Strategy and Applied Research of Multi-Constrained Model of Automatic Test Paper Based on Genetic Algorithm

2010 ◽  
Vol 37-38 ◽  
pp. 1223-1230 ◽  
Author(s):  
Zhi Feng Liu ◽  
Ji Shi ◽  
Jia Liu ◽  
Yang Li
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Applied Research ◽  
Genetic Manipulation ◽  
Initial Population ◽  
Test Paper ◽  
Examination System ◽  
Automatic Test ◽  
Online Examination ◽  
Constrained Model

Test paper problem is a typical multi-constrained objective optimization problem. By using genetic algorithm, this paper analyzes the initial population generation, the chromosome coding and its genetic manipulation, control parameters. Solving that by natural-coded genetic algorithm, improves test paper success rate and convergence rate. This genetic algorithm is applied successfully on NHibernate architecture, and developed "automatic test paper" Online Examination system.

Strategy for Test Paper Composition Based on Genetic Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 1688-1691
Author(s):  
Hong Tao Tang
Keyword(s):  
Genetic Algorithm ◽  
Computer Network ◽  
Rapid Development ◽  
Adaptive Genetic Algorithm ◽  
Test Paper ◽  
Examination System ◽  
Coding Schemes ◽  
Online Test ◽  
Online Examination ◽  
Elitist Strategy

Recently, with rapid development of computer/network technology and algorithms for composing test paper, cyber-based online examination system is a practically valuable hot research concern. In the paper, the mathematical model is created for solving problems with the online test paper composition system. Through comparative analysis of merits and shortcomings of various coding schemes, and to overcome the shortcoming that traditional genetic algorithms easily fall into premature convergence, it utilizes the adaptive adjustment method of dynamic parameters and elitist strategy to improve to develop the online test paper forming scheme based on adaptive genetic algorithm. For the selection of each parameter, simulation test is conducted to obtain the solution approximate to the best one.

Solving the 0/1 knapsack problem using an adaptive genetic algorithm

2002 ◽  
Vol 16 (1) ◽  
pp. 23-30 ◽  
Author(s):  
ZOHEIR EZZIANE
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Adaptive Genetic Algorithm ◽  
Stochastic Algorithms ◽  
Initial Population ◽  
Genetic Operators ◽  
Adaptive Penalty ◽  
Genetic Algorithm Approach

Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.

scholarly journals An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Yong Deng ◽  
Yang Liu ◽  
Deyun Zhou
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Traveling Salesman ◽  
Average Error ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Population Strategy ◽  
Series Of Experiments

A new initial population strategy has been developed to improve the genetic algorithm for solving the well-known combinatorial optimization problem, traveling salesman problem. Based on thek-means algorithm, we propose a strategy to restructure the traveling route by reconnecting each cluster. The clusters, which randomly disconnect a link to connect its neighbors, have been ranked in advance according to the distance among cluster centers, so that the initial population can be composed of the random traveling routes. This process isk-means initial population strategy. To test the performance of our strategy, a series of experiments on 14 different TSP examples selected from TSPLIB have been carried out. The results show that KIP can decrease best error value of random initial population strategy and greedy initial population strategy with the ratio of approximately between 29.15% and 37.87%, average error value between 25.16% and 34.39% in the same running time.

Optimal Sensor Placement Based on MAC and SPGA Algorithms

2012 ◽  
Vol 594-597 ◽  
pp. 1118-1122 ◽  
Author(s):  
Yong Ming Fu ◽  
Ling Yu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Manipulation ◽  
Optimization Method ◽  
Frame Structure ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Modal Assurance Criterion ◽  
Sensor Optimization ◽  
Computation Efficiency ◽  
The Individual

In order to solve the problem on sensor optimization placement in the structural health monitoring (SHM) field, a new sensor optimization method is proposed based on the modal assurance criterion (MAC) and the single parenthood genetic algorithm (SPGA). First, the required sensor numbers are obtained by using the step accumulating method. The SPGA is used to place sensors, in which the binary coding is adopted to realize the genetic manipulation through gene exchange, gene shift and gene inversion. Then, the method is further simplified and improved for higher computation efficiency. Where, neither the individual diversity of initial population nor the immature convergence problem is required. Finally, a numerical example of 61 truss frame structure is used to assess the robustness of the proposed method. The illustrated results show that the new method is better than the improved genetic algorithm and the step accumulating method in the search capacity, computational efficiency and reliability.

Research on Random Algorithm of English Test Paper Composition

2014 ◽  
Vol 1049-1050 ◽  
pp. 1502-1505
Author(s):  
Wei Wei Qu
Keyword(s):  
Language Learning ◽  
English Language ◽  
Random Number Generation ◽  
Recursive Formula ◽  
Flow Diagram ◽  
Test Paper ◽  
Examination System ◽  
Learning And Teaching ◽  
Online Examination ◽  
Random Algorithm

English, as one of the most important information carriers, has become the most widely used language worldwide, and its importance is increasingly prominent in each field of our society. Examinations are an important means of evaluating the level of English language learning and teaching outcomes. For the problems existed in traditional English exam, this paper research English online examination system intelligent test paper based on random algorithm. First, the design test paper constraints, including the question amount constraint, questions type constraints, knowledge point constraints, difficulty constraints, answer time constraints and so on; Then, using the linear congruence generator study of a random number generation, list recursive formula and describe parameters; Finally, using flow diagram to study random test paper process, for each step describing in detail. Advantages of random algorithm are easy to use, the disadvantage is not retrospective, applies to the number of exam papers and extraction requirements are relatively few cases.

Heuristic Genetic Test Paper Algorithm for Online Examination System

2013 ◽  
Vol 798-799 ◽  
pp. 676-679
Author(s):  
Feng Yu ◽  
Wei Liu ◽  
Ming Cui
Keyword(s):  
Information Management ◽  
Genetic Test ◽  
Experimental Results ◽  
Test Paper ◽  
Examination System ◽  
Internet Applications ◽  
Satisfactory Performance ◽  
Online Examination ◽  
Improved Algorithm

Online examination system is built on internet applications,aimed at achieving information management of examinations. Online examination system in the generation of test papers, the submission and approval can be done automatically on the network. We proposes a new heuristic genetic test paper algorithm, and the proposed algorithm is applied to the online examination system. From the experimental results,the improved algorithm has achieved satisfactory performance.

scholarly journals The Application of Genetic Algorithm and an Evaluation Algorithm in Online Examination System

2020 ◽  
Vol 5 (9) ◽  
pp. 356-362
Author(s):  
Yang Yue, Mingbo Zhao
Keyword(s):  
Genetic Algorithm ◽  
Significant Role ◽  
Examination System ◽  
Online Examination ◽  
Evaluation Algorithm

Online examination system plays a significant role in education. However, there are varieties of disadvantages in non-optimized systems, such as randomly selecting questions that make the exam paper has an imbalance difficulty, the unanticipated weight of knowledge points, and so on. A genetic algorithm is an efficient and achievable way to improve the ability to generate exam paper. Besides, a massive amount of data are generated when the system is running. Nevertheless, some of the systems only store the data, in another word, they do not make full use of the generated data. An evaluation algorithm is put forward in this essay to give objective and scientific evaluations on students’ learning and teachers’ teaching via using the data that are generated in examinations, which is based on the degree of difficulty. To make this algorithm working well, the degree of difficulty of questions stored in the database is supposed to be updated dynamically when the samples of questions’ answers become large enough.

Some Preliminary Results on the Development and Comparison of a New Multi-Objective Genetic Algorithm

2001 ◽  
Author(s):  
A. Farhang-Mehr ◽  
J. Wu ◽  
S. Azarm
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Quality Metrics ◽  
Initial Population ◽  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Preliminary Results ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Speed Reducer

Abstract Some preliminary results for a new multi-objective genetic algorithm (MOGA) are presented. This new algorithm aims at obtaining the fullest possible representation of observed Pareto solutions to a multi-objective optimization problem. The algorithm, hereafter called entropy-based MOGA (or E-MOGA), is based on an application of the concepts from the statistical theory of gases to a MOGA. A few set quality metrics are introduced and used for a comparison of the E-MOGA to a previously published MOGA. Due to the stochastic nature of the MOGA, confidence intervals with a 95% confidence level are calculated for the quality metrics based on the randomness in the initial population. An engineering example, namely the design of a speed reducer is used to demonstrate the performance of E-MOGA when compared to the previous MOGA.

Test Paper Composition Design on College English Examination Based on Genetic Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 4585-4588
Author(s):  
Cong Yan
Keyword(s):  
Genetic Algorithm ◽  
English Education ◽  
Fitness Function ◽  
Basic Research ◽  
Initial Population ◽  
Test Paper ◽  
Design Algorithm ◽  
College English ◽  
Composition Design ◽  
Organization And Management

College English examination is an important part of university English education, for the difficult problems in the process of examination organization and management, in this paper, the intelligent test paper composition research based on genetic algorithm. First, basic research, including the basic idea of genetic algorithm, algorithm flow and perform operations on groups; then, studies test paper composition model, the test paper problem described as question number, question types, difficulty, discrimination, score, answer time, using the frequency property and other attributes, constituted seven dimensional vector. Finally, test paper composition design, to generate the initial population, fitness function design, operation operator design, algorithm terminates and others, four key steps were designed. The content of this paper to meet the quality and speed requirements of university English test paper composition, and has the advantages of randomness, scientificalness and so on.

Sign in / Sign up

Export Citation Format

Share Document