Adaptive Program Evolution with Diversity-Based Selection Pressure
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Solution Overview
Problem
Existing evolutionary algorithms struggle to balance exploration and exploitation effectively, leading to premature convergence and suboptimal solutions in program generation tasks, particularly in fields like AI-driven research and software development.
Innovation Solution
A method involving an agent that determines diversity information and adjusts a temperature parameter to select parent programs, performing evolution iterations with crossover and mutation to generate adaptive programs, dynamically balancing exploration and exploitation based on population diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If evolutionary algorithms use fixed selection pressure, then the algorithm is simple to implement, but it leads to premature convergence and suboptimal solutions
Solution Approach 1:
The patent implements dynamic selection pressure adjustment by introducing a temperature parameter that changes over time and adapts to population diversity. The selection pressure is no longer fixed but dynamically adjusted based on the current state of the population, allowing the algorithm to escape local optima while maintaining simplicity in the core evolutionary framework.
Solution Approach 2:
The patent changes the selection pressure parameter dynamically through the temperature mechanism. The temperature parameter T is adjusted based on population diversity metrics, transforming the fixed selection pressure into a variable parameter that adapts to the evolutionary stage, thereby improving solution quality without requiring complex algorithmic structures.
2Reliability
If evolutionary algorithms increase exploration, then diversity is maintained, but exploitation efficiency decreases leading to slower convergence
Solution Approach 1:
The patent implements periodic adjustment of exploration-exploitation balance through the temperature parameter that evolves over generations. The temperature is periodically adjusted based on diversity metrics, creating a rhythm of exploration and exploitation that maintains diversity while ensuring convergence, rather than maintaining a constant balance.
Solution Approach 2:
The patent introduces feedback mechanisms where the temperature parameter is adjusted based on measured population diversity. The system continuously monitors diversity metrics and uses this feedback to adjust the temperature, thereby dynamically balancing exploration and exploitation to maintain both diversity and convergence speed.
3Adaptability or versatility
If temperature parameter is kept high, then exploration capability is enhanced, but selection pressure becomes too weak causing slow convergence
Solution Approach 1:
The patent makes the temperature parameter dynamic rather than static. The temperature adjusts its magnitude based on the evolutionary stage and population diversity, being high during early exploration phases and gradually decreasing to enable exploitation in later phases, thus balancing exploration capability with convergence time.
Solution Approach 2:
The patent implements preliminary high-temperature exploration followed by gradual cooling. The temperature is initially set high to ensure thorough exploration of the solution space, then systematically reduced to enable efficient exploitation and convergence, pre-planning the temperature trajectory to optimize both exploration and time efficiency.
Data Source
AI summary
A method for generating an adaptive program based on artificial intelligence (AI) performed by an agent in an electronic device. The method includes: determining diversity information of a target population, wherein the diversity information indicates program diversity of the target population; determining a temperature parameter according to the diversity information, wherein the temperature parameter is used to adjust a selection pressure; selecting, from the target population according to the temperature parameter, a first parent program and a second parent program corresponding to the first parent program; and obtaining a target program by performing an evolution iteration according to the first parent program and the second parent program.