A memory efficient and continuous-valued compact EDA for large scale problems

Rojas–Galeano, Sergio; Rodríguez, Néstor

doi:10.1145/2330163.2330204

Cited by 7 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this respect, we envisage two interesting avenues for further research, the first one related to algorithmic crafting so as to speed up the computation of the kernel function, which might be a bottleneck in such big data scenarios e.g. [ 14 , 27 , 50 , 51 ]; the second one is considering compact representations of the probability model enabling memory and time savings during updating of its parameters [ 23 , 52 – 54 ].…”

Section: Discussionmentioning

confidence: 99%

Discovering feature relevancy and dependency by kernel-guided probabilistic model-building evolution

Rodríguez

Rojas–Galeano

2017

BioData Mining

Self Cite

View full text Add to dashboard Cite

BackgroundDiscovering relevant features (biomarkers) that discriminate etiologies of a disease is useful to provide biomedical researchers with candidate targets for further laboratory experimentation while saving costs; dependencies among biomarkers may suggest additional valuable information, for example, to characterize complex epistatic relationships from genetic data. The use of classifiers to guide the search for biomarkers (the so–called wrapper approach) has been widely studied. However, simultaneously searching for relevancy and dependencies among markers is a less explored ground.ResultsWe propose a new wrapper method that builds upon the discrimination power of a weighted kernel classifier to guide the search for a probabilistic model of simultaneous marginal and interacting effects. The feasibility of the method was evaluated in three empirical studies. The first one assessed its ability to discover complex epistatic effects on a large–scale testbed of generated human genetic problems; the method succeeded in 4 out of 5 of these problems while providing more accurate and expressive results than a baseline technique that also considers dependencies. The second study evaluated the performance of the method in benchmark classification tasks; in average the prediction accuracy was comparable to two other baseline techniques whilst finding smaller subsets of relevant features. The last study was aimed at discovering relevancy/dependency in a hepatitis dataset; in this regard, evidence recently reported in medical literature corroborated our findings. As a byproduct, the method was implemented and made freely available as a toolbox of software components deployed within an existing visual data–mining workbench.ConclusionsThe mining advantages exhibited by the method come at the expense of a higher computational complexity, posing interesting algorithmic challenges regarding its applicability to large–scale datasets. Extending the probabilistic assumptions of the method to continuous distributions and higher–degree interactions is also appealing. As a final remark, we advocate broadening the use of visual graphical software tools as they enable biodata researchers to focus on experiment design, visualisation and data analysis rather than on refining their scripting programming skills.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-017-0131-y) contains supplementary material, which is available to authorized users.

show abstract

Section: Discussionmentioning

confidence: 99%

Discovering feature relevancy and dependency by kernel-guided probabilistic model-building evolution

Rodríguez

Rojas–Galeano

2017

BioData Mining

Self Cite

View full text Add to dashboard Cite

show abstract

“…(vi) Noisy/dynamic optimisation: It will be useful to investigate new compact optimisation schemes (for instance, with new distributions or new sampling mechanisms) to deal with noisy functions or Dynamic Optimisation Problems (DOPs) [328,329] (i.e., problems where the search space changes over time). Concerning noisy optimisation, apart from some works on cGA [240,[330][331][332], the only few works dealing with noise are based on rcGA [333], cDE [334], and a compact EDA framework [335]. In terms of dynamic optimisation, a Hooke-Jeeves-based Memetic Algorithm (HJMA) was presented in [336], where experiments have been conducted on the Moving Peaks (MP) problem (this benchmark is defned in [337] as an artifcial multidimensional landscape comprising multiple peaks, each of which has its height, width, and position slightly altered whenever a change occurs in the environment.…”

Section: Future Research Directionsmentioning

confidence: 99%

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

Khalfi,

Caraffini,

Iacca

2023

International Journal of Intelligent Systems

View full text Add to dashboard Cite

In the last three decades, the field of computational intelligence has seen a profusion of population-based metaheuristics applied to a variety of problems, where they achieved state-of-the-art results. This remarkable growth has been fuelled and, to some extent, exacerbated by various sources of inspiration and working philosophies, which have been thoroughly reviewed in several recent survey papers. However, the present survey addresses an important gap in the literature. Here, we reflect on a systematic categorisation of what we call “lightweight” metaheuristics, i.e., optimisation algorithms characterised by purposely limited memory and computational requirements. We focus mainly on two classes of lightweight algorithms: single-solution metaheuristics and “compact” optimisation algorithms. Our analysis is mostly focused on single-objective continuous optimisation. We provide an updated and unified view of the most important achievements in the field of lightweight metaheuristics, background concepts, and most important applications. We then discuss the implications of these algorithms and the main open questions and suggest future research directions.

show abstract

“…The available operators for binary genotypes are: one bit and multiple bit flip mutation. In the case of real-coded genotypes it provides the truncated Gaussian mutation [15]. Additionally, Python script code-injection is also enabled.…”

Section: Mutationmentioning

confidence: 99%

Deconstructing GAs into Visual Software Components

Garzón-Rodriguez

Diosa

Rojas–Galeano

2015

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

Self Cite

View full text Add to dashboard Cite

We envisage Genetic Algorithms (GA) as search-based optimisation techniques encompassing independent bio-inspired operators and representations that are realizable as selfcontained deployable computational units. In other words, we think of GAs as a set of software components conforming to a formally-defined evolution-oriented composition model. Furthermore, we imagine such components being assembled on a visual programming-free board, much like prefabricated electronic chips are wired up to build electronic devices. Here we introduce Goldenberry-GA, a toolbox of visual software components complying with these premises that has been built over the Orange framework for data mining. The paper describes at user-level the suite of new released components (GeneticAlgorithm, InitialPopulation, SolutionRepresentation, Selection, Mutation, Crossover), including working examples that demonstrate some advantages of the reuse and extension principles of its underlying component-based software architecture. It also explains the composition model specification of the toolbox and the software design patterns that were taken into account during its development. A qualitative comparative study with similar Evolutionary Computation frameworks is reported so as to highlight strengths and weaknesses of the toolbox, as well as to point out directions for future work.Goldenberry-GA is open-source under the New BSD License. Downloading and installation guides are available at:

show abstract

A memory efficient and continuous-valued compact EDA for large scale problems

Cited by 7 publications

References 15 publications

Discovering feature relevancy and dependency by kernel-guided probabilistic model-building evolution

Discovering feature relevancy and dependency by kernel-guided probabilistic model-building evolution

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

Deconstructing GAs into Visual Software Components

Contact Info

Product

Resources

About