Evolutionary Algorithms in Artificial Intelligent Systems

Dear Colleagues,

Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. Successful early applications of the evolutionary computational approach can be found in the field of numerical optimization, while they have now become pervasive in applications for planning, scheduling, transportation and logistics, vehicle routing, packing problems, etc. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, as components of intelligent systems for supporting tasks and decisions in the fields of machine vision, natural language processing, parameters optimization for neural networks (neuroevolution), and features selection in machine learning systems. Moreover, they are also applied in areas like complex networks dynamics, evolution and trend detection in social networks, emergent behavior in multiagent systems and adaptive evolutionary user interfaces, to mention a few. In these systems, the evolutionary components are integrated in the overall architecture and they provide services, e.g., pattern matching services, to the specific algorithmic solutions.

The aim of this Special Issue is to bring together recent theoretical and applicative research advancements in the area of evolutionary algorithms as components of intelligent systems, with a focus on solutions and methodologies that can be reused to solve subclasses of problems recurring in intelligent applications.

Contributions are welcome on theoretical models and applications to intelligent systems of evolutionary algorithms for, but not limited to, single-objective and multiobjective optimization, numerical continuous nonlinear optimization, combinatorial optimization, graph matching and pattern matching, agents, and automata optimization. Evolutionary paradigms to be considered, non-exhaustively, include continuous and discrete differential evolution, genetic algorithms, memetic and foraging schemes, online evolutionary algorithms, genetic programming, co-evolution mechanisms, artificial immune systems, swarm-based approaches, ant colony optimization, and, more generally, nature and bio-inspired metaheuristics.

The selection criteria will be primarily based on the formal and technical soundness, the experimental support, and the relevance of the contribution and its impact on reusability of the results for solving subgroup of problems recurring in a class of intelligent applications.

Prof. Dr. Alfredo Milani
Prof. Dr. Valentina Franzoni
Dr. Marco Baioletti
Guest Editors

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