The EvoSpace Model for Pool-Based Evolutionary Algorithms

García-Valdéz, Mario; Trujillo, Leonardo; Merelo, J. J.; Vega, Francisco Fernández de; Olague, Gustavo

doi:10.1007/s10723-014-9319-2

Cited by 33 publications

(13 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other solutions (as they are implemented now) do not offer this opportunity, because they host the population on a central server and therefore would require a separate server for each concurrent experiment, see for instance the EvoSpace framework described in [12].…”

Section: Discussionmentioning

confidence: 99%

“…When dealing with network packets, it is always safer to have a strongly typed language able to detect missing/misspelled fields so that users do not received malformed packets. For these reasons, the decision was made to use TypeScript 12 , an open-source superset of JavaScript. As described in [23], TypeScript is useful for avoiding mistakes while writing JavaScript code.…”

Section: System Architecturementioning

confidence: 99%

See 1 more Smart Citation

The Seamless Peer and Cloud Evolution Framework

Leclerc

Auerbach

Iacca

et al. 2016

Proceedings of the Genetic and Evolutionary Computation Conference 2016

View full text Add to dashboard Cite

Evolutionary algorithms are increasingly being applied to problems that are too computationally expensive to run on a single personal computer due to costly fitness function evaluations and/or large numbers of fitness evaluations. Here, we introduce the Seamless Peer And Cloud Evolution (SPACE) framework, which leverages bleeding edge web technologies to allow the computational resources necessary for running large scale evolutionary experiments to be made available to amateur and professional researchers alike, in a scalable and cost-effective manner, directly from their web browsers. The SPACE framework accomplishes this by distributing fitness evaluations across a heterogeneous pool of cloud compute nodes and peer computers. As a proof of concept, this framework has been attached to the RoboGen TM open-source platform for the co-evolution of robot bodies and brains, but importantly the framework has been built in a modular fashion such that it can be easily coupled with other evolutionary computation systems.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: System Architecturementioning

confidence: 99%

The Seamless Peer and Cloud Evolution Framework

Leclerc

Auerbach

Iacca

et al. 2016

Proceedings of the Genetic and Evolutionary Computation Conference 2016

View full text Add to dashboard Cite

show abstract

“…The first work to involve technologies on the Platform-as-a-Service (PaaS) level is from Merelo Guervós et al, who devised a model for Pool-based EAs [6]. The work put EAs in a real-world environment, highlighting some important aspects, such as easiness in developing and low-cost deployment.…”

Section: Related Workmentioning

confidence: 99%

Speed up genetic algorithms in the cloud using software containers

Salza

Ferrucci

2019

Future Generation Computer Systems

View full text Add to dashboard Cite

“…Another approach is to change the basic GP paradigm, which is mostly used as a synchronous and local algorithm. For instance, it is possible to use the evolutionary model proposed in [37] to distribute the evolutionary process across various client machines (either locally or over the web) in a plug-and-play manner, thus increasing the available computational resources by exploiting cloud-based technologies. However, this option is left for possible future work.…”

Section: M3gp Parallelizationmentioning

confidence: 99%

A Scalable Genetic Programming Approach to Integrate miRNA‐Target Predictions: Comparing Different Parallel Implementations of M3GP

et al. 2018

Self Cite

View full text Add to dashboard Cite

There are many molecular biology approaches to the analysis of microRNA (miRNA) and target interactions, but the experiments are complex and expensive. For this reason, in silico computational approaches able to model these molecular interactions are highly desirable. Although several computational methods have been developed for predicting the interactions between miRNA and target genes, there are substantial differences in the results achieved since most algorithms provide a large number of false positives. Accordingly, machine learning approaches are widely used to integrate predictions obtained from different tools. In this work, we adopt a method called multidimensional multiclass GP with multidimensional populations (M3GP), which relies on a genetic programming approach, to integrate and classify results from different miRNA-target prediction tools. The results are compared with those obtained with other classifiers, showing competitive accuracy. Since we aim to provide genome-wide predictions with M3GP and, considering the high number of miRNA-target interactions to test (also in different species), a parallel implementation of this algorithm is recommended. In this paper, we discuss the theoretical aspects of this algorithm and propose three different parallel implementations. We show that M3GP is highly parallelizable, it can be used to achieve genome-wide predictions, and its adoption provides great advantages when handling big datasets.

show abstract

The EvoSpace Model for Pool-Based Evolutionary Algorithms

Cited by 33 publications

References 34 publications

The Seamless Peer and Cloud Evolution Framework

The Seamless Peer and Cloud Evolution Framework

Speed up genetic algorithms in the cloud using software containers

A Scalable Genetic Programming Approach to Integrate miRNA‐Target Predictions: Comparing Different Parallel Implementations of M3GP

Contact Info

Product

Resources

About