18,980 research outputs found
Results of Evolution Supervised by Genetic Algorithms
A series of results of evolution supervised by genetic algorithms with
interest to agricultural and horticultural fields are reviewed. New obtained
original results from the use of genetic algorithms on structure-activity
relationships are reported.Comment: 6 pages, 1 Table, 2 figure
Tailored Source Code Transformations to Synthesize Computationally Diverse Program Variants
The predictability of program execution provides attackers a rich source of
knowledge who can exploit it to spy or remotely control the program. Moving
target defense addresses this issue by constantly switching between many
diverse variants of a program, which reduces the certainty that an attacker can
have about the program execution. The effectiveness of this approach relies on
the availability of a large number of software variants that exhibit different
executions. However, current approaches rely on the natural diversity provided
by off-the-shelf components, which is very limited. In this paper, we explore
the automatic synthesis of large sets of program variants, called sosies.
Sosies provide the same expected functionality as the original program, while
exhibiting different executions. They are said to be computationally diverse.
This work addresses two objectives: comparing different transformations for
increasing the likelihood of sosie synthesis (densifying the search space for
sosies); demonstrating computation diversity in synthesized sosies. We
synthesized 30184 sosies in total, for 9 large, real-world, open source
applications. For all these programs we identified one type of program analysis
that systematically increases the density of sosies; we measured computation
diversity for sosies of 3 programs and found diversity in method calls or data
in more than 40% of sosies. This is a step towards controlled massive
unpredictability of software
PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison
The selection, development, or comparison of machine learning methods in data
mining can be a difficult task based on the target problem and goals of a
particular study. Numerous publicly available real-world and simulated
benchmark datasets have emerged from different sources, but their organization
and adoption as standards have been inconsistent. As such, selecting and
curating specific benchmarks remains an unnecessary burden on machine learning
practitioners and data scientists. The present study introduces an accessible,
curated, and developing public benchmark resource to facilitate identification
of the strengths and weaknesses of different machine learning methodologies. We
compare meta-features among the current set of benchmark datasets in this
resource to characterize the diversity of available data. Finally, we apply a
number of established machine learning methods to the entire benchmark suite
and analyze how datasets and algorithms cluster in terms of performance. This
work is an important first step towards understanding the limitations of
popular benchmarking suites and developing a resource that connects existing
benchmarking standards to more diverse and efficient standards in the future.Comment: 14 pages, 5 figures, submitted for review to JML
Beyond Biomass: Valuing Genetic Diversity in Natural Resource Management
Strategies for increasing production of goods from working and natural systems have raised concerns that the diversity of species on which these services depend may be eroding. This loss of natural capital threatens to homogenize global food supplies and compromise the stability of human welfare. We assess the trade off between artificial augmentation of biomass and degradation of biodiversity underlying a populations' ability to adapt to shocks. Our application involves the augmentation of wild stocks of salmon. Practices in this system have generated warnings that genetic erosion may lead to a loss of the “portfolio effect” and the value of this loss is not accounted for in decision making. We construct an integrated bioeconomic model of salmon biomass and genetic diversity. Our results show how practices that homogenize natural systems can still generate positive returns. However, the substitution of more physical capital and labor for natural capital must be maintained for gains to persist, weakens the capacity for adaptation should this investment cease, and can cause substantial loss of population wildness. We apply an emerging optimization method—approximate dynamic programming—to solve the model without simplifying restrictions imposed previously
Regulatory motif discovery using a population clustering evolutionary algorithm
This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences
Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum.
The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench) lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In fivefold cross-validation, prediction accuracies ranged from 0.46 (PBN) to 0.49 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.75 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4-52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits
MISSEL: a method to identify a large number of small species-specific genomic subsequences and its application to viruses classification
Continuous improvements in next generation sequencing technologies led to ever-increasing collections of genomic sequences, which have not been easily characterized by biologists, and whose analysis requires huge computational effort. The classification of species emerged as one of the main applications of DNA analysis and has been addressed with several approaches, e.g., multiple alignments-, phylogenetic trees-, statistical- and character-based methods
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