References of "Jimenez Laredo, Juan Luis 40021373"
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See detailVisualization and Classification of Protein Secondary Structures using Self-Organizing Maps
Grevisse, Christian UL; Muller, Ian William UL; Jimenez Laredo, Juan Luis UL et al

in IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014) (2014, December)

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See detailExploiting the Hard-wired Vulnerabilities of Newscast via Connectivity-splitting Attack
Muszynski, Jakub UL; Varrette, Sébastien UL; Jimenez Laredo, Juan Luis UL et al

in Proc. of the IEEE Intl. Conf. on Network and System Security (NSS 2014) (2014, October)

Newscast is a model for information dissemination and mem- bership management in large-scale, agent-based distributed systems. It deploys a simple, peer-to-peer data exchange protocol. The Newscast pro ... [more ▼]

Newscast is a model for information dissemination and mem- bership management in large-scale, agent-based distributed systems. It deploys a simple, peer-to-peer data exchange protocol. The Newscast pro- tocol forms an overlay network and keeps it connected by means of an epidemic algorithm, thus featuring a complex, spatially structured, and dynamically changing environment. It has recently become very popu- lar due to its inherent resilience to node volatility as it exhibits strong self-healing properties. In this paper, we analyze the robustness of the Newscast model when executed in a distributed environment subjected to malicious acts. More precisely, we evaluate the resilience of Newscast against cheating faults and demonstrate that even a few naive cheaters are able to defeat the protocol by breaking the network connectivity. Concrete experiments are performed using a framework that implements both the protocol and the cheating model considered in this work. [less ▲]

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See detailDesigning Robust Volunteer-based Evolutionary Algorithms
Jimenez Laredo, Juan Luis UL; Bouvry, Pascal UL; Lombraña Gonzalez, Daniel et al

in Genetic Programming and Evolvable Machines (2014)

This paper tackles the design of scalable and fault-tolerant evolutionary algorithms computed on volunteer platforms. These platforms aggregate computational resources from contributors all around the ... [more ▼]

This paper tackles the design of scalable and fault-tolerant evolutionary algorithms computed on volunteer platforms. These platforms aggregate computational resources from contributors all around the world. Given that resources may join the system only for a limited period of time, the challenge of a volunteer-based evolutionary algorithm is to take advantage of a large amount of computational power that in turn is volatile. The paper analyzes first the speed of convergence of massively parallel evolutionary algorithms. Then, it provides some guidance about how to design efficient policies to overcome the algorithmic loss of quality when the system undergoes high rates of transient failures, i.e. computers fail only for a limited period of time and then become available again. In order to provide empirical evidence, experiments were conducted for two well-known problems which require large population sizes to be solved, the first based on a genetic algorithm and the second on genetic programming. Results show that, in general, evolutionary algorithms undergo a graceful degradation under the stress of losing computing nodes. Additionally, new available nodes can also contribute to improving the search process. Despite losing up to 90% of the initial computing resources, volunteer-based evolutionary algorithms can find the same solutions in a failure-prone as in a failure-free run. [less ▲]

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See detailCooperative Selection: Improving Tournament Selection via Altruism
Jimenez Laredo, Juan Luis UL; Nielsen, Sune Steinbjorn UL; Danoy, Grégoire UL et al

in The 14th European Conference on Evolutionary Computation in Combinatorial Optimisation (2014)

This paper analyzes the dynamics of a new selection scheme based on altruistic cooperation between individuals. The scheme, which we refer to as cooperative selection, extends from tournament selection ... [more ▼]

This paper analyzes the dynamics of a new selection scheme based on altruistic cooperation between individuals. The scheme, which we refer to as cooperative selection, extends from tournament selection and imposes a stringent restriction on the mating chances of an individual during its lifespan: winning a tournament entails a depreciation of its fitness value. We show that altruism minimizes the loss of genetic diversity while increasing the selection frequency of the fittest individuals. An additional contribution of this paper is the formulation of a new combinatorial problem for maximizing the similarity of proteins based on their secondary structure. We conduct experiments on this problem in order to validate cooperative selection. The new selection scheme outperforms tournament selection for any setting of the parameters and is the best trade-off, maximizing genetic diversity and minimizing computational efforts. [less ▲]

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See detailThe sandpile scheduler: How self-organized criticality may lead to dynamic load-balancing
Jimenez Laredo, Juan Luis UL; Bouvry, Pascal UL; Guinand, Frederic et al

in Cluster Computing (2014)

This paper studies a self-organized criticality model called sandpile for dynamically load-balancing tasks arriving in the form of Bag-of-Tasks in large-scale decentralized system. The sandpile is ... [more ▼]

This paper studies a self-organized criticality model called sandpile for dynamically load-balancing tasks arriving in the form of Bag-of-Tasks in large-scale decentralized system. The sandpile is designed as a decentralized agent system characterizing a cellular automaton, which works in a critical state at the edge of chaos. Depending on the state of the cellular automaton, different responses may occur when a new task is assigned to a resource: it may change nothing or generate avalanches that reconfigure the state of the system. The abundance of such avalanches is in power-law relation with their sizes, a scale-invariant behavior that emerges without requiring tuning or control parameters. That means that large—catastrophic—avalanches are very rare but small ones occur very often. Such emergent pattern can be efficiently adapted for non-clairvoyant scheduling, where tasks are load balanced in computing resources trying to maximize the performance but without assuming any knowledge on the tasks features. The algorithm design is experimentally validated showing that the sandpile is able to find near-optimal schedules by reacting differently to different conditions of workloads and architectures. [less ▲]

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See detailDynamic and Partially Connected Ring Topologies for Evolutionary Algorithms with Structured Populations
Fernandes, Carlos; Jimenez Laredo, Juan Luis UL; Merelo, Juan Julian et al

in The European Conference on the Applications of Evolutionary Computation (2014)

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 ... [more ▼]

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, multimodal 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. [less ▲]

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See detailOversized Populations and Cooperative Selection: Dealing with Massive Resources in Parallel Infrastructures
Jimenez Laredo, Juan Luis UL; Dorronsoro, Bernabe; Fernandes, Carlos et al

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 ▲]

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See detailPool vs. island based evolutionary algorithms: an initial exploration
Merelo, Juan Julian; Mora, Antonio M.; Fernandes, Carlos et al

in 2012 Seventh International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (2012)

This paper explores the scalability and performance of pool and island based evolutionary algorithms, both of them using as a mean of interaction an object store; we call this family of algorithms SofEA ... [more ▼]

This paper explores the scalability and performance of pool and island based evolutionary algorithms, both of them using as a mean of interaction an object store; we call this family of algorithms SofEA. This object store allows the different clients to interact asynchronously; the point of the creation of this framework is to build a system for spontaneous and voluntary distributed evolutionary computation. The fact that each client is autonomous leads to a complex behavior that will be examined in the work, so that the design can be validated, rules of thumb can be extracted, and the limits of scalability can be found. In this paper we advance the design of an asynchronous, fault-tolerant and scalable distributed evolutionary algorithm based on the object store CouchDB. We test experimentally the different options and show the trade-offs that pool and island-based solutions offer. [less ▲]

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See detailValidating a Peer-to-Peer Evolutionary Algorithm
Jimenez Laredo, Juan Luis UL; Bouvry, Pascal UL; Mostaghim, Sanaz et al

in European Conference on the Applications of Evolutionary Computation (2012)

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See detailDesigning a Self-organized Approach for Scheduling Bag-of-Tasks
Jimenez Laredo, Juan Luis UL; Dorronsoro, Bernabé UL; Pecero, Johnatan UL et al

in Designing a Self-organized Approach for Scheduling Bag-of-Tasks (2012)

This paper proposes a decentralized and self-organized agent system for dynamically load-balancing tasks arriving in the form of Bags-of-Tasks (BoTs) in large-scale decentralized systems. The approach is ... [more ▼]

This paper proposes a decentralized and self-organized agent system for dynamically load-balancing tasks arriving in the form of Bags-of-Tasks (BoTs) in large-scale decentralized systems. The approach is inspired by the emergent behavior of the sandpile model; a cellular automaton behaving at the edge of chaos. Depending on the state of the cellular automaton, rather different responses may occur when a new task is assigned to a resource. It may change nothing or generate avalanches that reconfigure the state of the system. The proportion between the abundance of avalanches and their sizes shows a power-law relation, a scale-invariant behavior that does not need to be tuned. That means that large –catastrophic– avalanches are very rare but small ones occur very often. Such a smart and emergent behavior fits well with the idea of non-clairvoyant scheduling, where tasks are load balanced into computing resources trying to maximize the performance but without assuming any knowledge on the tasks features. In order to study the viability of the approach, we have conducted an empirical experimentation which shows that the sandpile is able to find near-optimal schedules by reacting differently to different conditions of workloads and architectures. [less ▲]

Detailed reference viewed: 29 (2 UL)