Akademik Veri Yönetim Sistemi
JOURNAL OF OPEN SOURCE SOFTWARE, cilt.10, sa.105, ss.1-6, 2025 (Hakemli Dergi)
pycellga is a Python package that implements cellular genetic algorithms (CGAs) for optimizing complex problems. CGAs combine the principles of cellular automata and traditional genetic algorithms, utilizing a spatially structured population organized in a grid-like topology. This structure allows each individual to interact only with its neighboring individuals, promoting diversity and maintaining a balance between exploration and exploitation during the optimization process. While CGAs themselves are not a novel contribution of this work, pycellga significantly enhances their applicability by integrating advanced features and providing unparalleled versatility. The package supports binary, real-valued, and permutation-based optimization problems, making it adaptable to a wide variety of problem domains. Its use of machine-coded operators for real-valued optimization, adhering to IEEE 754 floating-point arithmetic standards, ensures high precision and computational efficiency. Moreover, pycellga is designed to be extensible, enabling users to easily customize selection, crossover, and mutation operators to suit specific problem requirements. The package is designed to be user-friendly, with a straightforward installation process and comprehensive documentation. Researchers and practitioners in fields such as operations research, artificial intelligence, and machine learning can leverage pycellga to tackle complex optimization challenges effectively. By integrating the principles of cellular automata with genetic algorithms, pycellga represents a significant advancement in the field of evolutionary computation, offering increased flexibility and adaptability compared to traditional methods. Additionally, pycellga includes machine-coded operators with byte implementations, developed by Satman (2013). It features Alpha-male CGA, Machine-Coded Compact CGA, and Improved CGA with Machine-Coded Operators for real-valued optimization problems (Karakaya & Satman, 2024).