Autonomous robots deployed in the real world will need control policies ...
Efforts to improve the adversarial robustness of convolutional neural
ne...
Experienced users often have useful knowledge and intuition in solving
r...
The ongoing advancements in network architecture design have led to
rema...
Electric machine design optimization is a computationally expensive
mult...
To solve complex real-world problems, heuristics and concept-based appro...
Significant effort has been made to solve computationally expensive
opti...
Significant effort has been made to solve computationally expensive
opti...
Dominance move (DoM) is a binary quality indicator that can be used in
m...
"Innovization" is a task of learning common relationships among some or ...
Black-box artificial intelligence (AI) induction methods such as deep
re...
Classification of datasets into two or more distinct classes is an impor...
For supervised classification problems involving design, control, other
...
In the context of optimization, visualization techniques can be useful f...
In this paper, we propose an efficient NAS algorithm for generating
task...
Software systems nowadays are complex and difficult to maintain due to
c...
Neural architecture search (NAS) has emerged as a promising avenue for
a...
Most optimization-based community detection approaches formulate the pro...
Convolutional neural networks have witnessed remarkable improvements in
...
In this paper, we propose a method to solve a bi-objective variant of th...
Search-Based Software Engineering (SBSE) is a promising paradigm that
ex...
Python has become the programming language of choice for research and
in...
Convolutional neural networks (CNNs) are the backbones of deep learning
...
The ultimate goal of multi-objective optimisation is to help a decision ...
This paper introduces NSGA-Net, an evolutionary approach for neural
arch...
A new kind of six degree-of-freedom teaching manipulator without actuato...
This paper proposes a push and pull search (PPS) framework for solving
c...
Bilevel optimization is defined as a mathematical program, where an
opti...
Most existing studies on evolutionary multi-objective optimization focus...
Multi-objective evolutionary algorithms (MOEAs) have achieved great prog...
Evolutionary Algorithms (EAs) are being routinely applied for a variety ...
Bilevel optimization problems are a class of challenging optimization
pr...