one of the most
frequently used algorithms for solving complex optimization problems. Its flexibility and …

A generative recurrent neural network is quickly trained in an unsupervised manner to
model popular reinforcement learning environments through compressed spatio-temporal …

This paper presents a comprehensive study on clustering: exiting methods and
developments made at various times. Clustering is defined as an unsupervised learning …

Flooding produces debris and waste including liquids, dead animal bodies and hazardous
materials such as hospital waste. Debris causes serious threats to people's health and can …

Summary The use of Artificial Intelligence (AI) has increased since the middle of the 20th
century as seen in its application in a wide range of engineering and science problems. The …

Metaheuristics are widely recognized as efficient approaches for many hard optimization
problems. This paper provides a survey of some of the main metaheuristics. It outlines the …

Test functions are important to validate and compare the performance of optimisation
algorithms. There have been many test or benchmark functions reported in the literature; …

Evolution has provided a source of inspiration for algorithm designers since the birth of
computers. The resulting field, evolutionary computation, has been successful in solving …

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 …