ВИКОРИСТАННЯ ГЕНЕТИЧНИХ АЛГОРИТМІВ В АДАПТИВНИХ КОМПІЛЯТОРАХ ДЛЯ КРОСПЛАТФОРМЕНОЇ ОПТИМІЗАЦІЇ

Context. Modern software is developed under conditions of continuously increasing complexity of computing systems. Today, developers must consider a vast diversity of platforms, ranging from resource-constrained mobile devices to servers with highperformance processors and specialized architectures such as GPUs, FPGAs and even quantum computers. This heterogeneity requires software to operate efficiently across various hardware platforms. However, portability remains one of the most challenging tasks, especially when high performance is required. One of the promising directions to address this problem is the use of adaptive compilers that can automatically optimize code for different architectures. This approach allows developers to focus on the functional part of the software, minimizing the effort spent on optimization and configuration. Genetic algorithms (GAs) play a special role among the methods used to create adaptive compilers. These are powerful evolutionary techniques that allow finding optimal solutions in complex and multidimensional parameter spaces.Objective. The objective of this research is to apply genetic algorithms in the process of adaptive compilation to enable automatic optimization of software across different hardware platforms.Method. The approach is based on genetic algorithms to automate the compilation process. The key stages include: population initialization – creation of an initial set of compilation parameters; fitness function evaluation – assessment of the efficiency of each parameter combination; evolutionary operations – applying crossover, mutation and selection to generate the next generation of parameters; termination criteria – stopping the iterative process upon reaching an optimal result or stabilization of metrics.Results. The developed algorithm was implemented in Python using the numpy, multiprocessing and subprocess libraries.Performance evaluation of the algorithm was carried out using execution time, energy consumption and memory usage metrics.Conclusions. The scientific novelty of the study lies in the development of: an innovative approach to automatic compilation parameter optimization based on genetic algorithms; a method for dynamic selection of optimization strategies based on performance metrics for different architectures; integration of GAs with modern compilers such as LLVM for automatic analysis of intermediate representation and code optimization; and methods for applying adaptive compilers to solve cross-platform optimization problems. The practical significance is determined by the use of genetic algorithms in adaptive compilers, which significantly improves the efficiency of the compilation process by automating the selection of optimal parameters for various architectures. The proposed approach can be successfully applied in the fields of mobile computing, cloud technologies and high-performance systems.