EVOLVE - A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation IV [EVOLVE 2013, Leiden The Netherlands, July 10-13, 2013]

Book published by Springer (2013)

Bulding Platform as a Service for High Performance Computing over an Opportunistic Cloud Computing

in Algorithms and Architectures for Parallel Processing (2013)

PlatformasaServiceprovidersdeliverdevelopmentandrun- time environments for applications that are hosted on the Cloud. In this paper, we present a Platform as a Service model constructed over a desktop ... [more ▼]

PlatformasaServiceprovidersdeliverdevelopmentandrun- time environments for applications that are hosted on the Cloud. In this paper, we present a Platform as a Service model constructed over a desktop-based Cloud infrastructure for developing high performance computing applications taking advantage of unused resources opportunis- tically. We highlight the key concepts and features of the platform, as well as its innovation on an opportunistic computing and we present the results of several tests showing the performance of the proposed model. [less ▲]

Achieving super-linear performance in parallel multi-objective evolutionary algorithms by means of cooperative coevolution

in Computers and Operations Research (2013), 40(6), 1552-1563

CA-DAG: Communication-Aware Directed Acyclic Graphs for Modeling Cloud Computing Applications

in Proceedings of the 2013 IEEE Sixth International Conference on Cloud Computing (2013)

The review of the requirements of different cloud applications identified the need to consider communication processes explicitly and equally to the computing tasks. Following this observation, we propose ... [more ▼]

The review of the requirements of different cloud applications identified the need to consider communication processes explicitly and equally to the computing tasks. Following this observation, we propose a new communication-aware model for cloud computing applications, called CA-DAG. This model is based on Directed Acyclic Graphs (DAGs) that in addition to computing vertices include separate vertices to represent communications. Such a representation allows making separate resource allocation decisions, assigning processors to handle computing jobs and network resources for information transmissions, such as application database requests. [less ▲]

Energy-Efficient Data Replication in Cloud Computing Datacenters

in IEEE Globecom 2013 International Workshop on Cloud Computing Systems, Networks, and Applications (CCSNA), Atlanta, GA, USA, 2013 (2013)

Oversized Populations and Cooperative Selection: Dealing with Massive Resources in Parallel Infrastructures

in Nicosia, G.; Pardalos, P. (Eds.) Learning and Intelligent Optimization (2013)

This paper proposes a new selection scheme for Evolutionary Algorithms (EAs) based on altruistic cooperation between individuals. Cooperation takes place every time an individual undergoes selection: the ... [more ▼]

This paper proposes a new selection scheme for Evolutionary Algorithms (EAs) based on altruistic cooperation between individuals. Cooperation takes place every time an individual undergoes selection: the individual decreases its own fitness in order to improve the mating chances of worse individuals. On the one hand, the selection scheme guarantees that the genetic material of fitter individuals passes to subsequent generations as to decrease their fitnesses individuals have to be firstly selected. On the other hand, the scheme restricts the number of times an individual can be selected not to take over the entire population. We conduct an empirical study for a parallel EA version where cooperative selection scheme is shown to outperform binary tournament: both selection schemes yield the same qualities of solutions but cooperative selection always improves the times to solutions. [less ▲]

Expected Running Time of Parallel Evolutionary Algorithms on Unimodal Pseudo-Boolean Functions over Small-World Networks

in Proc. of the IEEE Congress on Evolutionary Computation (CEC'2013) (2013)

This paper proposes a theoretical and experimental analysis of the expected running time for an elitist parallel Evolutionary Algorithm (pEA) based on an island model executed over small-world networks ... [more ▼]

This paper proposes a theoretical and experimental analysis of the expected running time for an elitist parallel Evolutionary Algorithm (pEA) based on an island model executed over small-world networks. Our study assumes the resolution of optimization problems based on unimodal pseudo-boolean funtions. In particular, for such function with d values, we improve the previous asymptotic upper bound for the expected parallel running time from O(d√n) to O(d log n). This study is a first step towards the analysis of influence of more complex network topologies (like random graphs created by P2P networks) on the runtime of pEAs. A concrete implementation of the analysed algorithm have been performed on top of the ParadisEO framework and run on the HPC platform of the University of Luxembourg (UL). Our experiments confirm the expected speed- up demonstrated in this article and prove the benefit that pEA can gain from a small-world network topology. [less ▲]

Simulation and Performance Analysis of Data Intensive and Workload Intensive Cloud Computing Data Centers

in Kachris, Christoforos; Bergman, Keren; Tomkos, Ioannis (Eds.) Optical Interconnects for Future Data Center Networks (2013)

DENS: data center energy-efficient network-aware scheduling

in Cluster Computing (2013), 16(1), 65-75

Evolutionary algorithms based on game theory and cellular automata with coalitions

in Zelinka, I.; Snasel, V.; Abraham, A. (Eds.) Handbook of Optimization (2013)

Cellular genetic algorithms (cGAs) are a kind of genetic algorithms (GAs) with decentralized population in which interactions among individuals are restricted to the closest ones. The use of decentralized ... [more ▼]

Cellular genetic algorithms (cGAs) are a kind of genetic algorithms (GAs) with decentralized population in which interactions among individuals are restricted to the closest ones. The use of decentralized populations in GAs allows to keep the population diversity for longer, usually resulting in a better exploration of the search space and, therefore in a better performance of the algorithm. However, the use of decentralized populations supposes the need of several new parameters that have a major impact on the behavior of the algorithm. In the case of cGAs, these parameters are the population and neighborhood shapes. Hence, in this work we propose a new adaptive technique based in Cellular Automata, Game Theory and Coalitions that allow to manage dynamic neighborhoods. As a result, the new adaptive cGAs (EACO) with coalitions outperform the compared cGA with fixed neighborhood for the selected benchmark of combinatorial optimization problems. [less ▲]