A Performance Analysis of Compressed Compact Genetic Algorithm | Zendy

Orawan Watchanupaporn | Zendy; Nuanwan Soonthornphisaj | Zendy; Worasait Suwannik | Zendy

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A Performance Analysis of Compressed Compact Genetic Algorithm

Author(s) -

Orawan Watchanupaporn,

Nuanwan Soonthornphisaj,

Worasait Suwannik

Publication year - 1970

Publication title -

ecti transactions on computer and information technology (ecti-cit)

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.132

H-Index - 2

ISSN - 2286-9131

DOI - 10.37936/ecti-cit.200621.53264

Subject(s) - robustness (evolution) , algorithm , computer science , encoding (memory) , genetic algorithm , population , convergence (economics) , population based incremental learning , artificial intelligence , machine learning , biochemistry , chemistry , demography , sociology , economics , gene , economic growth

Compressed compact genetic algorithm (c2GA) is an algorithm that utilizes the compressed chromosome encoding and compact genetic algorithm (cGA). The advantage of c2GA is to reduce the memory usage by representing population as a probability vector. In this paper, we analyze the performance in term of robustness of c2GA. Since the compression and decompression strategy employ two parameters, which are the length of repeating value and the repeat count, we vary these two parameters to see the performance affected in term of convergence speed. The experimental results show that c2GA outperforms cGA and is a robust algorithm.

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