Performance Evaluation of Bitstring Representations in a Linear Genetic Programming Framework

November 04, 2025 ยท Declared Dead ยท ๐Ÿ› arXiv.org

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Authors Clyde Meli, Vitezslav Nezval, Zuzana Kominkova Oplatkova, Victor Buttigieg, Anthony Spiteri Staines arXiv ID 2511.02897 Category cs.NE: Neural & Evolutionary Cross-listed cs.AI, cs.PF Citations 0 Venue arXiv.org Last Checked 4 months ago
Abstract
Different bitstring representations can yield varying computational performance. This work compares three bitstring implementations in C++: std::bitset, boost::dynamic_bitset, and a custom direct implementation. Their performance is benchmarked in the context of concatenation within a Linear Genetic Programming system. Benchmarks were conducted on three platforms (macOS, Linux, and Windows MSYS2) to assess platform specific performance variations. The results show that the custom direct implementation delivers the fastest performance on Linux and Windows, while std::bitset performs best on macOS. Although consistently slower, boost::dynamic_bitset remains a viable and flexible option. These findings highlight the influence of compiler optimisations and system architecture on performance, providing practical guidance for selecting the optimal method based on platform and application requirements.
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