Flex-GP: Genetic Programming on the Cloud

Sherry, Dylan; Veeramachaneni, Kalyan; McDermott, James; O’Reilly, Una-May

doi:10.1007/978-3-642-29178-4_48

Cited by 26 publications

(13 citation statements)

References 5 publications

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“…Request permissions from permissions@acm.org. them for evolutionary computation experiments [40]. Nothing beats free, which is one of the reasons why the era of Citizen Science already started a few years ago (with SETI@home [2] and then BOINC [1]) and it offers a vast amount of computational resources to be used, if only you know how to attract them.…”

Section: Introductionmentioning

confidence: 99%

Designing and Modeling a Browser-Based DistributedEvolutionary Computation System

Merelo

García‐Sánchez

2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Web browsers have scaled from simple page-rendering engines to operating systems that include most services the lower OS layer has, with the added facility that applications can be run by just visiting a web page. In this paper we will describe the front and back end of a distributed evolutionary computation system that uses the browser's capabilities of running programs written in JavaScript. We will focus on two different aspects of volunteer computing: first, the pragmatic: where to find those resources, which ones can be used, what kind of support you have to give them; and then, the theoretical: how evolutionary algorithms can be adapted to an environment in which nodes come and go, have different computing capabilities and operate in complete asynchrony of each other. We will examine the setup needed to create a simple distributed evolutionary algorithm using JavaScript, with the intention of eventually finding a model of how users react to it by collecting data from several experiments featuring a classical benchmark function.

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Section: Introductionmentioning

confidence: 99%

Designing and Modeling a Browser-Based DistributedEvolutionary Computation System

Merelo

García‐Sánchez

2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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“…Scalability is also a very challenging problem. Efforts to increase scalability of GP via GPUs and cloud computing have been reported (such as in [8], [9]). It is our belief that, if GP-SR is to be a trustable big learning [10] tool, it needs to take advantage of the developments in the general ML as well as the parallel and distributed computing techniques.…”

Section: Introductionmentioning

confidence: 99%

Improving genetic programming based symbolic regression using deterministic machine learning

Icke

Bongard

2013

2013 IEEE Congress on Evolutionary Computation

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Abstract-Symbolic regression (SR) is a well studied method in genetic programming (GP) for discovering free-form mathematical models from observed data. However, it has not been widely accepted as a standard data science tool. The reluctance is in part due to the hard to analyze random nature of GP and scalability issues. On the other hand, most popular deterministic regression algorithms were designed to generate linear models and therefore lack the flexibility of GP based SR (GP-SR). Our hypothesis is that hybridizing these two techniques will create a synergy between the GP-SR and deterministic approaches to machine learning, which might help bring the GP based techniques closer to the realm of big learning. In this paper, we show that a hybrid deterministic/GP-SR algorithm outperforms GP-SR alone and the state-of-the-art deterministic regression technique alone on a set of multivariate polynomial symbolic regression tasks as the system to be modeled becomes more multivariate.

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“…Our algorithms have the potential to scale to real-world problems upon utilizing GPUs [18] and/or cloud computing [19].…”

Section: Discussionmentioning

confidence: 99%

Modeling hierarchy using symbolic regression

Icke

Bongard

2013

2013 IEEE Congress on Evolutionary Computation

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Abstract-Symbolic Regression is an attractive modeling approach because it can capture and present, mathematically, relationships between variables of interest. However, given n variables to model, symbolic regression returns a flat list of n equations. As the number of state variables to be modeled scales, interpretation of such a list becomes difficult. Here we present a symbolic regression method that detects and captures hidden hierarchy in a given system. The method returns the equations in a hierarchical dependency graph, which increases the interpretability of the results. We demonstrate that two variations of this hierarchical modeling approach outperform non-hierarchical symbolic regression on a synthetic data suite.

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Flex-GP: Genetic Programming on the Cloud

Cited by 26 publications

References 5 publications

Designing and Modeling a Browser-Based DistributedEvolutionary Computation System

Designing and Modeling a Browser-Based DistributedEvolutionary Computation System

Improving genetic programming based symbolic regression using deterministic machine learning

Modeling hierarchy using symbolic regression

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