This paper reviews a majority of the nature-inspired algorithms, including heuristic and meta-
heuristic bio-inspired and non-bio-inspired algorithms, focusing on their source of inspiration …

Humans perform Bayesian inference in a wide variety of tasks, which can help people make
selection decisions effectively and therefore enhances learning efficiency and accuracy …

Epilepsy, a neurological condition colloquially known as a seizure disorder, causes
involuntary muscle contractions and cognitive changes through sudden, uncontrolled …

Several metaheuristic algorithms have been introduced to solve different optimization
problems. Such algorithms are inspired by a wide range of natural phenomena or behaviors …

Feature selection, widely used in data preprocessing, is a challenging problem as it involves
hard combinatorial optimization. So far some meta-heuristic algorithms have shown …

This paper proposes a novel social cognitive learning-based metaheuristic called kids
Learning Optimizer (KLO), inspired by the early social learning behavior of kids organized …

This paper presents a novel meta-heuristic optimization algorithm, named Adaptive
Simplified Human Learning Optimization (ASHLO), which is inspired by the human learning …

Neural networks have demonstrated their usefulness for solving complex regression
problems in circumstances where alternative methods do not provide satisfactory results …

This work proposes a novel reptile search algorithm (RSA) to solve optimization problems
called reinforcement reptile search algorithm (RLRSA). The basic RSA performs exploitation …

Abstract Human Learning Optimization (HLO) is a simple yet powerful meta-heuristic
developed based on a simplified human learning model. Many cognitive activities of …