Predefined-Strategy Genetic Learning for Adaptive Predictive Models
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Solution Overview
Problem
Existing predictive modeling, machine learning, and artificial intelligence techniques rely heavily on human interaction, making them inefficient in responding to unfamiliar inputs and events such as disaster response, strategic insights on breaking news, and mitigating zero-day exploit impacts.
Innovation Solution
A method for controlling genetic learning for predictive models using predefined strategies, involving the selection of predictive models and strategies, generation of candidate genomes, and merging new agent genomes with high-performing agents to adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static predictive modeling techniques are used, then model stability is maintained, but the system cannot respond to unfamiliar inputs and dynamic events
Solution Approach 1:
The patent implements dynamic predictive models through genetic learning that allows model structures to evolve and adapt over time. Multiple agents with different model configurations compete and reproduce, enabling the system to dynamically respond to unfamiliar inputs while maintaining stability through the evolutionary selection process that preserves high-performing model structures.
Solution Approach 2:
The system changes model parameters through genetic operations including mutation, crossover, and selection. Candidate genomes represent different parameter configurations, and the fitness evaluation process selects parameter sets that improve performance on unfamiliar data, allowing the model to adapt its parameters dynamically while maintaining structural stability.
2Productivity
If human interaction is required to design and operate predictive models, then model accuracy can be improved, but productivity and response time to events decrease
Solution Approach 1:
The system implements self-service through automated genetic learning where agents autonomously evolve their own model structures and parameters. The fitness evaluation and selection processes automatically identify high-performing models without human intervention, enabling rapid model development while maintaining accuracy through the evolutionary optimization process that continuously refines model performance.
Solution Approach 2:
The system uses feedback mechanisms where model performance is evaluated against target data and this feedback drives the genetic selection process. High-fitness models are selected for reproduction and mutation, creating a feedback loop that automatically improves model accuracy over generations while accelerating development productivity through automated evaluation and selection.
3Adaptability or versatility
If genetic learning with multiple agents is implemented, then adaptability to dynamic events improves, but device complexity increases
Solution Approach 1:
The system segments the predictive modeling task into multiple independent agents, each with its own genome and model configuration. This segmentation allows parallel evaluation of different model approaches and enables the system to adapt to dynamic events by selecting from diverse agent capabilities, while managing complexity through modular agent design that can be independently developed and evaluated.
Solution Approach 2:
The system implements multi-functionality through a universal agent framework that can perform multiple predictive modeling tasks. Each agent serves as a multi-functional unit capable of evaluating different model types and configurations, and the overall system can adapt to various dynamic events by deploying appropriate agent combinations, reducing complexity through shared evaluation infrastructure.
Data Source
AI summary
Methods for controlling genetic learning for predictive models using predefined strategies may include, for each of a plurality of agents, selecting a type of predictive model. A strategy may be selected from predefined strategies. Candidate genomes may be generated and may include a plurality of genes. Each gene may be associated with a feature of the agent predictive model. A fit of each candidate genome to the agent strategy may be determined. A candidate genome may be selected based on the fit. For each of a plurality of epochs, a plurality of training iterations may be performed for each agent. A fitness of each agent predictive model may be determined. A subset of agents with a highest fitness may be determined. For each agent of the subset, at least one new agent may be generated. The genomes of the new agents may be merged with some genomes of the subset.