International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
Volume 64 - Issue 22 |
Published: February 2013 |
Authors: Mohamed A. Belal, Mohamed H. Haggag |
![]() |
Mohamed A. Belal, Mohamed H. Haggag . A Structured-Population Genetic-Algorithm based on Hierarchical Hypercube of Genes Expressions. International Journal of Computer Applications. 64, 22 (February 2013), 5-18. DOI=10.5120/10775-4446
@article{ 10.5120/10775-4446, author = { Mohamed A. Belal,Mohamed H. Haggag }, title = { A Structured-Population Genetic-Algorithm based on Hierarchical Hypercube of Genes Expressions }, journal = { International Journal of Computer Applications }, year = { 2013 }, volume = { 64 }, number = { 22 }, pages = { 5-18 }, doi = { 10.5120/10775-4446 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2013 %A Mohamed A. Belal %A Mohamed H. Haggag %T A Structured-Population Genetic-Algorithm based on Hierarchical Hypercube of Genes Expressions%T %J International Journal of Computer Applications %V 64 %N 22 %P 5-18 %R 10.5120/10775-4446 %I Foundation of Computer Science (FCS), NY, USA
Structured-population Genetic Algorithm (GA) usually leads to more superior performance than the panmictic genetic algorithm; since it can control two opposite processes, namely exploration and exploitation in the search space. Several spatially structured-population GAs have been introduced in the literature such as cellular, patchwork, island-model, terrain-based A, graph-based, religion-based and social-based GA. All the aforementioned works did not construct the sub-populations based on the genes information of the individuals themselves. The structuring of sub-populations based on this information might help in attaining better performance and more efficient search strategy. In this paper, the structured population is represented as hierarchical hypercube of sub-populations that are dynamically constructed and adapted at search time. Each sub-population represents a sub-division of the real genes space. This structure could help in directing the search towards the promising sub-spaces. Finally, a comparative study with other known structured population GA is provided.