New avenues for the creation of intelligent, adaptive optimization strategies have been made possible by the combination of genetic algorithms (GAs) with artificial intelligence (AI). Through the introduction of learning-driven, context-aware, and dynamically adaptive mechanisms, this study investigates the developing role of AI in improving genetic operators—selection, crossover, and mutation. Because they frequently depend on heuristic rules and static probabilities, traditional genetic operators are not as effective in a variety of complicated problem spaces. Intelligent genetic operators that can self-tune, predict convergence trends, and preserve variety are the result of recent developments that use machine learning, deep learning, and reinforcement learning techniques. Current developments in AI-augmented GAs are reviewed, along with significant advancements in operator design and their effects on scalability and performance in multi-objective and real-time optimization problems. There are additional viewpoints on new issues including interpretability, computational overhead, and hybrid system design. Driven by the synergy between AI and evolutionary computation, the results point to a paradigm shift toward more autonomous and problem-specific evolutionary algorithms.
Genetic Operators, Intelligent Optimization, Self-tuning Operators
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