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"parents selection"
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When Graham Cavanaugh divorced his first wife it was to marry his girlfriend, Audra, a woman as irrepressible as she is spontaneous and fun. But, Graham learns, life with Audra can be exhausting. Audra firmly believes that through the sheer force of her personality she can overcome the most socially challenging interactions, shepherding her son through awkward playdates and origami club, and even deciding to establish a friendship with Graham's first wife, Elspeth. As Graham and Audra share dinners, holidays, and late glasses of wine with his first wife he starts to wonder: How can anyone love two such different women? Did I make the right choice? Is there a right choice?
Book
A frequency-based parent selection for reducing the effect of evaluation time bias in asynchronous parallel multi-objective evolutionary algorithms
Parallel evolutionary algorithms (PEAs) have been studied for reducing the execution time of evolutionary algorithms by utilizing parallel computing. An asynchronous PEA (APEA) is a scheme of PEAs that increases computational efficiency by generating a new solution immediately after a solution evaluation completes without the idling time of computing nodes. However, because APEA gives more search opportunities to solutions with shorter evaluation times, the evaluation time bias of solutions negatively affects the search performance. To overcome this drawback, this paper proposes a new parent selection method to reduce the effect of evaluation time bias in APEAs. The proposed method considers the search frequency of solutions and selects the parent solutions so that the search progress in the population is uniform regardless of the evaluation time bias. This paper conducts experiments on multi-objective optimization problems that simulate the evaluation time bias. The experiments use NSGA-III, a well-known multi-objective evolutionary algorithm, and compare the proposed method with the conventional synchronous/asynchronous parallelization. The experimental results reveal that the proposed method can reduce the effect of the evaluation time bias while reducing the computing time of the parallel NSGA-III.
Journal Article
Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
While IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) networks should be equipped to deal with the hard wireless challenges of industrial environments, the sensor networks are often still limited by the characteristics of the used physical (PHY) layer. Therefore, the TSCH community has recently started shifting research efforts to the support of multiple PHY layers, to overcome this limitation. On the one hand, integrating such multi-PHY support implies dealing with the PHY characteristics to fit the resource allocation in the TSCH schedule, and on the other hand, defining policies on how to select the appropriate PHY for each network link. As such, first a heuristic is proposed that is a step towards a distributed PHY and parent selection mechanism for slot bonding multi-PHY TSCH sensor networks. Additionally, a proposal on how this heuristic can be implemented in the IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH) protocol stack and its Routing Protocol for Low-power and Lossy network (RPL) layer is also presented. Slot bonding allows the creation of different-sized bonded slots with a duration adapted to the data rate of each chosen PHY. Afterwards, a TSCH slot bonding implementation is proposed in the latest version of the Contiki-NG Industrial Internet of Things (IIoT) operating system. Subsequently, via extensive simulation results, and by deploying the slot bonding implementation on a real sensor node testbed, it is shown that the computationally efficient parent and PHY selection mechanism approximates the packet delivery ratio (PDR) results of a near-optimal, but computationally complex, centralized scheduler.
Journal Article
A Generalized K2 Algorithm for Learning Bayesian Network Structures Using Ridge Regression
In this article, we propose a new approach of the K2 algorithm to enhance the process of selecting candidate parents in Bayesian networks by incorporating dependency criteria based on ridge regression. Traditional K2 algorithms primarily rely on scoring functions to evaluate parent sets, which may not effectively capture all dependencies in the presence of multicollinearity. Our approach leverages ridge regression to address this limitation by penalizing overly complex models, thereby providing a more robust mechanism for parent selection. Through real-world data applications, we demonstrate that our generalized algorithm significantly improves the accuracy and reliability of parent selection in Bayesian networks. The findings suggest that this integration of ridge regression with the K2 algorithm offers a promising avenue for advancing Bayesian network structure learning, particularly in complex data.
Journal Article
Predicting genetic variance in bi-parental breeding populations is more accurate when explicitly modeling the segregation of informative genomewide markers
Robust predictions of genetic variances for important traits would facilitate greater genetic gains in plant breeding. Previous attempts to predict the genetic variance (
σ
G
2
) of traits in bi-parental breeding populations were inconsistent and context specific. The weakness of methods that consider the phenotypic distance, genetic distance, and relationship-based distance of pairs of parents, which we collectively term
historical
methods, stems from the fact that they do not explicitly model the segregation of the underlying genetic effects for a trait within a population. To address this issue, we propose the use of three
modern
methods made possible by the commonplace use of genomewide molecular marker data and genomic selection in modern breeding programs. These modern methods utilize both phenotypic and genotypic records to, in varying degrees, explicitly model the segregation of informative genomewide markers to predict
σ
G
2
in bi-parental breeding populations. In this study, we evaluate the accuracy of historical and modern methods to predict
σ
G
2
using 40 field-tested bi-parental barley breeding populations evaluated during 2003–2010 for Fusarium head blight severity. In general, the modern methods predicted the field-based estimates of
σ
G
2
more accurately than the historical methods. Specifically, the modern method that most explicitly models the segregation of informative genomewide markers, called ‘PopVar,’ was the most accurate
σ
G
2
prediction method.
Journal Article
LMH-RPL: a load balancing and mobility aware secure hybrid routing protocol for low power lossy network
PurposeRouting protocol for low-power lossy network (RPL) being the de facto routing protocol used by low power lossy networks needs to provide adequate routing service to mobile nodes (MNs) in the network. As RPL is designed to work under constraint power requirements, its route updating frequency is not sufficient for MNs in the network. The purpose of this study is to ensure that MNs enjoy seamless connection throughout the network with minimal handover delay.Design/methodology/approachThis study proposes a load balancing mobility aware secure hybrid – RPL in which static node (SN) identifies route using metrics like expected transmission count, and path delay and parent selection are further refined by working on remaining energy for identifying the primary route and queue availability for secondary route maintenance. MNs identify route with the help of smart timers and by using received signal strength indicator sampling of parent and neighbor nodes. In this work, MNs are also secured against rank attack in RPL.FindingsThis model produces favorable result in terms of packet delivery ratio, delay, energy consumption and number of living nodes in the network when compared with different RPL protocols with mobility support. The proposed model reduces packet retransmission in the network by a large margin by providing load balancing to SNs and seamless connection to MNs.Originality/valueIn this work, a novel algorithm was developed to provide seamless handover for MNs in network. Suitable technique was developed to provide load balancing to SNs in network by maintaining appropriate secondary route.
Journal Article
A critical analysis of RPL objective functions in internet of things paradigm
IoT supports a spectrum of applications, each of which has certain specific requirements. For instance, mission critical applications cannot tolerate delay in data transmission however simple monitoring applications are delay tolerant. The lifetime and performance of IoT sensor networks depend on the metric/constraint (ETX, Energy etc.) selected for routing path, data size and quality of service required. The selection of metric /constraint dependent Objective function (OF) in RPL provides a range of solutions for IoT applications. However, state-of-art solutions mainly focus on single metric/constraint resulting in poor performance of protocol. To understand the protocol behavior for different metrics (single and combined) a complete evaluation of RPL over important performance parameters is needed. Researchers have proposed several routing algorithms which are application specific and do not define a generic parent selection process. We require a structured algorithm for Minimum Rank Hysteresis Objective Function (MRHOF) applicable to majority of IoT applications. In this paper we have proposed a generalized algorithm for MRHOF along with routing path cost evaluation which defines the complete parent selection process. Further, comparative analysis of different RPL OFs has been done to identify suitable OF for enhanced RPL performance. Performance evaluation parameters have been extended to PDR, power consumption, hop count, average ETX, Rt metric and inter packet time, for different network size and link quality. Results are obtained using Cooja simulator of Contiki. RPL with combined metric provide 24% higher PDR, 28% lower power consumption and 39% lower inter-packet time as compared to RPL with single metric.
Journal Article
General and specific combining ability in sweet sorghum
In breeding of sweet sorghum hybrids, non-additive genetic effects are important in phenotypic expression of the traits of interest. The aim of this study was to evaluate the general combining ability (GCA) of sweet sorghum lines and the specific combining ability (SCA) of the hybrids for agronomic and technological traits. Five fertility restorer lines, four male-sterile lines, and their hybrids from partial diallel crosses were evaluated in experiments laid out in a 5 x 6 triple rectangular lattice design in the municipalities of Lavras, MG and Sete Lagoas, MG, Brazil. Diallel analysis was performed using the Griffing model adapted to partial diallel crosses. There was a significant effect of GCA and SCA for most of the traits evaluated, indicating the participation of additive or dominant genes in inheritance. The restorer lines CMSX508, BRS 511, CMSXS643, and CMSXS646 show potential for use as parents in sorghum breeding programs.
Journal Article
Centrality measure and visualization technique for multiple-parent nodes of earthquakes based on correlation-metric
In this paper, we address the problem of earthquake declustering, and propose a
k
-nearest neighbors approach based on the selection of multiple-parent nodes with respect to each of the given earthquakes, which can be regarded as a natural extension of the conventional correlation-metric method based on the selection of a single-parent node. Based on this approach, we develop a centrality measure that exploits link weight assigned by a logarithmic-distance scheme and a technique of individually visualizing each set of child nodes with respect to given target earthquakes. For experimental evaluation, we used an earthquake catalog covering Japan and selected 24 earthquakes that caused considerable damage or casualties. We first show that our proposed centrality measure using a logarithmic-distance scheme can rank these 24 major earthquakes higher than four link-weighting schemes (i.e., uniform, magnitude, inverse-distance, and normalized-inverse-distance weighting) and conventional single-parent selection. We then show that unlike the conventional approach to simultaneously visualizing all the events in the catalog, our proposed technique can produce a naturally interpretable classification result for these 24 major earthquakes, by individually visualizing each set of the first to
k
-th child nodes with different colored markers plotted in the directly interpretable spatio and temporal metrics. As a consequence, we confirm that our approach based on multiple-parent selection is vital and promising.
Journal Article
Developing a course timetable system for academic departments using genetic algorithm
Preparing course timetables for universities is a search problem with many constraints. Exhaustive
search techniques in theory can be used to develop course timetables for academic departments, but
unfortunately these techniques are computation intensive, since the search space is very large and
therefore are impractical. In this paper, Genetic Algorithms (GA’s) are utilized to build an automated
course timetable system. The system is designed for any academic department. The proposed timetabling
system requires minimal effort from the administration staff to prepare the course timetable. Moreover,
the prepared course timetable considers faculties’ desires, students' needs and available resources, such
as classrooms and laboratories with optimal utilization.
The proposed timetabling process was divided into three stages. The first stage is the data collection
stage. In this stage, the administrative staff; usually the head of the department, is responsible for
preparing the required data, such as the names of the faculty personnel and their desires of courses and
laboratories ordered with some priority scheme. Number and type of theoretical and practical courses
are also fed to the system based on some statistics about student numbers and previous course timetable
history. The system is also fed with number of lecture rooms allocated for the department and number of
labs with information about theoretical courses they are able to serve. In the second stage, the program
generates an initial set of suggested schedules (chromosomes). Each chromosome represents a solution to
the problem, but usually is not satisfactory. Finally, the proposed timetabling system starts the search for
a good solution that satisfies best interests of the department according to a cost function. GA is applied
in search for a satisfactory course timetable based on a pre-defined criterion. The system has been
developed and tested utilizing benchmarked datasets developed by an international timetabling
competition (ITC2007) and for the Computer Engineering Department at Yarmouk University. In both
cases, the algorithm showed very satisfactory results.
Journal Article