Genetic Algorithm Pattern Design System
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Solution Overview
Problem
Designing ornamental patterns is challenging due to the complexity of procedural models, which require users to learn specific languages, making it difficult to control and modify these models efficiently.
Innovation Solution
A system that allows users to select predefined patterns, modify procedural models, and generate new patterns through genetic exploration methods, enabling users to create and apply patterns to image areas without needing extensive knowledge of procedural modeling languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users directly modify procedural models to design ornamental patterns, then pattern customization capability is improved, but user operation complexity increases significantly
Solution Approach 1:
The patent introduces a genetic algorithm as an intermediary between the user and the procedural model. Instead of users directly modifying complex procedural code, they interact with a simplified interface that translates user preferences into genetic algorithm parameters. The genetic algorithm then automatically evolves and optimizes the procedural model to generate desired patterns, effectively mediating the interaction and reducing operational complexity while maintaining customization capability.
Solution Approach 2:
The system enables self-service by allowing the genetic algorithm to autonomously optimize pattern designs based on user-selected criteria. Once users define their preferences (such as pattern density, complexity, or motif types), the genetic algorithm automatically iterates through generations of procedural models, evaluating and refining them without requiring users to manually adjust technical parameters or understand procedural modeling languages.
2Manufacturing precision
If procedural models are made more complex to enable detailed pattern control, then pattern design precision is improved, but model design difficulty increases
Solution Approach 1:
The patent replaces the manual mechanical process of procedural model design with an automated evolutionary system. Instead of users manually constructing and debugging complex procedural models to achieve precise pattern control, the genetic algorithm automatically evolves procedural models through selection, crossover, and mutation operations. This substitution of the design mechanism maintains high pattern design precision while dramatically reducing model design difficulty.
Solution Approach 2:
The system manages complexity by dynamically adjusting genetic algorithm parameters (such as population size, mutation rate, and selection pressure) based on the design stage and user preferences. During early exploration phases, higher mutation rates encourage diverse pattern generation, while during refinement phases, lower mutation rates and increased selection pressure enhance pattern precision. This adaptive parameter adjustment allows the system to maintain design precision without requiring users to manage complex model structures.
3Adaptability or versatility
If users manually create procedural models from scratch, then unique pattern designs are achieved, but design time increases
Solution Approach 1:
The patent implements preliminary action by pre-defining a library of procedural model templates and genetic algorithm operators before the actual design process. These pre-configured elements include common pattern motifs, procedural structures, and evolution rules that have been optimized in advance. When users initiate a design task, the system starts from these prepared templates rather than from scratch, significantly reducing design time while still allowing unique patterns to emerge through genetic evolution and user customization.
Solution Approach 2:
The system utilizes copying by replicating and recombining successful procedural model segments across different generations and design instances. The genetic algorithm identifies high-performing procedural patterns and copies them into new designs, accelerating the design process. Users can also copy and modify existing patterns from the library or previous designs, maintaining uniqueness through customization while reducing the time required to create new patterns from scratch.
Data Source
AI summary
Selection of an area of an image can be received. Selection of a subset of a plurality of predefined patterns may be received. A plurality of patterns can be generated. At least one generated pattern in the plurality of patterns may be based at least in part on one or more predefined patterns in the subset. Selection of another subset of patterns may be received. At least one pattern in the other subset of patterns may be selected from the plurality of predefined patterns and/or the generated patterns. Another plurality of patterns can be generated. At least one generated pattern in this plurality of patterns may be based at least on part on one or more patterns in the other subset. Selection of a generated pattern from the generated other plurality of patterns may be received. The selected area of the image may be populated with the selected generated pattern.