Question type and answer related keywords aware question generation

The traditional end-to-end Neural Question Generation (NQG) models tend to generate generic and bland questions, as there are two obscure points: 1) the modifications of the answer in the context can be used as the clues to the answer mentioned in the question, while they are generally not unique and can be used independently for generating diverse questions; 2) the same question content can also be asked in diverse ways, which depends on personal preference in practice. The above-mentioned two points are indeed two variables to conduct question generation, but they are not annotated in the original dataset and are thus ignored by the traditional end-to-end models. In this paper we propose a framework that clarifies those two points through two sub-modules to better conduct question generation. We take experiments based on the GPT-2 model and the SQuAD dataset, and prove that our framework can improve the performance measured by similarity metrics, while it also provides appropriate alternatives for controllable diversity enhancement.

A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization

<p>Dynamic multi-objective optimization problems (DMOPs) and Many-Objective Optimization Problems (MaOPs) are two classes of the optimization filed which have potential applications in engineering. Modified Multi-Objective Evolutionary Algorithms hybrid approaches seem to be suitable to effectively deal with such problems. However, the Crow Search Algorithm has not yet considered for both DMOP and MaOP. This paper proposes a Distributed <a>Bi-behaviors </a>Crow Search Algorithm (DB-CSA) with two different mechanisms, one corresponding to the search behavior and another to the exploitative behavior with a dynamic switch mechanism. The bi-behaviors CSA chasing profile is defined based on a large Gaussian-like Beta-1 function which ensures diversity enhancement, while the narrow Gaussian Beta-2 function is used to improve the solution tuning and convergence behavior. The DB-CSA approach is developed to solve several types of DMOPs and a set of MaOPs with 2, 3, 5, 7, 8, 10 and 15 objectives. The Inverted General Distance, the Mean Inverted General Distance and the Hypervolume Difference are the main measurement metrics are used to compare the DB-CSA approach to the state-of-the-art MOEAs. All quantitative results are analyzed using the nonparametric Wilcoxon signed rank test with 0.05 significance level which proving the efficiency of the proposed method for solving both 44 DMOPs and MaOPs utilized. </p>

A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization

<p>Dynamic multi-objective optimization problems (DMOPs) and Many-Objective Optimization Problems (MaOPs) are two classes of the optimization filed which have potential applications in engineering. Modified Multi-Objective Evolutionary Algorithms hybrid approaches seem to be suitable to effectively deal with such problems. However, the Crow Search Algorithm has not yet considered for both DMOP and MaOP. This paper proposes a Distributed <a>Bi-behaviors </a>Crow Search Algorithm (DB-CSA) with two different mechanisms, one corresponding to the search behavior and another to the exploitative behavior with a dynamic switch mechanism. The bi-behaviors CSA chasing profile is defined based on a large Gaussian-like Beta-1 function which ensures diversity enhancement, while the narrow Gaussian Beta-2 function is used to improve the solution tuning and convergence behavior. The DB-CSA approach is developed to solve several types of DMOPs and a set of MaOPs with 2, 3, 5, 7, 8, 10 and 15 objectives. The Inverted General Distance, the Mean Inverted General Distance and the Hypervolume Difference are the main measurement metrics are used to compare the DB-CSA approach to the state-of-the-art MOEAs. All quantitative results are analyzed using the nonparametric Wilcoxon signed rank test with 0.05 significance level which proving the efficiency of the proposed method for solving both 44 DMOPs and MaOPs utilized. </p>

A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization

<p>Dynamic multi-objective optimization problems (DMOPs) and Many-Objective Optimization Problems (MaOPs) are two classes of the optimization filed which have potential applications in engineering. Modified Multi-Objective Evolutionary Algorithms hybrid approaches seem to be suitable to effectively deal with such problems. However, the Crow Search Algorithm has not yet considered for both DMOP and MaOP. This paper proposes a Distributed <a>Bi-behaviors </a>Crow Search Algorithm (DB-CSA) with two different mechanisms, one corresponding to the search behavior and another to the exploitative behavior with a dynamic switch mechanism. The bi-behaviors CSA chasing profile is defined based on a large Gaussian-like Beta-1 function which ensures diversity enhancement, while the narrow Gaussian Beta-2 function is used to improve the solution tuning and convergence behavior. The DB-CSA approach is developed to solve several types of DMOPs and a set of MaOPs with 2, 3, 5, 7, 8, 10 and 15 objectives. The Inverted General Distance, the Mean Inverted General Distance and the Hypervolume Difference are the main measurement metrics are used to compare the DB-CSA approach to the state-of-the-art MOEAs. All quantitative results are analyzed using the nonparametric Wilcoxon signed rank test with 0.05 significance level which proving the efficiency of the proposed method for solving both 44 DMOPs and MaOPs utilized. </p>

A responsive ant colony optimization for large-scale dynamic vehicle routing problems via pheromone diversity enhancement

Large-scale dynamic vehicle routing problem (LSDVRP) is exhibiting extensive application prospect with the rapid growth of online logistics, whereas a few approaches have been developed to address LSDVRPs. The difficulty in solving LSDVRPs lies in that it requires quick response and high adaptability to numerous newly appeared customers in LSDVRPs. To overcome this difficulty, in this paper, we propose a responsive ant colony optimization algorithm, termed as RACO, for efficiently addressing LSDVRPs. In the proposed RACO, a pheromone diversity enhancing method is suggested to generate diverse pheromone matrices for quickly responding to newly appeared customer requests in solving LSDVRPs. A pheromone ensemble technique is further designed to produce a high-quality initial population that well adapts to the new customer requests by making use of diverse pheromone matrices. Empirical results on a set of 12 LSDVRP test instances demonstrate the effectiveness of the suggested pheromone diversity enhancing method in quickly responding to newly appeared customer requests for solving LSDVRPs. Moreover, we investigate the computational cost and the traveling cost obtained by the proposed RACO to evaluate responsiveness and adaptability of the proposed RACO, respectively. Comparison with four state-of-the-art approaches to DVRPs validates the superiority of the proposed RACO in addressing LSDVRPs in terms of responsiveness and adaptability.

Progress of mutant resource development and tilling on starch biosynthesis in wheat.

Induced mutations have been widely utilized for the development of plant mutant germplasm and varieties since 1927 and have contributed to genetic diversity enhancement and food security in the world. Mutant resources are essential for gene identification and functional characterization by forward and reverse genetic strategies. The publishing of annotated wheat reference genomes is greatly promoting the progress of wheat functional genomic research. Mutant resources of a broad spectrum and diversified wild- types will be the prerequisites in this process, in part due to the polyploid nature of wheat. This review describes the progress of mutant resource development derived from the winter wheat cultivar 'Jing411'. The segregating M2 population has been used for mining functional mutant alleles of key genes involved in starch biosynthesis and could be further used for allele mining of any other target genes. The morphological mutant resources developed from various mutagens have been, and are going to be, used to develop genetic populations for gene mapping and the genetic analysis of biological functions.