The Role of Genetic Algorithm Selection Operators in Extending WSN Stability Period: A Comparative Study

A genetic algorithm (GA) contains a number of genetic operators that can be tweaked to improve the performance of specific implementations. Parent selection, crossover, and mutation are examples of these operators. One of the most important operations in GA is selection. The performance of GA in addressing the single-objective wireless sensor network stability period extension problem using various parent selection methods is evaluated and compared. In this paper, six GA selection operators are used: roulette wheel, linear rank, exponential rank, stochastic universal sampling, tournament, and truncation. According to the simulation results, the truncation selection operator is the most efficient operator in terms of extending the network stability period and improving reliability. The truncation operator outperforms other selection operators, most notably the well-known roulette wheel operator, by increasing the stability period by 25.8% and data throughput by 26.86%. Furthermore, the truncation selection operator outperforms other selection operators in terms of the network residual energy after each protocol round.

GiPS: Genomics-informed parent selection uncovers the breeding value of wheat genetic resources

The great efforts spent in the maintenance of past diversity in genebanks are rationalized by the potential role of plant genetic resources in future crop improvement: a concept whose practical implementation has fallen short of expectations. Here, we implement genomics-informed parent selection to expedite pre-breeding without discriminating against non-adapted germplasm. We collect dense genetic profiles for a large winter wheat collection and evaluate grain yield and resistance to yellow rust in representative coresets. Genomic prediction within and across genebanks identified the best parents for PGR x elite derived crosses that outyielded current elite cultivars in multiple field trials.

INFLUENCE OF PARENT SELECTION METHODS BY B ANTIGEN SIMILARITY INDEX IN THE BLOOD GROUPS ON WEIGHT AND LINEAR GROWTH OF BULLS

In solving the problem of qualitative improvement of beef breeds, it is important to improve the methods of practical use of existing parent couples selection methods, using histocompatibility antigens, polymorphic proteins and blood group systems. The purpose of the thesis is to determine the influence of homogeneous and heterogeneous selection of parent couples by blood group factors on the weight and linear growth of bulls of Ukrainian beef breed. The Ukrainian beef breed was bred using four breeds and is characterized by high variability in polymorphic features. The type of parent selection was determined by the index of B antigen similarity (ras) of cattle blood groups. The formula of D.A. Zhyvotovskyi and A.M. Mashurov was used to calculate the index of antigenic similarity of parents. Selection by ras of parents ≥ 0,268 was considered homogeneous, and by ras ≤ 0,267 heterogeneous. It has been proven that bulls that are descended from their parents with more ras pravail in the test on average daily gains and have a higher live weight. If ras of the parents is over 0.268, animals tend to improve their growth rate up to 8 months of age. After weaning this trend persists. The average daily gain of bulls obtained from parents with ras up to 0.267 is better in the period from 15 to 18 months, which indicates their lower precocity. If the antigenic similarity index of parents is more than 0.268, the animals are better in terms of the severity of meat forms at the age of 15 and 18 months. At the age of 15 months, bulls obtained from homogeneous selection by ras have smaller height measurements, better developed front of the torso in width and depth of the chest, longer torso and buttocks. Homogeneous selection of parental couples according to the B antigen similarity index of blood groups leads to improvement of weight growth and severity of meat forms of bulls of Ukrainian beef breed.

An Analysis of Selection in Genetic Programming

<p>This thesis presents an analysis of the selection process in tree-based Genetic Programming (GP), covering the optimisation of both parent and offspring selection, and provides a detailed understanding of selection and guidance on how to improve GP search effectively and efficiently. The first part of the thesis providesmodels and visualisations to analyse selection behaviour in standard tournament selection, clarifies several issues in standard tournament selection, and presents a novel solution to automatically and dynamically optimise parent selection pressure. The fitness evaluation cost of parent selection is then addressed and some cost-saving algorithms introduced. In addition, the feasibility of using good predecessor programs to increase parent selection efficiency is analysed. The second part of the thesis analyses the impact of offspring selection pressure on the overall GP search performance. The fitness evaluation cost of offspring selection is then addressed, with investigation of some heuristics to efficiently locate good offspring by constraining crossover point selection structurally through the analysis of the characteristics of good crossover events. The main outcomes of the thesis are three new algorithms and four observations: 1) a clustering tournament selection method is developed to automatically and dynamically tune parent selection pressure; 2) a passive evaluation algorithm is introduced for reducing parent fitness evaluation cost for standard tournament selection using small tournament sizes; 3) a heuristic population clustering algorithm is developed to reduce parent fitness evaluation cost while taking advantage of clustering tournament selection and avoiding the tournament size limitation; 4) population size has little impact on parent selection pressure thus the tournament size configuration is independent of population size; and different sampling replacement strategies have little impact on the selection behaviour in standard tournament selection; 5) premature convergence occurs more often when stochastic elements are removed from both parent and offspring selection processes; 6) good crossover events have a strong preference for whole program trees, and (less strongly) single-node or small subtrees that are at the bottom of parent program trees; 7) the ability of standard GP crossover to generate good offspring is far below what was expected.</p>

An Analysis of Selection in Genetic Programming

<p>This thesis presents an analysis of the selection process in tree-based Genetic Programming (GP), covering the optimisation of both parent and offspring selection, and provides a detailed understanding of selection and guidance on how to improve GP search effectively and efficiently. The first part of the thesis providesmodels and visualisations to analyse selection behaviour in standard tournament selection, clarifies several issues in standard tournament selection, and presents a novel solution to automatically and dynamically optimise parent selection pressure. The fitness evaluation cost of parent selection is then addressed and some cost-saving algorithms introduced. In addition, the feasibility of using good predecessor programs to increase parent selection efficiency is analysed. The second part of the thesis analyses the impact of offspring selection pressure on the overall GP search performance. The fitness evaluation cost of offspring selection is then addressed, with investigation of some heuristics to efficiently locate good offspring by constraining crossover point selection structurally through the analysis of the characteristics of good crossover events. The main outcomes of the thesis are three new algorithms and four observations: 1) a clustering tournament selection method is developed to automatically and dynamically tune parent selection pressure; 2) a passive evaluation algorithm is introduced for reducing parent fitness evaluation cost for standard tournament selection using small tournament sizes; 3) a heuristic population clustering algorithm is developed to reduce parent fitness evaluation cost while taking advantage of clustering tournament selection and avoiding the tournament size limitation; 4) population size has little impact on parent selection pressure thus the tournament size configuration is independent of population size; and different sampling replacement strategies have little impact on the selection behaviour in standard tournament selection; 5) premature convergence occurs more often when stochastic elements are removed from both parent and offspring selection processes; 6) good crossover events have a strong preference for whole program trees, and (less strongly) single-node or small subtrees that are at the bottom of parent program trees; 7) the ability of standard GP crossover to generate good offspring is far below what was expected.</p>

A Trust-Integrated RPL Protocol to Detect Blackhole Attack in Internet of Things

Internet of things (IoT) offers communication between user-to-machine and machine-to-machine. Due to their inherent characteristics of open medium, very dynamic topology, lack of infrastructure and lack of centralized management authority, IoT present serious vulnerabilities to security attacks. The routing protocol for low-power and lossy networks (RPL) does not have an inherent mechanism to detect routing attacks. Popular among these IoT attacks is blackhole attack. An attacker can exploit the routing system of RPL to launch blackhole attack against an IoT network. To secure IoT networks from blackhole attack, trust-integrated RPL protocol (TRPL) is proposed and implemented. The trust system is embedded in the RPL protocol to detect and isolate a blackhole attack while optimizing network performance. The trust is calculated from successful interaction between two nodes. The calculated trust value is considered in parent selection. TRPL demonstrates its superior performance over the standard RPL protocol and existing techniques in the detection and isolation of blackhole attacks.

Combining Ability and Molecular Marker Approach Identified Genetic Resources to Improve Agronomic Performance in Coffea arabica Breeding

Plant breeding aims to develop cultivars with good agronomic traits through gene recombination and elite genotype selection. To support Coffea arabica breeding programs and assist parent selection, molecular characterization, genetic diversity (GD) analyses, and circulating diallel studies were strategically integrated to develop new cultivars. Molecular markers were used to assess the GD of 76 candidate parents and verify the crossing of potential F1 hybrids. Based on the complementary agronomic traits and genetic distance, eight elite parents were selected for circulating diallel analysis. The parents and 12 hybrids were evaluated based on 10 morpho-agronomic traits. For each trait, the effects of general and specific combining abilities, as well as the averages of the parents, hybrids, and predicted hybrids, were estimated. Crosses that maximize the genetic gains for the main agronomic traits of C. arabica were identified. Joint analysis of phenotypic and molecular data was used to estimate the correlation between molecular GD, phenotypic diversity (PD), phenotypic mean, and combining ability. The selection of parents that optimize the allele combination for the important traits of C. arabica is discussed in detail.