A novel functional sized population quantum evolutionary algorithm for fractal image compression

Author

Nodehi, Ali ; Tayarani, Mohamad ; Mahmoudi, Fariborz

Author_Institution

Islamic Azad Univ., Gorgan, Iran

fYear

2009

fDate

20-21 Oct. 2009

Firstpage

564

Lastpage

569

Abstract

Quantum evolutionary algorithm (QEA) is a novel optimization algorithm which uses a probabilistic representation for solution and is highly suitable for combinatorial problems like Knapsack problem. Fractal image compression is a well-known problem which is in the class of NP-Hard problems. Genetic algorithms are widely used for fractal image compression problems, but QEA is not used for this kind of problems yet. This paper uses a novel Functional Sized population Quantum Evolutionary Algorithm for fractal image compression. Experimental results show that the proposed algorithm has a better performance than GA and conventional fractal image compression algorithms.

Keywords

combinatorial mathematics; computational complexity; data compression; genetic algorithms; image coding; knapsack problems; Knapsack problem; NP-Hard problems; combinatorial problems; fractal image compression; functional sized population quantum evolutionary algorithm; genetic algorithms; probabilistic representation; Benchmark testing; Evolutionary computation; Fractals; Genetic algorithms; Genetic mutations; Image coding; NP-hard problem; Optimization methods; Partitioning algorithms; Size control; Fractal Image Compression; Genetic Algorithms; Optimization Method; Quantum Evolutionary Algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Conference, 2009. CSICC 2009. 14th International CSI

Conference_Location

Tehran

Print_ISBN

978-1-4244-4261-4

Electronic_ISBN

978-1-4244-4262-1

Type

conf

DOI

10.1109/CSICC.2009.5349639

Filename

5349639