NENov 4, 2025
Performance Evaluation of Bitstring Representations in a Linear Genetic Programming FrameworkClyde Meli, Vitezslav Nezval, Zuzana Kominkova Oplatkova et al.
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.
IRAug 28, 2019
VJAGG -- A Thick-Client Smart-Phone Journey Detection AlgorithmMichael P. J. Camilleri, Adrian Muscat, Victor Buttigieg et al.
In this paper we describe $Vja\dot{g}\dot{g}$, a battery-aware journey detection algorithm that executes on the mobile device. The algorithm can be embedded in the client app of the transport service provider or in a general purpose mobility data collector. The thick client setup allows the customer/participant to select which journeys are transferred to the server, keeping customers in control of their personal data and encouraging user uptake. The algorithm is tested in the field and optimised for both accuracy in registering complete journeys and battery power consumption. Typically the algorithm can run for a full day without the need of recharging and more than 88% of journeys are correctly detected from origin to destination, whilst 12% would be missing part of the journey.