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Automatic Sampler Discovery via Probabilistic Programming and Approximate Bayesian Computation [chapter]

Yura Perov, Frank Wood
2016 Lecture Notes in Computer Science  
Specifically, we learn the procedure code of samplers for one-dimensional distributions.  ...  Our results are competive relative to state-of-the-art genetic programming methods and demonstrate that we can learn approximate and even exact samplers.  ...  Figure 8 shows that PMCMC inference performance is similar to genetic programming. In contrast to genetic programming, PMCMC is statistically valid estimator of the target distribution.  ... 
doi:10.1007/978-3-319-41649-6_27 fatcat:3jbkfa2t7bhubph2h6t6wl4z6e

Observation of the cancer patient journey: a learning curve for Genetic Counsellors

JA Taylor, KJ Mann
2012 Hereditary Cancer in Clinical Practice  
However, in their training, genetic counsellors seldom have much exposure to the medical environments in which their clients receive their cancer treatment.  ...  both consultations and medical procedures in their speciality area.  ... 
doi:10.1186/1897-4287-10-s2-a51 pmcid:PMC3326867 fatcat:siybyzaae5b47hliit34tmeyui

Policy Optimization by Genetic Distillation [article]

Tanmay Gangwani, Jian Peng
2018 arXiv   pre-print
Genetic algorithms have been widely used in many practical optimization problems.  ...  GPO uses imitation learning for policy crossover in the state space and applies policy gradient methods for mutation.  ...  The child policy is learned using a two-step procedure. A schematic of the methodology is shown in Figure 2 .  ... 
arXiv:1711.01012v2 fatcat:d3rvfngimzbozkbb4ykqe3r274

Probability-Enhanced Predictions in the Anticipatory Classifier System [chapter]

Martin V. Butz, David E. Goldberg, Wolfgang Stolzmann
2001 Lecture Notes in Computer Science  
A behavioral act in the ACS with all the involved learning procedures Similar to all LCSs, the ACS evolves a population of classi ers.  ...  In order to be able to learn an internal environmental model, the ACS relies on a causality in successive perceptions of an environment.  ... 
doi:10.1007/3-540-44640-0_4 fatcat:lgmcebaprjclvf5dz7nij2ejtq

Japanese Quail's Genetic Background Modulates Effects of Chronic Stress on Emotional Reactivity but Not Spatial Learning

Agathe Laurence, Cécilia Houdelier, Christophe Petton, Ludovic Calandreau, Cécile Arnould, Angélique Favreau-Peigné, Christine Leterrier, Alain Boissy, Marie-Annick Richard-Yris, Sophie Lumineau, Claudia Mettke-Hofmann
2012 PLoS ONE  
They were then trained in a T-maze for seven days and their spatial learning was tested. The chronic stress protocol had an impact on resting, preening and foraging in the home cage.  ...  Chronic stress is known to enhance mammals' emotional reactivity and alters several of their cognitive functions, especially spatial learning. Few studies have investigated such effects in birds.  ...  in novel environments [65] .  ... 
doi:10.1371/journal.pone.0047475 pmid:23071811 pmcid:PMC3469497 fatcat:dtb2ype4rzfwtcy7jc5j36vof4

Q-Learning Applied to Genetic Algorithm-Fuzzy Approach for On-Line Control in Autonomous Agents

Hengameh Sarmadi
2009 Journal of Intelligent Systems  
The optimization of the fuzzy rule-based system is performed by a combination of genetic algorithms and Q-learning, whereby an agent-based predicting machine with desired performance is achieved.  ...  For illustrating the validity of the described technique in control applications, the approach is evaluated on the acrobot task.  ...  The embodiment of an action selection procedure should be realized as an expression of the learning procedure in an anticipatory fashion, which considers environment dynamic and makes a prediction about  ... 
doi:10.1515/jisys.2009.18.1-2.1 fatcat:nfbvmoqhwfenjkkz65mbpzbot4

Application of Genetic Algorithms in Stock Market Simulation

Jiří Štěpánek, Jiří Šťovíček, Richard Cimler
2012 Procedia - Social and Behavioral Sciences  
Next point is to show, how can be this implementation of genetic algorithms used in learning process of simulation.  ...  It is difficult to predict changes in prices of stocks because of ma ny parameters in behavioral algorithms. There is also problem with learning soft-skills because of many variables.  ...  Holding shares is not considered in this model. Decision function is created by GP procedures and distribution of profits should mount to normal distribution.  ... 
doi:10.1016/j.sbspro.2012.06.619 fatcat:hnnerrr6fvdull53zkj4zwugkm

Transfer learning in genome-wide association studies with knockoffs [article]

Shuangning Li, Zhimei Ren, Chiara Sabatti, Matteo Sesia
2021 arXiv   pre-print
for, and efficiently learn from the genetic variation associated to diverse ancestries.  ...  Finally, we apply these methods to analyze several phenotypes in the UK Biobank data set, demonstrating that transfer learning helps knockoffs discover more numerous associations in the data collected  ...  The three transfer learning methods introduced in Section 2 are implemented and compared with the results of the vanilla knockoffs procedure.  ... 
arXiv:2108.08813v1 fatcat:vvkxdnaykfeq5lce5xzqn637eq

Selection of important variables by statistical learning in genome-wide association analysis

Wei Yang, C Charles Gu
2009 BMC Proceedings  
Several statistical learning methods seem quite promising in this context.  ...  The complexity of such analysis is multiplied when one has to consider interaction effects, be they among the genetic variations (G × G) or with environment risk factors (G × E).  ...  Acknowledgements This research is supported in part by an NIH grant HL091028 and an AHA grant 0855626G. The Genetic Analysis Workshops are supported by NIH grant R01 GM031575. We thank Dr.  ... 
doi:10.1186/1753-6561-3-s7-s70 pmid:20018065 pmcid:PMC2795972 fatcat:tubcmuq5xfayboazupgoyf3qby

Space Radiation Alters Genotype–Phenotype Correlations in Fear Learning and Memory Tests

Ovidiu Dan Iancu, Sydney Weber Boutros, Reid H. J. Olsen, Matthew J. Davis, Blair Stewart, Massarra Eiwaz, Tessa Marzulla, John Belknap, Christina M. Fallgren, Elijah F. Edmondson, Michael M. Weil, Jacob Raber
2018 Frontiers in Genetics  
Changes in the environment can alter genotype-phenotype relationships.  ...  stability of the genetic component of fear learning and memory-related measures.  ...  Robert Hitzemann, Department of Behavioral Neuroscience, OHSU, for providing the HS/Npt mice for breeding the mice used in this study.  ... 
doi:10.3389/fgene.2018.00404 pmid:30356920 pmcid:PMC6190902 fatcat:tvwwv4xcajdnvabmljx3fqodk4

Rule acquisition for cognitive agents by using estimation of distribution algorithms

Tokue Nishimura, Hisashi Handa
2010 International Journal of Knowledge Engineering and Soft Data Paradigms  
In general, learning mechanisms are equipped for such agents in order to realize intellgent behaviors.  ...  In this paper, we propose a new Estimation of Distribution Algorithms (EDAs) which can acquire effective rules for cognitive agents.  ...  General calculation procedure of Estimation of Distribution Algorithms briefly introduced in IV. EDA-CRF proposed in V.  ... 
doi:10.1504/ijkesdp.2010.035905 fatcat:fdh4zkrecrcxzm7x4o25dsjkl4

A Novel Evolutionary Algorithm for Solving Static Data Allocation Problem in Distributed Database Systems

Ali Safari Mamaghani, Mostafa Mahi, Mohammad Reza Meybodi, Mohammad Hosseinzadeh Moghaddam
2010 2010 Second International Conference on Network Applications, Protocols and Services  
In this paper an approximate algorithm has been proposed. This algorithm is a hybrid evolutionary algorithm obtained from combining object migration learning automata and genetic algorithm.  ...  transfer cost incurred in executing the queries.  ...  LEARNING AUTOMATA AND GENETIC ALGORITHMS Learning automata are adaptive decision-making devices operating on unknown random environments.  ... 
doi:10.1109/netapps.2010.10 fatcat:yq3mku2nk5he7o63fgwe25gr5y

Accuracy of Genomic Prediction in Switchgrass ( Panicum virgatum L.) Improved by Accounting for Linkage Disequilibrium

Guillaume P. Ramstein, Joseph Evans, Shawn M. Kaeppler, Robert B. Mitchell, Kenneth P. Vogel, C. Robin Buell, Michael D. Casler
2016 G3: Genes, Genomes, Genetics  
We evaluated prediction procedures that varied not only by learning schemes and prediction models, but also by the way the data were preprocessed to account for redundancy in marker information.  ...  Genomic selection (GS) is an attractive technology to generate rapid genetic gains in switchgrass, and meet the goals of a substantial displacement of petroleum use with biofuels in the near future.  ...  This research was funded in part by the following agencies and organizations: the  ... 
doi:10.1534/g3.115.024950 pmid:26869619 pmcid:PMC4825640 fatcat:3brf3mjhdrdbjizoglrg57m2wy

APRIORI BASED MACHINE LEARNING IN POWER DISTRIBUTION NETWORK

Jasreen Kaur
2018 International Journal of Advanced Research in Computer Science  
This paper proposed Apriori based machine learning algorithm to predict the loads and schedule it in the optimum way.  ...  This imbalance in loads at different phases of system affects tool utilization, voltage ranges system stability and security .The comprehensive review has shown that the use of machine learning in not  ...  Power distribution network in hetrogenous environment Table I .  ... 
doi:10.26483/ijarcs.v9i2.5679 fatcat:2qej3tte5jgorckn4lyppe3t6m

Analyze Effects of the Genetic Programming-Based Emergence Engineering in Trustiness of Engineering Solutions

Babak Farhadi, Eslam Nazemi
2015 International Journal of Computer Applications  
In self-organization filed, Emergence Engineering is a new idea in software engineering scope which aims at setting up emergent phenomena in categories of individuals in order to extract those phenomena  ...  In this paper we analyze the effects of the clarification of the behavioral explanation in terms of trustiness of the solutions.  ...  We divided them in two procedure. Election Procedure In this challenge, one agent is elected as "commander" and all agents accordant to this choice is the distributed nature.  ... 
doi:10.5120/19986-1943 fatcat:v3lrp5yxabewxfccflghzi6p7q
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