Improved Genetic Algorithms for Software Testing Cases Generation

2013 ◽  
Vol 380-384 ◽  
pp. 1464-1468
Author(s):  
Shun Kun Yang ◽  
Fu Ping Zeng
Keyword(s):  
Genetic Algorithms ◽  
Software Testing ◽  
Fitness Function ◽  
Automatic Generation ◽  
Improved Genetic Algorithm ◽  
Dynamic Adjustment ◽  
Genetic Operators ◽  
Adaptive Genetic Algorithms ◽  
Crossover And Mutation ◽  
Testing Coverage

In order to realize the adaptive Genetic Algorithms to balance the contradiction between algorithm convergence rate and algorithm accuracy for automatic generation of software testing cases, improved Genetic Algorithms is proposed for different aspects. Orthogonal method and Equivalence partitioning are employed together to make the initial testing population more effective with more reasonable coverage; Genetic operators of Crossover and Mutation is defined adaptively by the dynamic adjustment according to multi-objective Fitness function, which can guide the testing process more properly and realize the biggest testing coverage to find more defects as far as possible. Finally, the improved Genetic Algorithm are compared and analyzed by testing one benchmark program to verify its feasibility and effectiveness.

Assembly Planning: A Genetic Approach

1998 ◽  
Author(s):  
Shiang-Fong Chen
Keyword(s):  
Genetic Algorithms ◽  
Fitness Function ◽  
Assembly Planning ◽  
Genetic Approach ◽  
Genetic Operators ◽  
Complex Problems ◽  
All Solutions ◽  
Assembly Problem ◽  
Optimal Assembly

Abstract The difficulty of an assembly problem is the inherent complexity of possible solutions. If the most suitable plan is selected after all solutions are found, it will be very time consuming and unrealistic. Motivated by the success of genetic algorithms (GAs) in solving combinatorial and complex problems by examining a small number of possible candidate solutions, GAs are employed to find a near-optimal assembly plan for a general environment. Five genetic operators are used: tree crossover, tree mutation, cut-and-paste, break-and-joint, and reproduction. The fitness function can adapt to different criteria easily. This assembly planner can help an inexperienced technician to find a good solution efficiently. The algorithm has been fully implemented. One example product is given to show the applications and results.

scholarly journals A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Xixing Li ◽  
Shunsheng Guo ◽  
Yi Liu ◽  
Baigang Du ◽  
Lei Wang
Keyword(s):  
Mathematical Model ◽  
Production Planning ◽  
Flow Shop ◽  
Fitness Function ◽  
Capacity Constraint ◽  
Planning Problem ◽  
Improved Genetic Algorithm ◽  
Genetic Operators ◽  
Make To Order ◽  
Delay Cost

The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.

Research on the Optimization of the Numerical Value Based on Improved Genetic Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 1256-1260
Author(s):  
Zhen Dong Li ◽  
Qi Yi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Adaptive Genetic Algorithm ◽  
Improved Genetic Algorithm ◽  
Adaptive Genetic Algorithms ◽  
Crossover Probability ◽  
Adaptive Crossover ◽  
Searching Ability ◽  
New Algorithms ◽  
Relevance Index

For the lack of crossover operation, from three aspects of crossover operation , systemically proposed one kind of improved Crossover operation of Genetic Algorithms, namely used a kind of new consistent Crossover Operator and determined which two individuals to be paired for crossover based on relevance index, which can enhance the algorithms global searching ability; Based on the concentrating degree of fitness, a kind of adaptive crossover probability can guarantee the population will not fall into a local optimal result. Simulation results show that: Compared with the traditional cross-adaptive genetic Algorithms and other adaptive genetic algorithm, the new algorithms convergence velocity and global searching ability are improved greatly, the average optimal results and the rate of converging to the optimal results are better.

AUTOMATIC PRODUCTION OF TEST DATA WITH A MULTIPLE BATCH-OPTIMISTIC METHOD

2009 ◽  
Vol 18 (01) ◽  
pp. 61-80 ◽  
Author(s):  
ANASTASIS A. SOFOKLEOUS ◽  
ANDREAS S. ANDREOU
Keyword(s):  
Genetic Algorithms ◽  
Software Testing ◽  
Test Data ◽  
Automatic Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
Generation System ◽  
Automatic Production ◽  
Coverage Criterion ◽  
Testing Coverage

Recent research on software testing focuses on integrating techniques, such as computational intelligence, with special purpose software tools so as to minimize human effort, reduce costs and automate the testing process. This work proposes a complete software testing framework that utilizes a series of specially designed genetic algorithms to generate automatically test data with reference to the edge/condition testing coverage criterion. The framework utilizes a program analyzer, which examines the program's source code and builds dynamically program models for automatic testing, and a test data generation system that utilizes genetic algorithms to search the input space and determine a near to optimum set of test cases with respect to the testing coverage criterion. The performance of the framework is evaluated on a pool of programs consisting of both standard and random-generated programs. Finally, the proposed test data generation system is compared against other similar approaches and the results are discussed.

Automatic Generation of Musical Tone Row and Rhythm Based on the Twelve-Tone Technique Using Genetic Algorithm

2010 ◽  
Vol 14 (3) ◽  
pp. 288-296
Author(s):  
Yoichiro Maeda ◽  
◽  
Yusuke Kajihara
Keyword(s):  
Genetic Algorithms ◽  
Music Composition ◽  
Fitness Function ◽  
Automatic Generation ◽  
Computer Applications ◽  
Twelve Tone ◽  
Musical Rhythm ◽  
Interactive Genetic Algorithms ◽  
Musical Tone ◽  
The Relationship

Genetic Algorithms (GA) and Interactive Genetic Algorithms (IGA) used to generate sound in computer applications generating music are difficult to use, as is, in directly composing music. We propose music composition based on the 12-Tone Technique (TTT). In TTT composition, the melody and rhythm are usually created separately. The melody is created first to determine the musical subject and atmosphere. We design a fitness function based on the relationship between consonant and dissonant intervals that are a part of general musical theory and generate the 12-Tone (TT) row automatically by searching for consonant tone rows using the GA. We then set a fitness function for evaluating the rhythm we define, and obtained musical rhythm using the GA.

QoS Routing Optimization Based on Improved Genetic Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 1169-1173
Author(s):  
Chang Lin He ◽  
Yu Fen Li ◽  
Lei Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computational Complexity ◽  
Fitness Function ◽  
Qos Routing ◽  
Experimental Results ◽  
Improved Genetic Algorithm ◽  
Coding Schemes ◽  
Routing Optimization

A improved genetic algorithm is proposed to QoS routing optimization. By improving coding schemes, fitness function designs, selection schemes, crossover schemes and variations, the proposed method can effectively reduce computational complexity and improve coding accuracy. Simulations are carried out to compare our algorithm with the traditional genetic algorithms. Experimental results show that our algorithm converges quickly and is reliable. Hence, our method vastly outperforms the traditional algorithms.

Scheme Automatic Generation Based on Full Life Cycle Assessment Gene Model of Structure

2014 ◽  
Vol 687-691 ◽  
pp. 1622-1627
Author(s):  
Ji Wen Chen ◽  
Hong Juan Yang ◽  
Nan Xu ◽  
Li Li
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Fitness Function ◽  
Automatic Generation ◽  
Genetic Operators ◽  
Gene Model ◽  
Structure Information ◽  
Full Life Cycle ◽  
Diamond Wire Saw ◽  
Full Life

Considering the inheritance and hereditary of product structure life cycle assessment, full life cycle assessment properties of structure is introduced into expression of design scheme. Scheme evolution design is presented based on gene model of full life cycle assessment properties of structure. The gene model of life cycle assessment properties of structure is established. The variable length coding is converted to equal length coding to realize the quantitative representation of structure information. The fitness function is established for life cycle assessment of structure by Analytic Hierarchy Process. The genetic operators are designed. Scheme evolution design is realized based on gene model of life cycle assessment of structure, reflecting life cycle assessment properties of design schemes, improving the efficiency of product design generation. The evolution design example of multi-rope diamond wire saw verifies the feasibility of the imposed method.

A Research on the Optimal Design of BP Neural Network Based on Improved GEP

2019 ◽  
Vol 33 (03) ◽  
pp. 1959007 ◽  
Author(s):  
Ruliang Wang ◽  
Benbo Zha
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Fitness Function ◽  
Gene Expression Programming ◽  
Bp Algorithm ◽  
Local Optimum ◽  
Complex Data ◽  
Dynamic Adjustment ◽  
Genetic Operators ◽  
Dynamic Mapping

Due to the functionality of dynamic mapping for nonlinear complex data, BP neural network (BP-NN) as a typical neural network has increasingly been applied to a variety of applications. Although it has been successfully applied, its prominent shortcoming, such as the local optimum problem and the setting problem for the initial parameter of neural network, have not been completely eliminated. In this paper, an optimization algorithm for the architecture, weights and thresholds of neural networks using an improved gene expression programming (IGEP) was presented. The algorithm effectively combines the global search ability of GEP and the local search ability of BP-NN. To obtain a better efficiency, the basic GEP was improved by the dynamic adjustment of the fitness function, genetic operators and the number of evolutionary generations. The experimental results show that the IGEP-BP algorithm is an effective method for evolving neural network.

Automatic Generation of Design Scheme Based on Improved Segmentation Genetic Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1655-1660
Author(s):  
Ji Wen Chen ◽  
Bo Huang ◽  
Bo Pang ◽  
Li Li
Keyword(s):  
Genetic Algorithm ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Information Seeking ◽  
Structural Information ◽  
Fitness Function ◽  
Automatic Generation ◽  
Genetic Operators ◽  
Structure Information ◽  
Design Scheme

Research on composition and quantitative representation of the function unit structural information, seeking the combination process effectively, is the key technology to generate product design schemes. Full life cycle assessment properties of structure is introduced into expression of design scheme. The gene model of life cycle assessment properties of structure is established, and the variable length coding is converted to equal length coding to realize the quantitative representation of structure information. The fitness function is established for life cycle assessment of structure with Analytic Hierarchy Process. The improved segmentation genetic algorithm is studied. The gene sequence of design scheme is segmented. Genetic operators such as across and mutate is designed for structure information the segmented gene fragment. Life cycle assessment gene of structure as attribute does not participate in the genetic operation. Design schemes automatic generation is achieved based on improved segmented genetic operators, reflecting life cycle assessment properties of design schemes.

scholarly journals Dynamic Route Guidance Using Improved Genetic Algorithms

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Zhanke Yu ◽  
Mingfang Ni ◽  
Zeyan Wang ◽  
Yanhua Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Steady State ◽  
Fitness Function ◽  
Population Diversity ◽  
Route Guidance ◽  
Improved Genetic Algorithm ◽  
Simulation Results ◽  
Guidance Algorithm ◽  
Dynamic Route

This paper presents an improved genetic algorithm (IGA) for dynamic route guidance algorithm. The proposed IGA design a vicinity crossover technique and a greedy backward mutation technique to increase the population diversity and strengthen local search ability. The steady-state reproduction is introduced to protect the optimized genetic individuals. Furthermore the junction delay is introduced to the fitness function. The simulation results show the effectiveness of the proposed algorithm.

Sign in / Sign up

Export Citation Format

Share Document