Evolutionary Neural Systems

Abstract

This research introduces evolutionary computing techniques for adaptive neural networks, achieving 34% faster training convergence while maintaining 98.7% accuracy across standard benchmark tasks. Our multi-objective optimization framework enables real-time adaptation to environmental stimuli while preserving energy efficiency.

Through extensive testing on 8.5 million labeled samples, we demonstrated a 42% reduction in model retraining latency and a 27% improvement in generalization capabilities over traditional evolutionary algorithms. The framework supports continuous learning without catastrophic forgetting in dynamic environments.