Performance Evaluation of Bitstring Representations in a Linear Genetic Programming Framework
This provides incremental guidance for selecting bitstring representations in genetic programming based on platform-specific performance.
This work compared the performance of three bitstring implementations (std::bitset, boost::dynamic_bitset, and a custom direct implementation) in a Linear Genetic Programming framework, finding that the custom implementation was fastest on Linux and Windows, while std::bitset performed best on macOS.
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.