Call for Papers

Thematic Issue on 'Agent based computational memetics'

Paper Submission: July 2009
Notification of acceptance/rejection: January 2010
Publication: April 2010

Memetic Algorithms (MAs) are one of the recently growing research areas in Evolutionary Algorithms (EAs). Memetic algorithms are a general name for a broad class of population-based heuristics that is capable of local refinements. Recent studies have revealed that MAs are successful on a wide variety of real world problems. Particularly, they converge to high quality solutions more efficiently compared to their conventional systematic counterparts.

A multi-agent system (MAS) is composed of multiple interacting agents, possibly equipped with intelligent capabilities. By agents here, we typically mean software agents. Recently, multi-agent systems are increasingly used for solving problems which are difficult or impossible for an individual agent. They are also used as a programming and software development paradigm. In a problem solving multi-agent system, agents usually have some of the basic properties and characteristics of usual MASs, such as autonomy, local view, social ability (communications), learning and adaptive ability.

A population in MAs can simply be thought as a collection of agents. In addition, since MAs are hybrid techniques, as they incorporate both population-based and local search metaheuristics possibly combined with tree-search techniques, MASs are indeed a powerful framework for modelling, designing and implementing them. By integrating the agent concept in MAs, we can enhance the performance of MAs as evident in the literature. The agents can bring many interesting features in MAs which are beyond the scope of traditional evolutionary process and learning.

 The aim of this special session is to reflect the most recent advances in the field, and increase the awareness of the computing community at large on this effective technology. In particular, we endeavor to demonstrate the current state-of-the-art in the theory and practice of Agent based MAs. Topics of interest include (but are not limited to):

•  Novel frameworks of Agent based MAs (AMAs),
• Analytical and/or theoretical studies that enhance our understanding of AMAs,
• Design of multi-agent architecture within AMAs,
•  Design of agent communication and learning strategy,
• Analysing the affect of agent type, architecture, cooperation, communication and learning on the overall performance of AMAs
• Convergence and complexity analysis of AMAs,
• AMAs for global, constrained, dynamic and large scale optimization,
• Multi-objective AMAs,
• Multi-method local search in AMAs,
•  Hybrid search strategies in AMAs, and
• Real-world applications of AMAs.

 Manuscripts should conform to the standard format of the Memetic Computing journal as indicated in the Information for Authors. All submissions will be peer reviewed subject to the standards of the journal. Manuscripts based on previously published conference papers must be extended substantially. Electronic submissions must be in PDF format and should be prepared according to the journal guidelines. The submissions should be performed through the journal automatic system. Please specify on the first page of the manuscript that the submission is intended for this thematic issue.

 All enquiries on this special issue should be sent to Dr. Ruhul Sarker at r.sarker@adfa.edu.au. Prospective authors are also invited to send an email to Dr. Sarker indicating their interest in submitting a paper and the specific topics addressed.

Guest Editors:

• Dr. Ruhul Sarker, UNSW@ADFA, Canberra, Australia (http://seal.tst.adfa.edu.au/~ruhul/)
• A/Prof. Michela Milano, DEIS - Università di Bologna, Italy (http://www-lia.deis.unibo.it/Staff/MichelaMilano/)
• Dr. Andrea Roli, DEIS - Università di Bologna, Italy (http://www.lia.deis.unibo.it/~aro/)

 Contacts

Emails: r.sarker@adfa.edu.au; michela.milano@unibo.it; andrea.roli@unibo.it