Evaluation and benchmarking of many-objective optimization (MaOO) methods are
complicated. The rapid development of new optimization algorithms for solving problems …

In machine learning, hyperparameter tuning is strongly useful to improve model
performance. In our research, we concentrate our attention on classifying imbalanced data …

Performance of many-objective evolutionary algorithms (MaOEAs) heavily depends on the
environmental selection strategy which determines the offspring for next generations. One …

Colorectal cancer affects many people and is one of the most frequent causes of cancer-
related deaths in many countries. Professionals of the Reina Sofia University Hospital have …

Nowadays, a great deal of attention is paid to metaheuristic algorithms to reach the
approximate solution in an acceptable computational time. As one of the recent-developed …

In this work we explore the properties which make many real-life global optimization
problems extremely difficult to handle, and some of the common techniques used in …

In recent years, the evolutionary algorithms used in the solution of NP-Hard problems have
become increasingly important. In addition, platforms and application development …

Multi‐objective optimization problems frequently appear in many diverse research areas
and application domains. Metaheuristics, as efficient techniques to solve them, need to be …

Optimisation is highly relevant in many problems in artificial intelligence, machine learning,
engineering and statistics. In these situations, optimisation by means of evolutionary …

Many frameworks and libraries are available for researchers working on optimization.
However, the majority of them require programming knowledge, lack of a friendly user …