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; …

“Exploration and exploitation are the two cornerstones of problem solving by search.” For
more than a decade, Eiben and Schippers' advocacy for balancing between these two …

In this paper, an improved multithreshold image segmentation method based on the whale
optimization algorithm (RAV-WOA) is proposed, with the between-class variance (Otsu …

More than a decade after the first extensive overview on parameter control, we revisit the
field and present a survey of the state-of-the-art. We briefly summarize the development of …

This study introduces the Borg multi-objective evolutionary algorithm (MOEA) for many-
objective, multimodal optimization. The Borg MOEA combines-dominance, a measure of …

Genetic algorithms have been used in science and engineering as adaptive algorithms for
solving practical problems and as computational models of natural evolutionary systems …

This is the second edition of our 2003 book. It is primarily a book for lecturers and graduate
and undergraduate students. To this group the book offers a thorough introduction to …

This book provides a handbook of algorithmic recipes from the fields of Metaheuristics,
Biologically Inspired Computation and Computational Intelligence that have been described …

In this paper we describe an efficient approach for multimodal function optimization using
genetic algorithms (GAs). We recommend the use of adaptive probabilities of crossover and …

Genetic algorithms provide an alternative to traditional optimization techniques by using
directed random searches to locate optimal solutions in complex landscapes. We introduce …