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Call for Special Session Proposals for ICSI'2011

ICSI'2011 technical program will include special sessions. Their aim is to provide a complementary flavor to the regular sessions and should include hot topics of interest to the swarm intelligence community that may also go beyond disciplines traditionally represented at ICSI.

Prospective organizers of special sessions should submit proposals indicating:

* Title of the session
* Tationale of the need for the special session at ICSI. The rationale should stress the novelty of the topic and/or its multidisciplinary flavor, and must explain how it is different from the subjects covered by the regular sessions
* Short biography of the organizers
* List of 5 - 6 contributed papers (including titles, authors, contact information of the corresponding author).

Proposals are due on or before 1 December 2010 and should be sent via e-mail (in either pdf or plain ascii text form) to the Special Sessions chairs or Secretariat at ICSI2011@ic-si.org.

Proposals will be evaluated based on the timeliness of the topic, the qualifications of the organizers and the authors of the papers proposed in the session. In its decision, the committee will try to realize a balance of the topics across the technical areas represented in swarm intelligence.

Notification of acceptance will be sent to the organizers no later than 15 December 2010. Authors of papers included in approved special sessions should submit their manuscript on or before 1 January 2011. Manuscripts should conform to the formatting and electronic submission guidelines of regular ICSI papers (Springer's LNCS format).

When they submit papers, there is a choice to indicate that their papers are special session papers which will also undergo peer review. It is the responsibility of the organizers to ensure that their Special Session meets the ICSI quality standards. If, at the end of the review process, less than four (4) papers are accepted, the session will be canceled and the accepted papers will be moved to regular sessions.
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1. Special Session on "Data Fusion and Swarm Intelligence"

Organizers: Dr. Benlian Xu and Prof. Ba-Ngu Vo

2. Special Session on "Artificial Immune Systems and Applications"

Organizer: Prof. Hongwei Mo

3. Special Session on "Fish School Search – Foundations and Application"

Organizers: Prof. Fernando Buarque and Prof. Carmelo J. A. Bastos-Filho

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1. Special Session on "Data Fusion and Swarm Intelligence"

Organizers:

Dr. Benlian Xu
School of Electric and Automatic Engineering
Changshu Institute of Technology
Changshu, 215500, China
Email: xu_benlian@yahoo.com.cn

Prof. Ba-Ngu Vo
School of Electrical Electronic & Computer Engineering
The University of Western Australia, Australia
Email: ba-ngu.vo@uwa.edu.au

Data fusion refers to a broad range of problems which require the combination of diverse types of data provided by a variety of sources to achieve improved accuracies and more specific inferences than could be achieved by the use of a single source alone. Since its inception, this area has been growing in conceptual tools as well as applications eg. defence, geoscience, robotics and intelligent vehicles, medicine, and industrial engineering. The natural mathematical tool for multi-sensor multi-target data fusion is random finite set, which allows the rapid advancement of data fusion from a loose collection of related techniques to an emerging true engineering discipline with standardized terminology, collections of robust mathematical techniques, and established system design principles.
Swarm intelligence, as a scientific discipline, is born from biological insights about the incredible abilities of social insects to solve their everyday-life problems. Their colonies display fascinating behaviors that combine efficiency with both flexibility and robust. The study of natural swarm intelligence leads directly to novel algorithms that have a wide variety of applications. For example, it provided new ways to manage and control traffic and communications networks, accomplished and effective ways to generate realistic simulations of swarms, etc. Recently, swarm intelligence based algorithms have been successfully applied into data fusion field. At a more fundamental level the mathematical tools for data fusion can be used to analyse behavior of swarm intelligence algorithms. Swarm intelligence involves the spatio-temporal evolution of many dynamical components such as ants in a colony. The mathematical tool for data fusion--random finite set theory--is the study of stochastic spatial point patterns and their evolution in time.
This special session aims to bring together new theories and methodologies inspired by swarm intelligence and data fusion. The emphasis of the session is more inclined to these research areas such as theory, modeling, simulation and applications .
Topics of interest:
The topics explored in this special session include, but are not limited to:

• Stochastic geometry, random finite set as mathematical tool for data fusion and swarm intelligence.
• Novel random set algorithms for registration, detection, localization, tracking and data association;
• Swarm intelligence in sequential inference, data mining, graph analysis and machine learning;
• New intelligence methods for image and video processing;
• Applications of swarm intelligence concept in aided fusion, sensor networks, persistent surveillance, security, robotics, manufacturing, economics, financial, environmental monitoring.

2. Special Session on "Artificial Immune Systems and Applications"

Organizer: Prof. Hongwei Mo
Harbin Engineering University, Harbin, China
Email: mhonwei@sina.com

Artificial immune systems (AIS) is a diverse and maturing area of research that bridges the disciplines of immunology and engineering. The scope of AIS ranges from modelling and simulation of the immune system through to immune-inspired algorithms and engineering solutions. In recent years, algorithms inspired by theoretical immunology have been applied to a wide variety of domains, including computer security, fault tolerance, data-mining and optimisation. Increasingly, theoretical insight into aspects of artificial and real immune systems has been sought through mathematical and computational modelling and analysis.

The main themes of the special issue include Artificial immune systems and applications

Immune algorithms:
Clone Selection Algorithm
Artificial Immune Network
Negative Selection Algorithm
Immune mechanisms inspired algorithms
Hybrid algorithms of immunity and other nature inspired computing
Comparisons between AIS and other naturally-inspired paradigms.
Applications(not limited):
Optimization
Control
Data mining and machine learning
Network Intelligence
Computer security
Fault tolerance
Evolvable Hardware
Robotics
Medical(Immune system simulation and modeling, disease prediction and treatment)
Immunity Inspired(based) Hardware systems
Immunity Inspired(based) software systems
Theories of AIS:
Self-nonself models, self-assertional models.
Network models (e.g., of B-cells).
Clonal selection and hypermutation.
Danger theory models.
Abstractions of other immunological processes.
Immune algorithm theory
Theories related to the application fields of AIS

3. Special Session on "Fish School Search – Foundations and Application"

Organizers:

Prof. Fernando Buarque de Lima Neto   
University of Pernambuco, Recife, Brazil
Email: fbln@ecomp.poli.br

Prof. Carmelo J. A. Bastos Filho
University of Pernambuco, Recife, Brazil
Email: carmelofilho@poli.br

Real world problems are quite often complex in nature and most of the time, hard to be computed. The main reason for that is generally associated with the large dimensionality of the search space and sometimes the high cardinality of solutions. Therefore, searching for parameters or candidate solutions– depending on the class of problem dealt with –is frequently costly and sometimes unfeasible if one uses traditional methods (i.e. single-track computation).
         On the other hand, nature inspired algorithms– especially population based such as swarms, flocks and herds –are able to deal fairly well with the abovementioned difficulties. In 2008, Bastos Filho and Lima Neto proposed a new metaheuristic in the fast growing family of swarm intelligence techniques, namely, Fish School Search (FSS); please refer to Proceedings of IEEE-SMC2008 (Singapore), pp. 2646-2651, "A Novel Search Algorithm based on Fish School Behavior". This new technique greatly benefit from the collective emerging behavior of fish that when in schools, increase their success by (i) mutual protection and (ii) synergistic achievement of collective tasks. In FSS, the school “swims”(searches) for “food”(candidate solutions) in the  “aquarium”(search space). The weight of each fish acts as a factual-memory of its individual success; emergently, promising areas regarding quality of solutions can be inferred from regions where bigger ensembles of fish are located. 
         Similarly to PSO or GA, the search guidance in FSS is driven by the merit of individual members of the population. The main difference though is that fishes contain only their innate memory (i.e. their weights). In comparison to PSO, this information can obviate the need to keep a log of best positions visited as well as any other global variables. In comparison to GA, the factual hyper-dimensional coordinates of each fish directly substitutes the need of a chromosome. As for social reasoning, the barycenter of the whole school can automatically guide expansion and contraction of the school, evocating exploration and exploitation when necessary.
         Broadly speaking, FSS is composed of operators that can be grouped in the following categories feeding, swimming and breeding. Together these operators afford computational features such as: (i) high-dimensional search abilities, (ii) on-the-‘swim’ selection between exploration and exploitation, and (iii) self-adaptable guidance towards sought solutions (that can be multi-modal).
         The authors think that FSS should keep principles such as the following:
                   (i) Simple computations in all individuals;
                   (ii) Creative yet simple means of storing distributed memory of past computations;
                   (iii) Local computations (preferably within small radiuses);
                   (iv) Low (or none) communications between neighboring individuals;
                   (v) Minimum centralized control (preferably only the barycenter information); and
                   (vi) Simple diversity keeping mechanisms among individuals.
         Topics of interest for this special session, involving FSS, are:
                   (i) development and refinement of FSS operators;
                   (ii) application of FSS in various domains;
                                (iii) stability and scalability studies of FSS; and,
                   (iv) performance comparative studies of FSS.
                Interested practitioner and investigators could quickly grasp the rational of FSS in the book chapter: BASTOS-FILHO, Carmelo J. A.; LIMA NETO, Fernando Buarque de; LINS, Anthony J. C. C.;                   NASCIMENTO, Antônio I. S.; LIMA, Marília P. "Fish School Search: an overview". In: CHIONG, Raymond  (Ed.). Nature-Inspired Algorithms for Optimisation. Series: Studies in Computational Intelligence, Vol. 193.. pp. 261-277. Berlin: Springer-Verlag, 2009. {ISBN: 978-3-642-00266-3}.

For those who are not familiar with FSS, we put together a website with all the necessary information for you to try FSS against other Swarm Intelligence techniques. In the new internet site there is a download package with the complete Vanilla version of FSS (website is at http://www.fbln.pro.br/fss/ )

To be added......

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