Inertial geometric particle swarm optimization

Moraglio, Alberto; Togelius, Julian

doi:10.1109/cec.2009.4983182

Cited by 5 publications

(2 citation statements)

References 14 publications

(12 reference statements)

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“…The attribute selection filter used CfsSubsetEval as the evaluator function and particle swarm optimization search algorithm with a population size of 1000. 30 This ensured that the test data were separate from the training data used to build the model in each fold. We report prediction performance using leave-one-out-cross-validation for each renal phase separately.…”

Section: Multivariate Analysismentioning

confidence: 99%

Imaging-genomic pipeline for identifying gene mutations using three-dimensional intra-tumor heterogeneity features

et al. 2015

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Abstract. This paper presents an imaging-genomic pipeline to derive three-dimensional intra-tumor heterogeneity features from contrast-enhanced CT images and correlates them with gene mutation status. The pipeline has been demonstrated using CT scans of patients with clear cell renal cell carcinoma (ccRCC) from The Cancer Genome Atlas. About 15% of ccRCC cases reported have BRCA1-associated protein 1 (BAP1) gene alterations that are associated with high tumor grade and poor prognosis. We hypothesized that BAP1 mutation status can be detected using computationally derived image features. The molecular data pertaining to gene mutation status were obtained from the cBioPortal. Correlation of the image features with gene mutation status was assessed using the Mann-Whitney-Wilcoxon rank-sum test. We also used the random forests classifier in the Waikato Environment for Knowledge Analysis software to assess the predictive ability of the computationally derived image features to discriminate cases with BAP1 mutations for ccRCC. Receiver operating characteristics were obtained using a leave-one-out-cross-validation procedure. Our results show that our model can predict BAP1 mutation status with a high degree of sensitivity and specificity. This framework demonstrates a methodology for noninvasive disease biomarker detection from contrast-enhanced CT images.

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Section: Multivariate Analysismentioning

confidence: 99%

Imaging-genomic pipeline for identifying gene mutations using three-dimensional intra-tumor heterogeneity features

et al. 2015

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“…It compares the effectiveness of the basic particle swarm optimization scheme (BPSO) with each of BPSO with mutation, constriction particle swarm optimization (CPSO) with mutation, and CPSO without mutation. Alberto Moraglio and Julian Togelius [13] proposed an extension of Geometric Particle Swarm Optimization (GPSO), the inertial GPSO (IGPSO), that generalizes the traditional PSO endowed with the full equation of motion of particles to generic search spaces. Yongfang Chu and Zhihua Cui [14] developed an algorithm based on Neighborhood Sharing Particle Swarm Optimization which replaces the individual experience by the neighbor sharing information of current state and proposes the neighbourhood sharing particle swarm algorithm.…”

Section: Cmentioning

confidence: 99%

A Novel Approach of Vector Quantization Using Modified Particle Swarm Optimization Algorithm for Generating Efficient Codebook

Rani¹

2017

ijarcs

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Abstract:The objective of this proposed work is to develop an efficient compression algorithm without compromising image quality. Mostly vector quantization designs a local optimal codebook for compressing images effectively. In recent days, several optimization algorithms are used to generate global codebook. Particle swarm optimization algorithm is one of the efficient evolutionary computing algorithms which helps to reduce the computation time and generates an efficient codebook as well. This paper presents a novel approach of Modified Particle Swarm Optimization(MPSO) technique using vector quantization technique. The initial swarm is formed out of image blocks with high variance. Furthermore the random values for updating the gbest and pbest in velocity update equation has been replaced with optimal values which has significantly improved the image quality. Experimental results on test images show that MPSO suits well for all types of images, yielding very high PSNR values compared to Standard PSO and K-means algorithms.

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Function Optimization in Conformal Space by Using Spherical Inversions and Reflections

Rubio

Aguirre

Guzmán

2014

Advances in Artificial Intelligence -- IBERAMIA 2014

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Inertial geometric particle swarm optimization

Cited by 5 publications

References 14 publications

Imaging-genomic pipeline for identifying gene mutations using three-dimensional intra-tumor heterogeneity features

Imaging-genomic pipeline for identifying gene mutations using three-dimensional intra-tumor heterogeneity features

A Novel Approach of Vector Quantization Using Modified Particle Swarm Optimization Algorithm for Generating Efficient Codebook

Function Optimization in Conformal Space by Using Spherical Inversions and Reflections

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