Simulation Program for Cognitive Radio Cognitive Engine Based on Genetic Algorithm

This document presents a simulation program for a cognitive radio cognitive engine based on genetic algorithms. The program employs genetic algorithms to simulate and optimize the working principles and performance of cognitive radio engines. Genetic algorithms are computational methods that mimic natural evolutionary processes, iteratively evolving solutions to find optimal configurations through selection, crossover, and mutation operations. In this simulation program, the genetic algorithm implementation includes fitness functions that evaluate radio performance metrics such as throughput, bit error rate, and spectral efficiency. The algorithm optimizes key cognitive engine parameters including transmission power levels, modulation schemes, and channel selection criteria. Population initialization methods generate diverse parameter sets, while crossover operations combine promising solutions and mutation introduces new variations to prevent premature convergence. The simulation framework allows researchers to model complex radio environments and test optimization strategies through multiple generations of evolutionary computation. Performance visualization tools track convergence patterns and solution quality over iterations. By using this simulation program, researchers can better understand cognitive radio engine operation principles and improve system performance through algorithmic optimization. This program provides valuable insights for cognitive radio technology research and offers guidance for future developments in adaptive wireless communication systems.