Genetic Algorithm Search Method with Lifespan and State Transitions

The patent combines the speed advantage of gradient-based methods with the global search capability of genetic algorithms by integrating fitness evaluation with gradient information. The modified fitness function incorporates both the objective function value and gradient magnitude, allowing the algorithm to quickly approach optimal regions while avoiding local minima through genetic variation mechanisms.

The patent implements feedback mechanisms through fitness sharing and elitism strategies. The fitness sharing approach provides feedback about the search space structure by penalizing regions with similar solutions, while elitism preserves the best solutions across generations, ensuring that the search process continuously improves while maintaining diversity to escape local minima.

The patent applies local quality through fitness sharing and niche techniques. By evaluating fitness not only based on objective function value but also on similarity to other solutions, the algorithm creates different selective pressures for different regions of the search space. This maintains diversity by allowing multiple species to coexist in different niches rather than converging to a single optimal solution.

The patent implements dynamic adaptation through changing population composition over time. The algorithm dynamically adjusts which individuals are selected for reproduction based on real-time fitness evaluations and environmental conditions. This dynamic process allows the population to adapt to changing problem landscapes and maintain diversity by continuously evolving new solution types.

The patent applies preliminary action by pre-establishing a diverse population with multiple species before the actual problem solving begins. This initial diversity is maintained through careful selection of initial individuals and use of mutation operators that preserve genetic variation. When problem characteristics change, this pre-established diversity allows the algorithm to quickly adapt without requiring complete reinitialization.

The patent implements parameter changes through dynamic adjustment of genetic algorithm parameters such as mutation rate, crossover probability, and population size. These parameters are modified based on the problem characteristics and search progress, allowing the algorithm to adapt to changing problem conditions. This dynamic parameter adjustment enables the algorithm to handle time-varying problems without reinitialization.