Abstract Machine learning (ML) is a new-age thriving technology, which facilitates
computers to read and interpret from the previously present data automatically. It makes use …

A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …

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 …

Because of successful implementations and high intensity, metaheuristic research has been
extensively reported in literature, which covers algorithms, applications, comparisons, and …

Genetic Algorithms are heuristic search approaches that are applicable to a wide range of
optimization problems. This flexibility makes them attractive for many optimization problems …

Deep reinforcement learning (RL) methods generally engage in exploratory behavior
through noise injection in the action space. An alternative is to add noise directly to the …

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data
from experiments, field measurements, and large-scale simulations at multiple …

In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

We propose a metalearning approach for learning gradient-based reinforcement learning
(RL) algorithms. The idea is to evolve a differentiable loss function, such that an agent …

The knowledge discovery process is as old as Homo sapiens. Until some time ago this
process was solely based on the 'natural personal'computer provided by Mother Nature …