2024-03-28T20:59:14Zhttps://eprints.lib.hokudai.ac.jp/dspace-oai/requestoai:eprints.lib.hokudai.ac.jp:2115/468312023-10-05T00:27:12Zhdl_2115_20061hdl_2115_153An adaptive resolution hybrid binary-real coded genetic algorithmAbdul-Rahman, Omar ArifMunetomo, MasaharuAkama, KiyoshiBinary-coded GAReal-coded GAHybrid scheme410In genetic algorithms (GAs), is it better to use binary encoding schemes or floating point encoding schemes? In this article, we try to tackle this controversial question by proposing a novel algorithm that divides the computational power between two cooperative versions of GAs. These are a binary-coded GA (bGA) and a real-coded GA (rGA). The evolutionary search is primarily led by the bGA, which identifi es promising regions in the search space, while the rGA increases the quality of the solutions obtained by conducting an exhaustive search throughout these regions. The resolution factor (R), which has a value that is increasingly adapted during the search, controls the interactions between the two versions. We conducted comparison experiments employing a typical benchmark function to prove the feasibility of the algorithm under the critical scenarios of increasing problem dimensions and decreasing precision power.SpringerJournal Articleapplication/pdfhttp://hdl.handle.net/2115/46831https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/46831/1/3.%20paper.pdf1433-5298Artificial Life and Robotics1611211242011enginfo:doi/10.1007/s10015-011-0906-zThe original publication is available at www.springerlink.comauthor