High Performance Adaptive Genetic Algorithms for Real Time Decision Making in Complex Systems

Complex systems need instant decision capabilities which traditional algorithms cannot deliver without difficulty. The adaptive genetic algorithms solve optimization problems through dynamic evolutionary optimization strategy development. The proposed research develops a high-performance framework of adaptive genetic algorithm which combines variables-dependent mutation rates along with automatic crossover probability adjustment and parallel processing. The algorithm received testing in different complex system environments which included logistics networks and financial modeling alongside autonomous control systems. The adaptive framework achieved enhanced performance results that attained 94.8% accuracy in logistics optimization and 92.3% accuracy in financial forecasting along with 89.7% accuracy in autonomous control systems. The performance boost using adaptive genetic algorithms reached a 78.6% increase versus classic genetic algorithms during execution. High-performance adaptive genetic algorithms develop resilient solutions for real-time complex system decision making that optimizes performance based on changing environmental conditions and provides scalable real-time solutions with maintained adaptability.