Intelligent systems represent an essential part of contemporary social and industrial applications. They facilitate smart operations, management, and control in their target domains and help to utilize the vast amounts of data continuously collected within smart, cognitive environments. Intelligent systems are often based on a computational implementation of successful nature-inspired problem-solving strategies. Neural, evolutionary, and swarm-based methods are only a few members of the broad family of unconventional approaches whose wide applicability was enabled by the advances in information and communication technologies. However, the design and tuning of such intelligent systems, necessary for their successful use in the complex conditions of real-world applications, is a non-trivial process that is often tackled by nature-inspired methods as well. At the same time, many hybrid (multi-paradigm) approaches such as neuroevolution, deep learning, and genetic fuzzy systems, are employed to obtain accurate and efficient intelligent systems.
This special session is concerned with novel nature-inspired approaches to design, evolution, and optimization of all types of intelligent systems. It will especially welcome submissions dealing with both theoretical and practical issues of real-world systems and their data-driven adaptation and optimization.
Scope and Topics:
The proposed special session aims to bring together latest research on nature-inspired design, evolution, and optimization of all sorts of intelligent systems. It will facilitate knowledge exchange, technical discussions, and networking on topics of interest that include, but are not limited to:
- Nature-inspired methods for the design of intelligent systems.
- Evolution, adaptation, transfer learning, and optimization of intelligent systems.
- Mining intelligent behaviour from large data collections.
- Data- and simulation-driven intelligent systems.
- Evolutionary and swarm-based methods for fine-tuning of system parameters.
- Hybrid and multi-paradigm intelligent systems.
- Real-world applications of nature-inspired intelligent systems.
- Learning with a small number of examples and unbalanced data.
- Intelligent systems in adversarial modeling.
- Advances in the theory of evolutionary computational methods.
- Evolutionary architectures of Neural Networks.
- Evolutionary methods deep learning algorithms.
- Combination of evolutionary and non-evolutionary methods.
An online CFP is available here.
Paper submission: February, 1, 2018 (EXTENDED)
Notification to authors: March, 15, 2018
Camera ready submission: May, 1, 2018
Author registration: May, 1, 2018