Applying selective mutation strategies to the AsmetaL language

An improved multi-group self-adaptive evolutionary programming Algorithm is used to get adapt attribute weight for CBR system. Firstly, this paper analyses the adaptability function based on reference case base REF and testing case base TEST, develops a novel Bi-group self-adaptive evolutionary programming that overcome the lack of conventional evolutionary programming. In this Novel algorithm, evolution of Cauchy operator and Gauss operator are parallel performed with different mutation strategies, and the Gauss operator owns the ability of self-adaptation according to the variation of adaptability function. Information is exchanged when sub-groups are reorganized. Experiment results prove the validity of self-adaptive Algorithm and CBR design system is used successfully in engine design process.

Download Full-text

Investigating Different Metrics for Evaluation and Selection of Mutation Operators for Java

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s021819402150011x ◽

2021 ◽

Vol 31 (03) ◽

pp. 311-336

Author(s):

Shweta Rani ◽

Bharti Suri

Keyword(s):

Cost Reduction ◽

Test Suite ◽

Cost Benefits ◽

Powerful Technique ◽

Reduction Techniques ◽

Mutation Operators ◽

Different Types ◽

Java Programs ◽

Selective Mutation ◽

Selection Of

Mutation testing is a successful and powerful technique, specifically designed for injecting the artificial faults. Although it is effective at revealing the faults, test suite assessment and its reduction, however, suffer from the expense of executing a large number of mutants. The researchers have proposed different types of cost reduction techniques in the literature. These techniques highly depend on the inspection of mutation operators. Several metrics have been evolved for the same. The selective mutation technique is most frequently used by the researchers. In this paper, the authors investigate different metrics for evaluating the traditional mutation operators for Java. Results on 13 Java programs indicate how grouping few operators can impact the effectiveness of an adequate and minimal test suite, and how this could provide several cost benefits.

Download Full-text

Augmented Brain Storm Optimization with Mutation Strategies

Lecture Notes in Computer Science - Simulated Evolution and Learning ◽

10.1007/978-3-319-68759-9_78 ◽

2017 ◽

pp. 949-959 ◽

Cited By ~ 2

Author(s):

Xianghua Chu ◽

Jiansheng Chen ◽

Fulin Cai ◽

Chen Chen ◽

Ben Niu

Keyword(s):

Brain Storm Optimization ◽

Mutation Strategies

Download Full-text

Adaptive Unified Differential Evolution for Clustering

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ◽

10.22146/ijccs.27871 ◽

2018 ◽

Vol 12 (1) ◽

pp. 53

Author(s):

Maulida Ayu Fitriani ◽

Aina Musdholifah ◽

Sri Hartati

Keyword(s):

Differential Evolution ◽

Fitness Function ◽

Single Mutation ◽

Clustering Methods ◽

Mutation Strategy ◽

Clustering Quality ◽

Silhouette Index ◽

Mutation Strategies ◽

Time Required

Various clustering methods to obtain optimal information continues to evolve one of its development is Evolutionary Algorithm (EA). Adaptive Unified Differential Evolution (AuDE), is the development of Differential Evolution (DE) which is one of the EA techniques. AuDE has self adaptive scale factor control parameters (F) and crossover-rate (Cr).. It also has a single mutation strategy that represents the most commonly used standard mutation strategies from previous studies.The AuDE clustering method was tested using 4 datasets. Silhouette Index and CS Measure is a fitness function used as a measure of the quality of clustering results. The quality of the AuDE clustering results is then compared against the quality of clustering results using the DE method.The results show that the AuDE mutation strategy can expand the cluster central search produced by ED so that better clustering quality can be obtained. The comparison of the quality of AuDE and DE using Silhoutte Index is 1:0.816, whereas the use of CS Measure shows a comparison of 0.565:1. The execution time required AuDE shows better but Number significant results, aimed at the comparison of Silhoutte Index usage of 0.99:1 , Whereas on the use of CS Measure obtained the comparison of 0.184:1.

Download Full-text