IEEE ECAAD 2021

CEC Special Session on Evolutionary Computation for Automated Algorithm Design (ECAAD 2021)

Aims, Scope and List of Topics

Evolutionary algorithms play an imperative role in solving real world complex problems across industry and business sectors. These algorithms have contributed to many facets of problems including data mining, machine learning, transportation, health systems, computer vision, computer security, robotics, software engineering scheduling, and amongst others. Implementation of these algorithms and techniques require a number of design decisions to be made, e.g. what architecture to use, what parameter values to use, and derivation of problem specific operators. It may also be necessary to employ a hybrid system combining techniques to solve a problem which introduces additional decisions such as which techniques to use and how to combine these techniques. This makes the development of evolutionary computation techniques time consuming, requiring extensive expertise, and many man hours. Consequently, there have been a number of initiatives to automate these processes. There has been a fair amount of research into parameter tuning and control. The field of auto-machine learning aims to automate the design of machine learning algorithms so as to produce off-the-shelf machine learning techniques. Attempts to automate neural network architecture design has led to the field of neuroevolution. Research in this area has also been directed at inducing fuzzy functions, rule-based systems and multi-agent architectures. Hyper-heuristics, which were initially aimed at providing generalized solutions to combinatorial optimization problems, are proving to be effective in the automated development of techniques such as metaheuristics. Evolutionary algorithms such as genetic programming and genetic algorithms have chiefly been used in these initiatives. Recent areas that need investigation in the context of evolutionary algorithms for automated design include transfer learning an explainable artificial intelligence. The aim of this special session is to examine recent developments in the field and future directions including the challenges and how these can be overcome. The topics covered include, but are not limited to, the use of evolutionary algorithms for the following:


Organizers

Nelishia Pillay
University of Pretoria
nelishia.pillay@up.ac.za

Rong Qu
University of Nottingham
Rong.Qu@nottingham.ac.uk

Submission Deadline

31 January 2021

Submission Link

Submit your paper via the IEEE CEC 2021 submission website by selecting SS-42. Evolutionary Computation for Automated Algorithm Design