AI-Driven Engineering: Redefining Possibilities and Problem-Solving
Created by W.Langdon from
gp-bibliography.bib Revision:1.8355
- @InProceedings{Gandomi:2024:CINTI,
-
author = "Amirhossein Gandomi",
-
title = "{AI-Driven} Engineering: Redefining Possibilities and
Problem-Solving",
-
booktitle = "2024 IEEE 24th International Symposium on
Computational Intelligence and Informatics (CINTI)",
-
year = "2024",
-
pages = "13--14",
-
month = nov,
-
keywords = "genetic algorithms, genetic programming, Optimisation
methods, Biological systems, Problem-solving,
Artificial intelligence, Particle swarm optimisation,
Nonlinear systems, Informatics, Computational
intelligence",
-
ISSN = "2471-9269",
-
DOI = "
doi:10.1109/CINTI63048.2024.10830819",
-
abstract = "Artificial Intelligence (AI) has been extensively
applied over the past two decades and continues to be a
prominent area of research, particularly in addressing
complex, real-world challenges. Evolutionary and Swarm
Intelligence (ESI) techniques represent a unique subset
of AI, deriving their efficiency from mimicking the
best features of natural and biological systems that
have evolved over millions of years. This presentation
focuses on the principles of ESI and their broader
applications across various engineering domains. We
will explore automated learning approaches, such as
genetic programming, and highlight advancements in
evolutionary learning for complex problem-solving.
Additionally, the presentation will evaluate the
general impact of AI in engineering, discussing key
applications of ESI in optimising complex, nonlinear
systems. We will also demonstrate the advantages of ESI
over traditional optimisation methods, showcasing
results from large-scale and multi-objective problems.
Finally, adaptable heuristics that enhance ESI
performance will be introduced, illustrating their
potential to improve optimisation outcomes.",
-
notes = "Also known as \cite{10830819}",
- }
Genetic Programming entries for
A H Gandomi
Citations