Hyperparameter Optimizer Using Ranked Genetic Search
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Solution Overview
Problem
Existing hyperparameter optimization techniques, such as grid searching, are inefficient as they do not prioritize the most important hyperparameters, leading to excessive compute time and resources, resulting in poorly optimized machine learning models.
Innovation Solution
A hyperparameter optimizer that utilizes a ranked list of hyperparameters by importance, using genetic or Bayesian algorithms to prioritize the most influential hyperparameters, generating multiple copies of ML models with different hyperparameter combinations and optimizing them simultaneously to identify the most accurate combination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If grid searching is used to optimize hyperparameters, then all possible hyperparameter combinations are tested, but compute time and resources become excessive
Solution Approach 1:
The patent segments the hyperparameter optimization process into two distinct phases: a coarse search phase that samples a limited number of hyperparameter combinations to identify promising regions, and a fine search phase that performs detailed optimization only in those identified regions. This segmentation avoids exhaustive testing of all combinations while still finding optimal hyperparameters, thereby reducing compute time while maintaining optimization accuracy.
Solution Approach 2:
The patent performs preliminary action by conducting a coarse search before the fine search. The coarse search preliminarily identifies promising hyperparameter regions that warrant detailed investigation, filtering out unpromising combinations early. This preliminary filtering action prevents wasting computational resources on poor hyperparameter combinations during the more expensive fine search phase.
2Manufacturing precision
If all hyperparameter combinations are tested, then the most accurate model can be found, but developer effort and resources are excessive
Solution Approach 1:
The patent segments the hyperparameter search space into multiple regions and uses parallel processing to simultaneously evaluate different regions. Multiple processors or computing nodes work in parallel on different segments of the search space, reducing the wall-clock time required to evaluate numerous hyperparameter combinations while maintaining thoroughness in the search.
Solution Approach 2:
The patent applies partial action by testing a sufficient but not exhaustive number of hyperparameter combinations. The coarse search tests a limited subset of combinations strategically selected to cover the search space, and the fine search then performs detailed optimization only on the most promising candidates. This partial testing approach achieves adequate model accuracy without the excessive developer effort required for exhaustive testing.
3Manufacturing precision
If multiple hyperparameters are optimized simultaneously, then comprehensive optimization is achieved, but compute resources are overwhelmed
Solution Approach 1:
The patent segments the optimization process into coarse search and fine search phases, each with different levels of computational intensity. The coarse search uses simpler evaluation to quickly filter combinations, while the fine search performs more complex optimization only on selected candidates. This temporal segmentation of optimization quality requirements reduces peak computational complexity while maintaining overall optimization quality.
Solution Approach 2:
The patent performs partial optimization by focusing computational resources on optimizing a subset of hyperparameters in detail (fine search) rather than exhaustively optimizing all hyperparameters to the same degree. The coarse search provides a preliminary optimization for all hyperparameters, and the fine search performs excessive (detailed) optimization only for the most critical hyperparameters in promising regions, achieving comprehensive optimization quality with reduced computational complexity.
Data Source
AI summary
Various embodiments are generally directed to techniques for optimizing hyperparameters, such as optimizing different combinations of hyperparameters, for instance. Some embodiments are particularly directed using a genetic or Bayesian algorithm to identify and optimize different combinations of hyperparameters for a machine learning (ML) model. Many embodiments construct a search using a genetic algorithm that prioritizes the most important hyperparameters in influencing model performance.