Evolution based memetic algorithm and its application in software cost estimation
Journal of Intelligent and Fuzzy Systems, ISSN: 1875-8967, Vol: 32, Issue: 3, Page: 2485-2498
2017
- 8Citations
- 13Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
A new memetic algorithm named EAMDGA is designed by combining the characteristics of Environmental Adaption Method for Dynamic Environment (EAMD) and Genetic Algorithm (GA). This algorithm is highly efficient and robust in solving the unimodal and multimodal problems. It avoids the problems of getting trapped in local optima and premature convergence. Performance of this algorithm is checked over a group of 24 unimodal and multimodal benchmark functions provided by Black Box Optimization Benchmarking (BBOB-2013). It is found that EAMDGA is superior in performance in comparison to the other algorithms.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85014217599&origin=inward; http://dx.doi.org/10.3233/jifs-16463; http://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JIFS-16463; https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JIFS-16463; https://dx.doi.org/10.3233/jifs-16463; https://content.iospress.com:443/articles/journal-of-intelligent-and-fuzzy-systems/ifs16463
IOS Press
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know