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This study evaluates the efficiency of two artificial intelligence models, DeepSeek and OpenAI, in generating code for algorithmic systems. Efficiency is assessed through execution speed, code accuracy, and the number of code characters produced. Data were collected from 100 tests covering search, sorting, graph, dynamic programming, optimization, data processing, text, and machine learning algorithms. The objective is to compare the performance of both models to support the development of efficient information retrieval systems. The method involves algorithm testing with statistical analysis of execution time, accuracy, and code length. Results indicate that DeepSeek has an average execution time of 28.74 seconds, slightly slower than OpenAI’s 28.49 seconds. However, DeepSeek’s accuracy (85.88%) surpasses OpenAI’s (85.03%). The average number of code characters is identical at 96.35 characters. The study concludes that DeepSeek excels in accuracy, while OpenAI is faster in certain cases, providing valuable insights for developers in selecting AI models for information retrieval applications.

Genetic Algorithm is a kind of search algorithm based on the mechanics of natural selection and genetics. This algorithm can  search  for a global optimum  solution using multiple path and  treat  integer problem naturally. This paper presents application of Genetic Algorithm for determining the size, location, type, and number of capacitors to be placed on radial distribution system. The objective is to minimize the peak power losses and energy losses in the distribution system considering the capacitor cost. The algorithm was implemented in Delphi programming language and tested for a realistic physically­existing feeder to show its feasibility and capabilities.