Detailed Programme

Application of Nature-inspired Techniques for Communication Networks and other Parallel and Distributed Systems featuring a fast-track option for Applied Soft Computing journal

This year's edition is technically sponsored by the World Federation on Soft Computing (http://softcomputing.org).


EvoCOMNET solicits contributions dealing with the application of ideas from natural processes and systems to the definition, analysis, and development of novel sequential, parallel, and distributed evolutionary computation approaches to the solution of problems of practical and theoretical interest in all domains related to network systems. Use and tuning of hybrid computational approaches based on evolutionary techniques are also welcome.

Topics of interest include, but are not limited to:


Accepted papers will appear in the proceedings of EvoStar, published in a volume of the Springer Lecture Notes in Computer Science, which will be available at the Conference.Submissions must be original and not published elsewhere. The submissions will be peer reviewed by at least three members of the program committee. The authors of accepted papers will have to improve their paper on the basis of the reviewers comments and will be asked to send a camera ready version of their manuscripts. At least one author of each accepted work has to register for the conference and attend the conference and present the work.The reviewing process will be double-blind, please omit information about the authors in the submitted paper.

Furthermore, a selection of the best accepted papers, suitably revised and extended, will be eligible for fast-track publication in Applied Soft Computing journal by Elsevier (Impact Factor 2.140), thanks to the technical sponsorship offered by the World Federation on Soft Computing.

Submission Details

Submissions must be original and not published elsewhere. They will be peer reviewed by members of the program committee. The reviewing process will be double-blind, so please omit information about the authors in the submitted paper.

Submit your manuscript in Springer LNCS format.

Please provide up to five keywords in your Abstract

Page limit: 12 pages to http://myreview.csregistry.org/evoapps14/.


Submission deadline: 1 November 2013 11 November 2013
Notification: 06 January 2014
Camera ready: 01 February 2014
EvoCOMNET: 23-25 April 2014


Further information on the conference and co-located events can be
found in: http://www.evostar.org

Programme Committee

EvoCOMNET Programme

Wednesday 23 April


Wed 1120-1300  EvoCOMNET 1
Chair: Domenico Maisto

Evolving a Trust Model for Peer-To-Peer Systems Using Genetic Programming  Ugur Eray Tahta, Ahmet Burak Can, Sevil Sen  
Peer-to-peer (P2P) systems have attracted significant interest in recent years. In P2P networks, each peer act as both a server or a client. This characteristic makes peers vulnerable to a wide variety of attacks. Having robust trust management is very critical for such open environments to exclude unreliable peers from the system. This paper investigates the use of genetic programming to asses the trustworthiness of peers without a central authority. A trust management model is proposed in which each peer ranks other peers according to local trust values calculated automatically based on the past interactions and recommendations. The experimental results have shown that the model could successfully identify malicious peers without using a central authority or global trust values and, improve the system performance.

A hybrid primal heuristic for Robust Multiperiod Network Design  (EvoCOMNET best paper candidate)     Fabio D'Andreagiovanni, Jonatan Krolikowski, Jonad Pulaj
We investigate the Robust Multiperiod Network Design Problem, a generalization of the classical Capacitated Network Design Problem which additionally considers multiple design periods and provides solutions protected against traffic uncertainty. Given the intrinsic difficulty of the problem, we propose a hybrid primal heuristic based on the combination of ant colony optimization and an exact large neighborhood search. Computational experiments on a set of realistic instances from the SNDlib show that our heuristic can find solutions of extremely good quality with low optimality gap.

A Trajectory-based Heuristic to Solve a Three-Objective Optimization Problem for Wireless Sensor Network Deployment    (EvoCOMNET best paper candidate) Jose M. Lanza-Gutierrez, Juan A. Gomez-Pulido, Miguel A. Vega-Rodríguez
Nowadays, wireless sensor networks (WSNs) are widely used in more and more fields of application. However, there are some important shortcomings which have not been solved yet in the current literature. This paper focuses on how to add relay nodes to previously established static WSNs with the purpose of optimizing three important factors: energy consumption, average coverage and network reliability. As this is an NP-hard multiobjective optimization problem, we consider two well-known genetic algorithms (NSGA-II and SPEA2) and a multiobjective approach of the variable neighborhood search algorithm (MO-VNS). These metaheuristics are used to solve the problem from a freely available data set, analyzing all the results obtained by considering two multiobjective quality indicators (hypervolume and set coverage). We conclude that MO-VNS provides better performance on average than the standard algorithms NSGA-II and SPEA2.


Wed 1430-1610  EvoCOMNET 2
Chair: Domenico Maisto

Optimizing AEDB Broadcasting Protocol with Parallel Multi-objective Cooperative Coevolutionary NSGAII     Bernabe Dorronsoro, Patricia Ruiz, El-Ghazali Talbi, Pascal Bouvry, Apivadee Piyatumrong
Due to the highly unpredictable topology of ad hoc networks, most of the existing communication protocols rely on different thresholds for adapting their behavior to the environment. Good performance is required under any circumstances. Therefore, finding the optimal configuration for those protocols and algorithms implemented in these networks is a complex task. We propose in this work to automatically fine tune the AEDB broadcasting protocol for MANETs thanks to the use of cooperative coevolutionary multi-objective evolutionary algorithms. AEDB is an advanced adaptive protocol based on the Distance Based broadcasting algorithm that acts differently according to local information to minimize the energy and network use, while maximizing the coverage of the broadcasting process. In this work, it will be fine tuned using multi-objective techniques in terms of the conflicting objectives: coverage, energy and network resources, subject to a broadcast time constraint. Because of the few parameters of AEDB, we defined new versions of the problem in which variables are discretized into bit-strings, making it more suitable for cooperative coevolutionary algorithms. Two versions of the proposed method are evaluated and compared versus the original NSGA-II, providing highly accurate tradeoff configurations in shorter execution times.

Improving Extremal Optimization in Load Balancing by Local Search    Ivanoe de Falco, Eryk Laskowski, Richard Olejnik, Umberto Scafuri, Ernesto Tarantino, Marek Tudruj
The paper concerns the use of Extremal Optimization (EO) technique in dynamic load balancing for optimized execution of distributed programs. EO approach is used to periodically detect the best candidates for task migration leading to balanced execution. To improve the quality of load balancing and decrease time complexity of the algorithms, we have improved EO by a local search of the best computing node to receive migrating tasks. The improved guided EO algorithm assumes a two-step stochastic selection based on two separate fitness functions. The functions are based on specific program models which estimate relations between the programs and the executive hardware. The proposed load balancing algorithm is compared against a standard EO-based algorithm with random placement of migrated tasks and a classic genetic algorithm. The algorithm is assessed by experiments with simulated load balancing of distributed program graphs and analysis of the outcome of the discussed approaches.

Studying the Reporting Cells Planning with the Non-dominated Sorting Genetic Algorithm II     Víctor Berrocal-Plaza, Miguel A. Vega-Rodríguez and Juan M. Sánchez-Pérez
This manuscript addresses a vital task in any Public Land Mobile Network, the mobile location management. This management task is tackled following the Reporting Cells strategy. Basically, the Reporting Cells planning consists in selecting a subset of network cells as Reporting Cells with the aim of controlling the subscribers' movement and minimizing the signaling traffic. In previous works, the Reporting Cells Planning Problem was optimized by using single-objective metaheuristics, in which the two objective functions were linearly combined. This technique simplifies the optimization problem but has got several drawbacks. In this work, with the aim of avoiding such drawbacks, we have adapted a well-known multiobjective metaheuristic: the Non-dominated Sorting Genetic Algorithm II (NSGAII). Furthermore, a multiobjective approach obtains a wide range of solutions (each one related to a specific trade-off between objectives), and hence, it gives the possibility of selecting the solution that best adjusts to the real state of the signaling network. The quality of our proposal is checked by means of an experimental study, where we demonstrate that our version of NSGAII outperforms other algorithms published in the literature.