Much of recent machine learning has focused on deep learning, in which neural network
weights are trained through variants of stochastic gradient descent. An alternative approach …

“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 recent years, the research community has witnessed an explosion of literature dealing
with the mimicking of behavioral patterns and social phenomena observed in nature towards …

This paper proposes a multi-objective version of the recently proposed Ant Lion Optimizer
(ALO) called Multi-Objective Ant Lion Optimizer (MOALO). A repository is first employed to …

While evolutionary computation and evolutionary robotics take inspiration from nature, they
have long focused mainly on problems of performance optimization. Yet evolution in nature …

Multimodal optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions
in a single simulation run has practical relevance to problem solving across many fields …

The optimization of functions to find the best solution according to one or several objectives
has a central role in many engineering and research fields. Recently, a new family of …

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

In evolutionary computation, the fitness function normally measures progress toward an
objective in the search space, effectively acting as an objective function. Through deception …

Evolutionary algorithms (EAs) are becoming increasingly attractive for researchers from
various disciplines, such as operations research, computer science, industrial engineering …