Parent Selection Scheme Research Articles

B × R cross de rived parental line development using breeding value: A new model for hybrid rice parental line development

Three-line hybrid rice system is the most successful and widely practiced method around the world. Hybrid rice breeders have used B × B, A × R and R × R (R = Restorer line, B = Maintainer line, A = CMS line) scheme of parental line improvement frequently and avoided B × R and R × B scheme. As a result, female parents lack the genetic diversity carried by R lines. But B × R and R × B mating have great potential to produce high value parental lines of hybrid rice and overcome the limitation of the previous approach. We have demonstrated a new method for three-line hybrid system to minimize the barrier of crossing in parent selection for developing new elite maintainers and restorers. Parental combinations were selected based on breeding value of the genotypes. Breeding values were estimated based on ancestor, pedigree information and yield data of 74 test genotype to select parents for restorer and maintainer line improvement. This new protocol allows (B × R), (R × B) (R × Elite) and (B × Elite) improvement technique to bring out high yielding diverse B and R lines. This B line will be used for developing new A line in the genetic background of B line. Doubled haploid and RGA i.e. rapid generation advance tools of breeding will save the precious time and reduce breeding cycle length; and large population size will increase selection accuracy. We have predicted the genetic gain in parental line development for four parental cross using the studied 74 genotypes for doubled haploid and rapid generation advance methods. Our objectives were to demonstrate the new breeding approach plus breeding value and positive dominant gene effect-based parent selection strategy. We are hopeful about the new method that hybrid rice breeders across the world will extract benefit utilizing the new methodology of hybrid rice parental line development.

A novel parent selection strategy for the development of salt-tolerant cotton cultivars

AbstractSalinity poses a major obstacle in increasing the yield of cotton. To explore genetic material that can yield better under salt stress conditions, eight parents including 5 females and 3 testers were crossed in line × tester mating design. After successful completion of crossing, parents and their 15 crosses were evaluated for seed cotton yield, within boll yield components, fibre quality, ionic and biochemical traits under control and NaCl salt stressed conditions (10 and 20 dSm−1). Under salt stress conditions seed cotton yield, fibre length and fibre strength decreased in all genotypes whereas, lint percentage and fibre fineness increased. Among parents RH-647 and among crosses FH-214 × FH-2015 performed better for seed cotton yield while for fibre quality traits under salt stress conditions among parents KEHKSHAN, and among crosses FH-214 × KEHKSHAN performed better. Results suggested that plant height, boll weight, lint percentage, fibre length and fibre strength are reliable traits for the selection of salt tolerant genotypes in the future cotton breeding programs.

Avoiding excess computation in asynchronous evolutionary algorithms

AbstractAsynchronous evolutionary algorithms are becoming increasingly popular as a means of making full use of many processors while solving computationally expensive search and optimization problems. These algorithms excel at keeping large clusters fully utilized, but may sometimes inefficiently sample an excess of fast‐evaluating solutions at the expense of higher‐quality, slow‐evaluating ones. We have previously introduced a steady‐state parent selection strategy, SWEET (“Selection whilE EvaluaTing”), that sometimes selects individuals that are still being evaluated and allows them to reproduce early. We perform a takeover‐time analysis that confirms that this strategy gives slow‐evaluating individuals that have higher fitnesses an increased ability to multiply in the population. We also find that SWEET appears effective at improving optimization performance on problems in which solution quality is positively correlated with evaluation time. We evaluate our approach on six simulated real‐valued optimization problems and three real‐world applications: an autonomous vehicle controller problem that involves tuning a spiking neural network and two adversarial EA problems. We further evaluate SWEET versus a basic asynchronous process in a simulated setting. We present evidence that SWEET outperforms basic asynchronous processes in a use‐case in which performance is positively correlated with evaluation time, and performs comparably (and often better) than basic asynchronous processes in several use‐cases where performance is negatively correlated with evaluation time. That said, in the cases where performance and evaluation time are negatively correlated the variance of outcomes for SWEET is notably high.

An efficient chaos-LSA integrated game theory algorithm for a QoS-assured delay time control mechanism with a unique parent selection strategy for a 6LOWPAN wireless body area network

An efficient chaos-LSA integrated game theory algorithm for a QoS-assured delay time control mechanism with a unique parent selection strategy for a 6LOWPAN wireless body area network

A novel parent selection strategy for the development of drought-tolerant cotton cultivars

AbstractDrought is a devastating factor for crop production worldwide. Therefore, an experiment was conducted to study genetics for some agro-physiological traits in cotton under drought stress. The 13 parental cotton genotypes along with their 30 F1 hybrids were planted under normal and drought conditions. The mean performance of the genotypes was assessed through principal component and heat map analyses. The principal component analyses revealed 53.99 and 53.15% in the first two principal components of variability for normal and drought conditions, respectively. Heat map analysis revealed that three cotton genotypes i.e. FH-207 × NS-131, FH-207 × KZ-191 and S-15 × AA-703 attained higher values for all the traits except for canopy temperature under drought conditions. These crosses may proliferate to further filial generations to identify transgressive segregates for drought tolerance. The heritable differences of F̅1 and mid-parent showed dominance and non-additive gene action under drought conditions. Heritable differences between F̅1 and P̅1 showed over dominance and partial dominance under drought conditions. Heritable differences between F̅1 and P̅2 indicated negative over dominance and partial dominance for all traits under drought conditions. Proline contents and the bolls per plant showed high heritability and genetic advance through additive gene action. Therefore, these two traits can be used as a means of selection in future breeding programmes of drought tolerance.

Empowered Hybrid Parent Selection for Improving Network Lifetime, PDR, and Latency in Smart Grid

To support the constraints of smart meters—low power and memory—of AMI network, RPL is considered as the most suitable routing protocol to be implemented in practice. Network lifetime, PDR, and latency are the critical issues to be focused on and addressed. Generally, single parent selection scheme cannot satisfy all expected performance requirements of RPL based on AMI network due to tradeoff between workload balancing and transmission performance, PDR and latency. Moreover, the single parent also suffers from the package size and transmission range. Then, multiparent solution is proposed to overcome these demerits using multipath transmission strategy. Although the existing multiparent solutions, MELT and MAHP, overcome the issue of transmission performance, they present low network lifetime since multiparent solution consumes high energy in data transmission. In this paper, we propose an “empowered hybrid parent selection (EHPS)” that exploits the merits of multiparent solution and the single parent with cognitive radio technology in a hybridizing scheme. To split the data packet efficiently under multipath transmission strategy, a fuzzy AHP (FAHP) is adopted; therefore, EHPS balances the workload effectively and maximizes the network lifetime over long transmission range and large data size. Moreover, by exploiting cognitive radio, EHPS is flexible to the transmission range and data size since it achieves the highest transmission performance, highest PDR, and lowest latency among others, while maintaining high network lifetime.

Resource oriented topology construction to ensure high reliability in IoT based smart city networks

Today, advanced metering infrastructure (AMI) is a subject that is undergoing intense study in smart city perspective, where the quality of service is the main concern. Hence, data routing is a key challenge in such a network. So far, the routing protocol for low power and lossy network (RPL) is considered to fit on AMI structure for achieving the expected network requirements. However, it is not optimized, as its parent selection mechanism attracts a large number of nodes and constructs the unbalance topology that degrades the network reliability. To overcome this problem, in this paper, we propose an efficient parent selection scheme that avoids the overburdened parent. The scheme combines the multiple routing metrics to calculate the rank, and that results in the balanced topology formation, which ensures the highest reliability in the RPL based AMI network. We evaluate the proposed work by implementing in Contiki OS with Cooja simulator. The simulation results show that the proposed work provides a higher packet delivery ratio, a lower latency, and a higher throughput as compared to default RPL.

An improved class of real-coded Genetic Algorithms for numerical optimization✰

Over the last few decades, many improved Evolutionary Algorithms (EAs) have been proposed to tackle different types of optimization problems. Genetic Algorithm (GA) among other canonical algorithms have not shown consistent performance over a range of different optimization problems with complex characteristics. In this paper, an improved class of real-coded Genetic Algorithm is introduced to solve complex optimization problems. The first algorithm, Genetic Algorithm embedded with a new Differential Evolution crossover, GA–DEx, proposes a new variant of Differential Evolution mutation which is used as a new multi-parent crossover in Genetic Algorithms. The main purpose of this algorithm is to enhance the search ability of the GA algorithm by combining a new Differential Evolution crossover with a GA algorithm to avoid premature convergence and stagnation scenarios by exploring more solutions in the problem search space. The second amalgam algorithm, GA–DExSPS, uses an effective and efficient successful parent selection strategy to provide a successful alternative for the selection of parents during the Differential Evolution crossover process. This strategy improves the performance of first introduced algorithm by selecting more promising parents to guide the evolutionary search. The third algorithm, GA–aDExSPS, introduces an aging mechanism and a success-history-based adaptive Genetic Algorithm. This algorithm adapts the alpha parameter used by Differential Evolution crossover in a history-based adaptive manner. This adaptation helps the search to discover more promising regions and to prevent stagnation and premature convergence scenarios. To verify the performance of our class, a challenging test suite of 30 benchmark functions from the IEEE CEC2014 real parameter single objective competition is used. The results affirm the effectiveness and robustness of the proposed algorithms compared to other state-of-the-art well-known crossovers and recent Genetic Algorithms variants.

A novel hybrid multi-objective immune algorithm with adaptive differential evolution

In this paper, we propose a novel hybrid multi-objective immune algorithm with adaptive differential evolution, named ADE-MOIA, in which the introduction of differential evolution (DE) into multi-objective immune algorithm (MOIA) combines their respective advantages and thus enhances the robustness to solve various kinds of MOPs. In ADE-MOIA, in order to effectively cooperate DE with MOIA, we present a novel adaptive DE operator, which includes a suitable parent selection strategy and a novel adaptive parameter control approach. When performing DE operation, two parents are respectively picked from the current evolved and dominated population in order to provide a correct evolutionary direction. Moreover, based on the evolutionary progress and the success rate of offspring, the crossover rate and scaling factor in DE operator are adaptively varied for each individual. The proposed adaptive DE operator is able to improve both of the convergence speed and population diversity, which are validated by the experimental studies. When comparing ADE-MOIA with several nature-inspired heuristic algorithms, such as NSGA-II, SPEA2, AbYSS, MOEA/D-DE, MIMO and D2MOPSO, simulations show that ADE-MOIA performs better on most of 21 well-known benchmark problems.

Application specific instance generator and a memetic algorithm for capacitated arc routing problems

Capacitated arc routing problem (CARP) is a well known combinatorial problem that requires identifying minimum total distance traveled by a fleet of vehicles in order to serve a set of roads without violating the vehicles’ capacity constraints. A number of optimization algorithms have been proposed over the years to solve basic CARPs and their performance have been analyzed using selected benchmark suites available in literature. From an application point of view, there is a need to assess the performance of algorithms on specific class of instances that resemble realistic applications, e.g., inspection of electric power lines, garbage collection, winter gritting etc. In this paper we introduce a benchmark generator that controls the size and complexity of the underlying road network resembling a target application. It allows generation of road networks with multiple lanes, one-way/two-way roads and varying degree of connectedness. Furthermore, an algorithm capable of solving real life CARP instances efficiently within a fixed computational budget of evaluations is introduced. The proposed algorithm, referred to as MA-CARP, is a memetic algorithm embedded with a similarity based parent selection scheme inspired by multiple sequence alignment, hybrid crossovers and a modified neighborhood search to improve its rate of convergence. The mechanism of test instance generation is presented for three typical scenarios, namely, inspection of electric power lines, garbage collection and winter gritting. The code for the generator is available from http://seit.unsw.adfa.edu.au/research/sites/mdo/Research-Data/InstanceGenerator.rar. The performance of the algorithm is compared with a state-of-the-art algorithm for three generated benchmarks. The results obtained using the proposed algorithm are better for all the above instances clearly highlighting its potential for solving CARP problems.

Alpha Cut based Novel Selection for Genetic Algorithm

Genetic algorithm (GA) has several genetic operators that can be changed to improve the performance of particular implementations. These operators include selection, crossover and mutation. Selection is one of the important operations in the GA process. There are several ways for selection like Roulette-Wheel, Rank, and Tournament etc. This paper presents a new selection operator based on alpha cut as in Fuzzy Logic. This is compared with other selection in solving travelling salesman problem (TSP) using different parent selection strategy. Several TSP instances were tested and the results show that proposed selection outperformed proportional roulette wheel, achieving best solution quality with low computing times.

Time-Shifted Streaming in a Tree-Based Peer-to-Peer System

We propose a peer-to-peer (P2P) streaming system that multicasts live video as well as provides the live video as Video-on-Demand (VoD) during the live session. The proposed system allows users to individually pause and resume a live video stream, and to view video that was streamed before the particular user joined the session. Since live video content naturally results in many users watching it online at the same time, the P2P concept is leveraged to reduce server load. We extend our Stanford Peer-to-Peer Multicast (SPPM) protocol, originally designed for live video multicast, to support playback control. To achieve this in a P2P fashion, peers store received video packets in their local buffer and forward them at a later time when requested by other peers, which is called time-shifted streaming. To understand interaction between live and time-shifted traffic, we analyze video availability of the proposed system, which refers to how many peers possess video contents of a particular position. Motivated by the analysis, we propose fast prefetching and a parent selection scheme suitable for fast prefetching in order to further reduce server load by disseminating video quickly. Fast prefetching is possible because the relaxed transfer deadline of time-shifted streams and peers’ extra uplink bandwidths are exploited. With simulations and mathematical analysis, it is shown that fast prefetching not only alleviates the requirement of server bandwidth by expediting video dissemination, but also mitigates video disruption due to peer churn.

An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization

Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this paper, we propose a new mutation strategy, a fitness-induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of achieving improved performance. The new mutation operator, which we call DE/current-to-gr_best/1, is a variant of the classical DE/current-to-best/1 scheme. It uses the best of a group (whose size is q% of the population size) of randomly selected solutions from current generation to perturb the parent (target) vector, unlike DE/current-to-best/1 that always picks the best vector of the entire population to perturb the target vector. In our modified framework of recombination, a biased parent selection scheme has been incorporated by letting each mutant undergo the usual binomial crossover with one of the p top-ranked individuals from the current population and not with the target vector with the same index as used in all variants of DE. A DE variant obtained by integrating the proposed mutation, crossover, and parameter adaptation strategies with the classical DE framework (developed in 1995) is compared with two classical and four state-of-the-art adaptive DE variants over 25 standard numerical benchmarks taken from the IEEE Congress on Evolutionary Computation 2005 competition and special session on real parameter optimization. Our comparative study indicates that the proposed schemes improve the performance of DE by a large magnitude such that it becomes capable of enjoying statistical superiority over the state-of-the-art DE variants for a wide variety of test problems. Finally, we experimentally demonstrate that, if one or more of our proposed strategies are integrated with existing powerful DE variants such as jDE and JADE, their performances can also be enhanced.

Computing Localized Power-Efficient Data Aggregation Trees for Sensor Networks

We propose localized, self organizing, robust, and energy-efficient data aggregation tree approaches for sensor networks, which we call Localized Power-Efficient Data Aggregation Protocols (L-PEDAPs). They are based on topologies, such as LMST and RNG, that can approximate minimum spanning tree and can be efficiently computed using only position or distance information of one-hop neighbors. The actual routing tree is constructed over these topologies. We also consider different parent selection strategies while constructing a routing tree. We compare each topology and parent selection strategy and conclude that the best among them is the shortest path strategy over LMST structure. Our solution also involves route maintenance procedures that will be executed when a sensor node fails or a new node is added to the network. The proposed solution is also adapted to consider the remaining power levels of nodes in order to increase the network lifetime. Our simulation results show that by using our power-aware localized approach, we can almost have the same performance of a centralized solution in terms of network lifetime, and close to 90 percent of an upper bound derived here.

ＵＶ構造を考慮した適応的複製選択による実数値ＧＡの提案

The purpose of this paper is to propose a new real-coded genetic algorithm (RCGA) named Networked Genetic Algorithm (NGA) that intends to find multiple optima simultaneously in deceptive globally multimodal landscapes. Most current techniques such as niching for finding multiple optima take into account big valley landscapes or non-deceptive globally multimodal landscapes but not deceptive ones called UV-landscapes. Adaptive Neighboring Search (ANS) is a promising approach for finding multiple optima in UV-landscapes. ANS utilizes a restricted mating scheme with a crossover-like mutation in order to find optima in deceptive globally multimodal landscapes. However, ANS has a fundamental problem that it does not find all the optima simultaneously in many cases. NGA overcomes the problem by an adaptive parent-selection scheme and an improved crossover-like mutation. We show the effectiveness of NGA over ANS in terms of the number of detected optima in a single run on Fletcher and Powell functions as benchmark problems that are known to have multiple optima, ill-scaledness, and UV-landscapes.

A population ecology inspired parent selection strategy for numerical constrained optimization problems

A population ecology inspired parent selection strategy is proposed to improve the searching ability of evolutionary algorithms for numerical constrained optimization problems. This method is mainly used to help find an appropriate number of feasible parents for offspring generation. Based on the similar phenomenon in population ecology, the number of feasible parents has a sigmoid-type relationship with that of the feasible individuals. To implement the novel parent selection strategy, the population is divided into two groups according to the feasibility of the individuals: the feasible group and infeasible group. The evaluation and ranking of these two groups are performed separately. The dynamic penalty method, annealing penalty method and stochastic ranking method are tested with the parent selection strategy on 13 benchmark problems. The results show that the proposed method is capable of improving the searching performance.