Symbolic Programming for Machine Learning Algorithm Search

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Solution Overview

Problem

Existing machine learning algorithms are inefficient in identifying high-performing algorithms due to complex and unstructured search spaces, leading to high development costs and limited scalability in Automated Machine Learning (AutoML) applications.

Innovation Solution

A system that treats neural architecture search as a control flow primitive, using symbolic programming to manipulate symbols in the search space, allowing for efficient identification of high-performing machine learning algorithms through a simplified search process and reusable search algorithms.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional machine learning algorithms are used to search for high-performing algorithms, then the search process becomes complex and unstructured, but this leads to high development costs and limited scalability

Engineering Contradiction:

Improveidentification accuracy of high-performing algorithmsVSAvoidsearch space complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical search algorithms with a symbolic programming system that uses formal logic and mathematical transformations to navigate the search space. This substitution enables more efficient and systematic exploration of algorithm configurations, reducing complexity while maintaining reliability in identifying high-performing algorithms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system dynamically adjusts search parameters and transforms the search space representation to optimize the identification process. By changing parameters such as search depth, evaluation metrics, and algorithm configurations, the system efficiently navigates complex search spaces without proportionally increasing development costs.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If complex search methods are introduced to improve algorithm identification, then identification accuracy improves, but development costs and implementation complexity increase

Engineering Contradiction:

Improvealgorithm identification accuracyVSAvoiddevelopment cost

Core Design Contradiction:

ReliabilityVSEase of manufacture

Solution Approach 1:

The symbolic programming system implements a universal framework that can handle multiple search algorithms and configurations through a single unified interface. This multi-functionality allows the system to achieve high identification accuracy without requiring separate development efforts for each search method, thereby reducing overall development costs.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system uses symbolic representations and abstract models to copy and simulate complex search processes without requiring full implementation of each search method. This allows accurate algorithm identification while avoiding the high development costs associated with implementing and maintaining multiple complex search systems.

Inventive Principle:

Principle #26Copying

3Adaptability or versatility

If traditional search approaches are used, then implementation is simpler, but scalability and adaptability to new tasks are limited

Engineering Contradiction:

Improvescalability of AutoML applicationsVSAvoidsearch process structure

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments the search process into modular symbolic operations that can be independently configured and combined. This segmentation enables the system to scale to new tasks by assembling appropriate search components without requiring complete redesign, thereby improving adaptability while managing complexity through modular organization.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12443882B2Machine learning algorithm search with symbolic programming

Publication Date: 2025.10.14 GOOGLE LLC

AI summary

A method for searching for an output machine learning (ML) algorithm to perform an ML task is described. The method comprising: receiving data specifying an input ML algorithm; receiving data specifying a search algorithm that searches for candidate ML algorithms and an evaluation function that evaluates the performance of candidate ML algorithms; generating data representing a symbolic tree from the input ML algorithm; generating data representing a hyper symbolic tree from the symbolic tree; searching an algorithm search space that defines a set of possible concrete symbolic trees from the hyper symbolic tree for candidate ML algorithms and training the candidate ML algorithms to determine a respective performance metric for each candidate ML algorithm; and selecting one or more trained candidate ML algorithms among the trained candidate ML algorithms based on the determined performance metrics.