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Detailed Programme

Parallel Architectures and Distributed Infrastructures

There is growing interest in running evolutionary computation on Parallel and Distributed Computing Infrastructures. A number of technologies are already available. These include Grid and Cloud Computing, Internet Computing (e.g. seti@home, boinc), General Purpose Computation on Graphics Processing Units (GPGPU), multi-core and many-core architectures and supercomputers. Although they are routinely used for running computing intensive applications, considerable skill is required to get the best from them. The experimenter has to consider scheduling, porting of applications, communication topologies, new parallel models and architectures, cache and memory management optimization, preemptive multitasking and simultaneous multi threading and even energy consumption. Also, the experimenter may need to change their evolutionary algorithm to fully exploit these new tools. At EvoPar scientists and engineers will gather to share and exchange their experiences, discuss challenges, and report state-of-the-art and in-progress research on all aspects of the application of evolutionary algorithms for improving parallel architectures and distributed computing Infrastructures. EvoPAR will assist the two-way flow of ideas between the parallel computing community and the EC community.

Areas of Interest and Contributions

High quality paper submissions which demonstrate novelty in terms of methodology, application or both, were strongly encouraged. Applications of interest included (but were 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.

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/.

Best papers will be invited for a Special Issue to be edited with
Journal of Grid Computing, Springer. (2012 IMPACT FACTOR: 1.602)


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


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

Programme Committee

EvoPAR Programme

Thurs 1430-1610 EvoPAR 1
Chair : Francisco Fernández

Hybrid MPI/OpenMP Parallel Evolutionary Algorithms for Vehicle Routing Problems    Raul Banos, Julio Ortega, Consolacion Gil
The traditional fields of improvement in parallelism have been orientated to experimentation on high-budget equipment, such as clusters of computers or shared memory machines thanks to their high-performance and scalability. In recent years, the generalization of multi-core microprocessors in almost all the computing platforms makes it possible to take advantage of parallel processing even for the desktop computer user. This paper analyzes how to improve the performance of population-based meta-heuristics using MPI, OpenMP, and hybrid MPI/OpenMP implementations in a workstation having a multi-core processor. The results obtained when solving large scale instances of the Capacitated Vehicle Routing Problem with hard Time Windows (VRPTW) show that, in all cases, the parallel implementations produce better quality solutions for a given amount of runtime than the sequential algorithm, and also solutions of similar quality in less runtime.

Dynamic and Partially Connected Ring Topologies for Evolutionary Algorithms with Structured Populations     Carlos Fernandes, Juan Laredo, Juan Merelo, Carlos Cotta, Agostinho Rosa
This paper investigates dynamic and partially connected ring topologies for cellular Evolutionary Algorithms (cEA). We hypothesize that these structures maintain population diversity at a higher level and reduce the risk of premature convergence to local optima on deceptive and NP-hard fitness landscapes. A general framework for modelling partially connected topologies is proposed and three different schemes are tested. The results show that the structures improve the rate of convergence to global optima when compared to cEAs with standard topologies (ring, rectangular and square) on quasi-deceptive, deceptive and NP-hard problems. Optimal population size tests demonstrate that the proposed topologies require smaller populations when compared to traditional cEAs.

Systolic Genetic Search for Software Engineering: The Test Suite Minimization Case     Martín Pedemonte, Francisco Luna, Enrique Alba
The Test Suite Minimization Problem (TSMP) is a NP-hard real-world problem that arises in the field of software engineering. It lies in selecting the minimal set of test cases from a large test suite, ensuring that the test cases selected cover a given set of elements of a computer program under test. In this paper, we propose a Systolic Genetic Search (SGS) algorithm for solving the TSMP. We use the global concept of SGS to derive a particular algorithm to explicitly exploit the high degree of parallelism available in modern GPU architectures. The experimental evaluation on seven real-world programs shows that SGS is highly effective for the TSMP, as it obtains the optimal solution in almost every single run for all the tested software. It also outperforms two competitive Genetic Algorithms. The GPU-based implementation of SGS has achieved a high performance, obtaining runtime reductions of up to 40X compared to its sequential implementation, and solving all the instances considered in less than nine seconds.

Thurs 1630-1810  EvoPAR 2 
Chair: Ignacio Hidalgo

Optimization of Application Placement towards a Greener Cloud Infrastructure    Tania Lorido-Botran, Jose Antonio Pascual, Jose Miguel-Alonso, Jose Antonio Lozano
Cloud infrastructures are designed to simultaneously service many, diverse applications that consist of collections of Virtual Machines (VMs). The policy used to map applications onto physical servers (placement policy) has important effects in terms of application performance and resource efficiency. This paper proposes enhancing placement policies with network-aware optimizations trying to simultaneously improve application performance, resource efficiency and, as a consequence, power efficiency. The per-application placement decision is formulated as a bi-objective optimization problem (minimizing communication cost and minimizing the number of physical servers assigned to the application) whose solution is searched using an evolutionary algorithm with problem-specific crossover and mutation operators. Experiments carried out with a simulator demonstrate how a low-cost optimization technique results in improved placements that achieve all the target objectives.

GridVis: Visualisation of Island-Based Parallel Genetic Algorithms     Evelyne Lutton, Hugo Gilbert, Waldo Cancino, Benjamin Bach, Pierre Parrend, Pierre Collet
Island Model parallel genetic algorithms rely on various migration models and their associated parameter settings. A fine understanding of how the islands interact and exchange informations is an important issue for the design of efficient algorithms. This article presents GridVis, an interactive tool for visualising the exchange of individuals and the propagation of fitness values between islands. We performed several experiments on a grid and on a cluster to evaluate GridVis' ability to visualise the activity of each machine and the communication flow between machines. Experiments have been made on the optimisation of a Weierstrass function using the EASEA language, with two schemes: a scheme based on uniform islands and another based on specialised islands (Exploitation, Exploration and Storage Islands).