Search Engine Parameter Tuning via Genetic Algorithm Optimization

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Solution Overview

Problem

Traditional search engines rely on a manual, trial-and-error process to tune search parameter weights, which is inefficient and does not effectively optimize search results based on historical data.

Innovation Solution

The method involves determining a multi-dimensional search parameter space, defining initial populations of weight values, and applying genetic algorithms to select an optimal set of search parameter weights, using a modified evolution step to create new values within a range between parent values, thereby optimizing search results.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual trial-and-error process is used to tune search parameter weights, then the process is simple to implement, but it is inefficient and does not effectively optimize search results

Engineering Contradiction:

Improvesearch parameter tuning efficiencyVSAvoidtuning process complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system performs self-optimization by automatically tuning search parameters using genetic algorithms without requiring manual intervention. The search engine evaluates its own performance metrics and adjusts parameters autonomously, transforming a manual process into a self-service automated system that improves efficiency while managing complexity through algorithmic approaches

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The invention systematically varies search parameter weights through genetic algorithm operations (selection, crossover, mutation) to explore the parameter space. By treating parameters as adjustable variables that evolve over generations, the system efficiently identifies optimal weight configurations that improve search result relevance without exhaustive manual testing

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If genetic algorithms are used to optimize search parameter weights, then search result relevance is improved, but computational cost increases

Engineering Contradiction:

Improvesearch result relevanceVSAvoidcomputational cost

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system applies partial action by using a simplified fitness evaluation that focuses on key performance metrics rather than comprehensive analysis. The genetic algorithm performs sufficient optimization iterations to achieve meaningful parameter tuning while avoiding excessive computational effort through early stopping criteria and selective evaluation of candidate solutions

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The optimization process is segmented into discrete generations with controlled population sizes. By dividing the search parameter space into manageable populations and iterations, the system achieves thorough exploration of parameter combinations while maintaining computational feasibility through structured, incremental optimization steps

Inventive Principle:

Principle #1Segmentation

3Reliability

If comprehensive search parameter space is explored, then optimal weights are found, but tuning time increases

Engineering Contradiction:

Improveoptimality of search parametersVSAvoidparameter tuning time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The genetic algorithm employs periodic action through generational iterations, where populations are evaluated and evolved in discrete cycles. This periodic structure allows systematic exploration of the parameter space while maintaining controlled timing, as each generation represents a fixed computational unit that balances thoroughness with time efficiency

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The system uses feedback from fitness evaluation to guide parameter optimization. Performance metrics from search result analysis feed back into the genetic algorithm, informing selection and mutation operations. This feedback mechanism ensures that tuning efforts are directed toward promising regions of the parameter space, achieving reliable optimization without exhaustive searching

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20230289388A1System and methods for search engine parameter tuning using genetic algorithm

Publication Date: 2023.09.14 HOME DEPOT PRODUCT AUTHORITY LLC

AI summary

A method for operating a search engine may include determining a multi-dimensional search parameter space comprising a set of possible weight values for each of a plurality of search parameters and dividing the search parameter space into a grid of evenly-spaced values that is a subset of the set of possible values. The method may further include defining one or more initial populations of search parameter weight values, wherein each population of search parameter weight values comprises a plurality of initial individuals, wherein each initial individual comprises a respective one of the evenly-spaced values for each of the search parameters. The method may further include executing one or more genetic algorithms based on the one or more initial populations to select a final set of search parameter weight values, and returning results of a user search in the search engine according to the final set of search parameter weight values.