References of "Merelo, Juan Julian"
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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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