A Peer-to-Peer Approach to Genetic Programming

Laredo, Juan Luís Jiménez; González, Daniel Lombraña; Vega, Francisco Fernández de; Arenas, M. G.; Guervós, Juan Julián Merelo

doi:10.1007/978-3-642-20407-4_10

Cited by 5 publications

References 11 publications

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Validating a Peer-to-Peer Evolutionary Algorithm

Laredo

Bouvry

Mostaghim

et al. 2012

Applications of Evolutionary Computation

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Abstract. This paper proposes a simple experiment for validating a Peer-to-Peer Evolutionary Algorithm in a real computing infrastructure in order to verify that results meet those obtained by simulations. The validation method consists of conducting a well-characterized experiment in a large computer cluster of up to a number of processors equal to the population estimated by the simulator. We argue that the validation stage is usually missing in the design of large-scale distributed metaheuristics given the difficulty of harnessing a large number of computing resources. That way, most of the approaches in the literature focus on studying the model viability throughout a simulation-driven experimentation. However, simulations assume idealistic conditions that can influence the algorithmic performance and bias results when conducted in a real platform. Therefore, we aim at validating simulations by running a real version of the algorithm. Results show that the algorithmic performance is rather accurate to the predicted one whilst times-to-solutions can be drastically decreased when compared to the estimation of a sequential run.

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Validating a Peer-to-Peer Evolutionary Algorithm

Laredo

Bouvry

Mostaghim

et al. 2012

Applications of Evolutionary Computation

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Cloud Driven Design of a Distributed Genetic Programming Platform

Derby

Veeramachaneni

O’Reilly

2013

Applications of Evolutionary Computation

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FlexGP

et al. 2014

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We describe FlexGP, the first Genetic Programming system to perform symbolic regression on large-scale datasets on the cloud via massive data-parallel ensemble learning. FlexGP provides a decentralized, fault tolerant parallelization framework that runs many copies of Multiple Regression Genetic Programming, a sophisticated symbolic regression algorithm, on the cloud. Each copy executes with a different sample of the data and different parameters. The framework can create a fused model or ensemble on demand as the individual GP learners are evolving. We demonstrate our framework by deploying 100 independent GP instances in a massive data-parallel manner to learn from a dataset composed of 515K exemplars and 90 features, and by generating a competitive fused model in less than 10 minutes.

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A Peer-to-Peer Approach to Genetic Programming

Cited by 5 publications

References 11 publications

Validating a Peer-to-Peer Evolutionary Algorithm

Validating a Peer-to-Peer Evolutionary Algorithm

Cloud Driven Design of a Distributed Genetic Programming Platform

FlexGP

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