Parameter Set Optimization Using Fitness-Based Device Tuning
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Solution Overview
Problem
Manual optimization of parameter value sets in complex processes is inefficient and time-consuming, particularly in scenarios where multiple devices or systems require different configurations to achieve optimal performance metrics such as yield, user retention, or fuel consumption.
Innovation Solution
An automated system utilizing processing cores and optimization algorithms like adaptive simulated annealing or iterated local search to determine and implement optimal parameter value sets across devices or systems, based on fitness values and device-specific factors, facilitating adaptive configuration and improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual optimization of parameter value sets is performed, then flexibility and control are maintained, but efficiency and time consumption deteriorate
Solution Approach 1:
The system performs self-optimization by automatically evaluating fitness values of parameter value sets and determining optimal configurations without requiring manual intervention. The optimization algorithm autonomously iterates through parameter combinations, assesses their performance metrics, and implements improvements, enabling the system to service its own optimization needs efficiently
Solution Approach 2:
The patent replaces manual mechanical optimization processes with automated computational algorithms. Instead of human operators manually adjusting parameters and evaluating results, an optimization algorithm systematically explores parameter spaces, calculates fitness values, and determines optimal configurations, substituting human effort with automated computational mechanisms
2Productivity
If automated optimization algorithms are used, then efficiency and productivity improve, but device complexity increases
Solution Approach 1:
The optimization apparatus is designed as a universal system that can handle multiple types of parameters (e.g., software parameters, hardware settings, process variables) and apply various optimization algorithms (e.g., genetic algorithms, simulated annealing, gradient descent) through a single integrated platform. This multi-functional design manages complexity by providing a unified interface and standardized processes for diverse optimization tasks
3Reliability
If comprehensive parameter optimization is performed across all devices, then overall system performance improves, but computational resources and time requirements increase
Solution Approach 1:
The system applies local optimization by identifying and focusing computational resources on specific devices or parameter subsets that would benefit most from optimization. Rather than uniformly optimizing all devices, the apparatus evaluates fitness values and determines which local changes will yield the greatest performance improvement, allocating computational energy efficiently to high-impact areas
Data Source
AI summary
According to an example embodiment of the present invention there is provided an apparatus comprising at least one processing core, at least one memory including computer program code, the at least one memory and the computer program code being configured to, with the at least one processing core, cause the apparatus at least to cause a first parameter value set comprising at least one first parameter value to be provided to a first set of devices, obtain a fitness value associated with the first parameter value set in the first set of devices, determine, based at least in part on the fitness value and an optimization algorithm, at least one second parameter value set comprising at least one second parameter value, and cause the at least one second parameter value set to be provided to the first set or a second set of devices.