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Umbarkar, A. J.
- Crossover Operators in Genetic Algorithms:A Review
Abstract Views :461 |
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Authors
Affiliations
1 Department of Information Technology, Walchand College of Engineering, IN
2 Department of Master of Computer Applications, Government College of Engineering, Karad, IN
1 Department of Information Technology, Walchand College of Engineering, IN
2 Department of Master of Computer Applications, Government College of Engineering, Karad, IN
Source
ICTACT Journal on Soft Computing, Vol 6, No 1 (2015), Pagination: 1083-1092Abstract
The performance of Genetic Algorithm (GA) depends on various operators. Crossover operator is one of them. Crossover operators are mainly classified as application dependent crossover operators and application independent crossover operators. Effect of crossover operators in GA is application as well as encoding dependent. This paper will help researchers in selecting appropriate crossover operator for better results. The paper contains description about classical standard crossover operators, binary crossover operators, and application dependant crossover operators. Each crossover operator has its own advantages and disadvantages under various circumstances. This paper reviews the crossover operators proposed and experimented by various researchers.Keywords
Evolutionary Algorithm, Genetic Algorithm, Crossover, Genetic Operators.
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- Review of Parallel Genetic Algorithm Based on Computing Paradigm and Diversity in Search Space
Abstract Views :154 |
PDF Views:0
Authors
Affiliations
1 Department of Information Technology, Walchand College of Engineering, IN
2 Department of Computer Science and Engineering, Jawaharlal Nehru Engineering College, IN
1 Department of Information Technology, Walchand College of Engineering, IN
2 Department of Computer Science and Engineering, Jawaharlal Nehru Engineering College, IN
Source
ICTACT Journal on Soft Computing, Vol 3, No 4 (2013), Pagination: 615-622Abstract
Genetic Algorithm (GA), a stochastic optimization technique, doesn't ensure optimal solution every time. Nowadays there is a need to improve the performance of each and every application so that the time required for obtaining quality solution can be minimized. This paper gives a brief overview of theoretical advances and computing trends, particularly population diversity in PGA (Parallel GA) and provides information about how various authors, researchers, scientists have parallelized GA over various parallel computing paradigms viz. Cluster, MPP (Massively Parallel Processing), GPGPU (General purpose Graphics Processing Units), Grid, Cloud, Multicore/HPC to ensure more optimal solution every time with efficacy and efficiency.Keywords
Genetic Algorithm (GA), Parallel GA (PGA), General Purpose Graphics Processing Unit (GPGPU), Massively Parallel Processor (MPP), Population Diversity, Cloud, Grid, Cluster, HPC.- Genetic Algorithm on General Purpose Graphics Processing Unit: Parallelism Review
Abstract Views :162 |
PDF Views:0
Authors
Affiliations
1 Department of Information Technology, Walchand College of Engineering, IN
2 Department of Computer Engineering, Pune University, IN
3 Department of Computer Science and Engineering, Walchand College of Engineering, IN
1 Department of Information Technology, Walchand College of Engineering, IN
2 Department of Computer Engineering, Pune University, IN
3 Department of Computer Science and Engineering, Walchand College of Engineering, IN