It has been a very challenging task to develop efficient and robust techniques to solve real-
world engineering optimization problems due to the unknown function properties, complex …

Background Genetic programming (GP) is an evolutionary computing methodology capable
of identifying complex, non-linear patterns in large data sets. Despite the potential …

The success of the evolutionary computational methods in scanning at problem's solution
space and the ability to produce robust solutions, are important advantages for fuzzy …

This paper introduces evolutionary algorithms with its applications in multi-objective
optimization. Here elitist and non-elitist multiobjective evolutionary algorithms are discussed …

Accurate modelling of time-to-event data is of particular importance for both exploratory and
predictive analysis in cancer, and can have a direct impact on clinical care. This study …

Purpose–The purpose of this paper is to address a multiobjective FAP (frequency
assignment problem) formulation. More precisely, two conflicting objectives–the interference …

Kidney transplantation is the most effective long-term solution for renal disease.
Unfortunately, there are a multitude of factors that determine how compatible a donor kidney …

AN EXPLANATION OF EVOLUTIONARY ALGORITHMS AND THEIR USES Page 1
AIJREAS VOLUME 8, ISSUE 2 (2023, FEB) (ISSN-2455-6300)ONLINE Anveshana’s …

Survival analysis is an important part of medical statistics or for the study of failures in
mechanical systems. The latter is called reliability analysis in engineering. This paper …

The integration of optimization methodologies with computational simulations plays a
profound role in the product design. Such integration, however, faces multiple challenges …