Incremental Strategy for Applying Mutation Operators Emphasizing Faults Difficult to be Detected by Automated Static Analyser

2017 ◽  
Author(s):  
Vinícius Barcelos Silva ◽  
Cláudio Antonio Araujo ◽  
Edmundo Sérgio Spoto ◽  
Auri M. R. Vincenzi
Keyword(s):  
Mutation Operators ◽  
Incremental Strategy

Mutation Operators in Python using SMT-P

2018 ◽  
Vol 6 (7) ◽  
pp. 776-778
Author(s):  
R. Gupta ◽  
C. Verma ◽  
N.Singh .
Keyword(s):  
Mutation Operators

Online Method for Regression with an Incremental Strategy

2016 ◽  
Author(s):  
Roberto C. S. N. P. Souza ◽  
Saul C. Leite ◽  
Carlos C. H. Borges ◽  
Raul Fonseca Neto
Keyword(s):  
Incremental Strategy

Finding Community of Brain Networks Based on Neighbor Index and DPSO with Dynamic Crossover

2020 ◽  
Vol 15 (4) ◽  
pp. 287-299
Author(s):  
Jie Zhang ◽  
Junhong Feng ◽  
Fang-Xiang Wu
Keyword(s):  
Brain Function ◽  
Brain Networks ◽  
Discrete Particle Swarm Optimization ◽  
Mri Brain ◽  
Mutation Operators ◽  
Neural Unit ◽  
Crossover And Mutation ◽  
The Brain ◽  
Index Table ◽  
Dynamic Crossover

Background: : The brain networks can provide us an effective way to analyze brain function and brain disease detection. In brain networks, there exist some import neural unit modules, which contain meaningful biological insights. Objective:: Therefore, we need to find the optimal neural unit modules effectively and efficiently. Method:: In this study, we propose a novel algorithm to find community modules of brain networks by combining Neighbor Index and Discrete Particle Swarm Optimization (DPSO) with dynamic crossover, abbreviated as NIDPSO. The differences between this study and the existing ones lie in that NIDPSO is proposed first to find community modules of brain networks, and dose not need to predefine and preestimate the number of communities in advance. Results: : We generate a neighbor index table to alleviate and eliminate ineffective searches and design a novel coding by which we can determine the community without computing the distances amongst vertices in brain networks. Furthermore, dynamic crossover and mutation operators are designed to modify NIDPSO so as to alleviate the drawback of premature convergence in DPSO. Conclusion: The numerical results performing on several resting-state functional MRI brain networks demonstrate that NIDPSO outperforms or is comparable with other competing methods in terms of modularity, coverage and conductance metrics.

scholarly journals Resource and dependency based test case generation for RESTful Web services

2021 ◽  
Vol 26 (4) ◽  
Author(s):  
Man Zhang ◽  
Bogdan Marculescu ◽  
Andrea Arcuri
Keyword(s):  
Web Services ◽  
Domain Knowledge ◽  
Automated System ◽  
Test Case ◽  
Test Cases ◽  
Evolutionary Search ◽  
Sampling Strategies ◽  
Mutation Operators ◽  
On Line ◽  
Restful Web Services

AbstractNowadays, RESTful web services are widely used for building enterprise applications. REST is not a protocol, but rather it defines a set of guidelines on how to design APIs to access and manipulate resources using HTTP over a network. In this paper, we propose an enhanced search-based method for automated system test generation for RESTful web services, by exploiting domain knowledge on the handling of HTTP resources. The proposed techniques use domain knowledge specific to RESTful web services and a set of effective templates to structure test actions (i.e., ordered sequences of HTTP calls) within an individual in the evolutionary search. The action templates are developed based on the semantics of HTTP methods and are used to manipulate the web services’ resources. In addition, we propose five novel sampling strategies with four sampling methods (i.e., resource-based sampling) for the test cases that can use one or more of these templates. The strategies are further supported with a set of new, specialized mutation operators (i.e., resource-based mutation) in the evolutionary search that take into account the use of these resources in the generated test cases. Moreover, we propose a novel dependency handling to detect possible dependencies among the resources in the tested applications. The resource-based sampling and mutations are then enhanced by exploiting the information of these detected dependencies. To evaluate our approach, we implemented it as an extension to the EvoMaster tool, and conducted an empirical study with two selected baselines on 7 open-source and 12 synthetic RESTful web services. Results show that our novel resource-based approach with dependency handling obtains a significant improvement in performance over the baselines, e.g., up to + 130.7% relative improvement (growing from + 27.9% to + 64.3%) on line coverage.

TABU PROGRAMMING: A NEW PROBLEM SOLVER THROUGH ADAPTIVE MEMORY PROGRAMMING OVER TREE DATA STRUCTURES

2011 ◽  
Vol 10 (02) ◽  
pp. 373-406 ◽  
Author(s):  
ABDEL-RAHMAN HEDAR ◽  
EMAD MABROUK ◽  
MASAO FUKUSHIMA
Keyword(s):  
Search Algorithm ◽  
Benchmark Problems ◽  
Problem Solver ◽  
Learning Tools ◽  
Mutation Operators ◽  
Tree Data ◽  
Tree Data Structure ◽  
Comprehensive Framework ◽  
Rate Of Success ◽  
Crossover And Mutation

Since the first appearance of the Genetic Programming (GP) algorithm, extensive theoretical and application studies on it have been conducted. Nowadays, the GP algorithm is considered one of the most important tools in Artificial Intelligence (AI). Nevertheless, several questions have been raised about the complexity of the GP algorithm and the disruption effect of the crossover and mutation operators. In this paper, the Tabu Programming (TP) algorithm is proposed to employ the search strategy of the classical Tabu Search algorithm with the tree data structure. Moreover, the TP algorithm exploits a set of local search procedures over a tree space in order to mitigate the drawbacks of the crossover and mutation operators. Extensive numerical experiments are performed to study the performance of the proposed algorithm for a set of benchmark problems. The results of those experiments show that the TP algorithm compares favorably to recent versions of the GP algorithm in terms of computational efforts and the rate of success. Finally, we present a comprehensive framework called Meta-Heuristics Programming (MHP) as general machine learning tools.

A Two-Stage Hybrid Algorithm for Flexible Job-Shop Scheduling

2011 ◽  
Vol 268-270 ◽  
pp. 476-481
Author(s):  
Li Gao ◽  
Ke Lin Xu ◽  
Wei Zhu ◽  
Na Na Yang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Job Shop ◽  
Flow Shop ◽  
Job Shop Scheduling ◽  
Selection Procedure ◽  
Dynamic Programming Method ◽  
Two Stage ◽  
Simulated Annealing Genetic Algorithm ◽  
Mutation Operators

A mathematical model was constructed with two objectives. A two-stage hybrid algorithm was developed for solving this problem. At first, the man-hour optimization based on genetic algorithm and dynamic programming method, the model decomposes the flow shop into two layers: sub-layer and patrilineal layer. On the basis of the man-hour optimization，A simulated annealing genetic algorithm was proposed to optimize the sequence of operations. A new selection procedure was proposed and hybrid crossover operators and mutation operators were adopted. A benchmark problem solving result indicates that the proposed algorithm is effective.

Sequence-Dependent Robotic Disassembly Line Balancing Problem Considering Disassembly Path

2020 ◽  
Author(s):  
Qinglian Chen ◽  
Bitao Yao ◽  
Duc Truong Pham
Keyword(s):  
High Efficiency ◽  
Line Balancing ◽  
Gear Pump ◽  
Nsga Ii ◽  
Line System ◽  
Mutation Operators ◽  
Sequence Dependent ◽  
Disassembly Line Balancing ◽  
Robotic Disassembly ◽  
Disassembly Line

Abstract For the realization of environmental protection and resource conservation, remanufacturing is of great significance. Disassembly is a key step in remanufacturing, the disassembly line system is the main scenario for product disassembly, usually consisting of multiple workstations, and has prolific productivity. The application of the robots in the disassembly line will eliminate various problems caused by manual disassembly. Moreover, the disassembly line balancing problem (DLBP) is of great importance for environmental remanufacturing. In the past, disassembly work was usually done manually with high cost and relatively low efficiency. Therefore, more and more researches are focusing on the automatic DLBP due to its high efficiency. This research solves the sequence-dependent robotic disassembly line balancing problem (SDRDLBP) with multiple objectives. It considers the sequence-dependent time increments and requires the generated feasible disassembly sequence to be assigned to ordered disassembly workstations according to the specific robotic workstation assignment method. In robotic DLBP, due to the special characteristics of robotic disassembly, we need to consider the moving time of the robots’ disassembly path during the disassembly process. This is also the first time to consider sequence-dependent time increments while considering the disassembly path of the robots. Then with the help of crossover and mutation operators, multi-objective evolutionary algorithms (MOEAs) are proposed to solve SDRDLBP. Based on the gear pump model, the performance of the used algorithm under different cycle times is analyzed and compared with another two algorithms. The average values of the HV and IGD indicators have been calculated, respectively. The results show the NSGA-II algorithm presents outstanding performance among the three MOEAs, and hence demonstrate the superiority of the NSGA-II algorithm.

Sign in / Sign up

Export Citation Format

Share Document