SHENZHEN XUMI YUNTU SPACE TECHNOLOGY CO., LTD. (20240330547). LAYOUT METHOD AND APPARATUS BASED ON GENETIC ALGORITHM simplified abstract
Contents
LAYOUT METHOD AND APPARATUS BASED ON GENETIC ALGORITHM
Organization Name
SHENZHEN XUMI YUNTU SPACE TECHNOLOGY CO., LTD.
Inventor(s)
LAYOUT METHOD AND APPARATUS BASED ON GENETIC ALGORITHM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240330547 titled 'LAYOUT METHOD AND APPARATUS BASED ON GENETIC ALGORITHM
The patent application describes a layout method and apparatus based on a genetic algorithm for optimizing layout schemes.
- The method involves determining a gene code mode based on standard part information and layout part information.
- An initial population is generated based on the gene code mode, including a variety of gene codes with standard code segments and layout code segments.
- The fitness of each gene code is evaluated to determine a dominant gene code.
- Double-point crossing and mutation operations are performed on the dominant gene code to generate a next generation gene code.
- The process continues until a termination condition is met, at which point the layout scheme corresponding to the dominant gene code is selected as the target layout scheme.
Potential Applications: - Circuit design optimization - Industrial manufacturing processes - Urban planning and design
Problems Solved: - Efficient layout optimization - Reduction of manual design efforts - Improved utilization of space and resources
Benefits: - Faster and more accurate layout design - Cost savings in design processes - Enhanced productivity and efficiency
Commercial Applications: Title: Genetic Algorithm Layout Optimization for Industrial Manufacturing This technology can be applied in industries such as electronics, automotive, and architecture to streamline design processes, reduce costs, and improve overall efficiency.
Prior Art: Researchers in the field of evolutionary algorithms and optimization techniques have explored similar methods for layout optimization in various applications. Further investigation into genetic algorithms and their specific applications in layout design can provide valuable insights.
Frequently Updated Research: Ongoing research in genetic algorithms and optimization methods continues to advance the field of layout design, with new algorithms and techniques being developed to address specific challenges in different industries.
Questions about Genetic Algorithm Layout Optimization: 1. How does the genetic algorithm approach differ from traditional layout design methods? The genetic algorithm approach in layout optimization involves evolving solutions based on genetic principles such as selection, crossover, and mutation, which can efficiently explore a large solution space and find optimal layouts. 2. What are the key factors that influence the performance of the genetic algorithm in layout optimization? The performance of the genetic algorithm in layout optimization can be influenced by parameters such as population size, crossover and mutation rates, and the selection mechanism, which determine the balance between exploration and exploitation in the search for optimal layouts.
Original Abstract Submitted
a layout method and apparatus based on a genetic algorithm are provided. the method includes: determining a gene code mode based on standard part information and layout part information; generating an initial population based on the gene code mode, the initial population including a plurality of gene codes, and the gene codes including standard code segments and layout code segments, and corresponding to layout schemes of standard parts and layout parts; acquiring fitness of each gene code; determining a dominant gene code based on the fitness; performing a double-point crossing operation and a double-point mutation operation on the dominant gene code to generate a next generation gene code, so as to form a dominant population; and if a preset termination condition is met, determining the layout scheme corresponding to the dominant gene code in the dominant population as a target layout scheme.
