Evolution based memetic algorithm and its application in software cost estimation

被引：5
|
作者：
Mishra, K. K. [1 ]
Tripathi, Ashish [2 ]
Tiwari, Shailesh [3 ]
Saxena, Nitin [1 ]
机构：
[1] Motilal Nehru Natl Inst Technol, Comp Sci & Engn Dept, Allahabad, Uttar Pradesh, India
[2] SP Mem Inst Technol, Comp Sci & Engn Dept, Allahabad, Uttar Pradesh, India
[3] ABES Engn Coll, Comp Sci & Engn Dept, Ghaziabad, India
来源：
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2017年 / 32卷 / 03期
关键词：
Evolutionary algorithm; adaptive learning; optimization; genetic algorithm; EAMD; phenotypic structure; PARTICLE SWARM OPTIMIZATION; HYBRID GENETIC ALGORITHM;
D O I：
10.3233/JIFS-16463
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：2485 / 2498
页数：14
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