cellular genetic algorithm:my first algorithm creation

作者在九月初构思出一种名为细胞遗传算法的进化算法,并着手实现。尽管发现已有类似研究,但其算法的独特进化规则展现出更高效率。面对研究成果被大学校长抢先发表的挑战,作者仍坚持追寻由Jeff Hoskin提出的真正智能。

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    Early September this year,I thought out a kind of evolutionary algorithm which I called it cellular genetic algorithm,

and I expressed my idea to my teacher.He urged me to realize it with program because there may be someone else who was working on it.

    when I searched on the Internet ,I found there was one,only one peridical paper mentioned the similar idea.Also the author called his algorithm cellular algorithm.After I read his paper carefully,I found his rules for cell evolution were not as effective as mine.So I thought it was still useful to work on it.But at that time I was working on  a parrallel software .so the work was done .but two weeks ago ,when I read periodical papers,I found the  former author issued another paper. In the paper,he concluded  kinds of cell evolution rules,including mine.

 

    can you understand the feeling When I read the guy's paper.

   when I looked at the name of the author ,I found it was my university president!

 

    but it's nothing,for my dream is to realize true intelligence proposed by Jeff Hoskin

 

 

    if you are doing research on genetic algorithm and wanna learn more about this cellular genetic algorithm,contact with onezeros@yahoo.cn

I'll send you some valuable materials about this algorithm

   

 

内容概要:该论文聚焦于T2WI核磁共振图像超分辨率问题,提出了一种利用T1WI模态作为辅助信息的跨模态解决方案。其主要贡献包括:提出基于高频信息约束的网络框架,通过主干特征提取分支和高频结构先验建模分支结合Transformer模块和注意力机制有效重建高频细节;设计渐进式特征匹配融合框架,采用多阶段相似特征匹配算法提高匹配鲁棒性;引入模型量化技术降低推理资源需求。实验结果表明,该方法不仅提高了超分辨率性能,还保持了图像质量。 适合人群:从事医学图像处理、计算机视觉领域的研究人员和工程师,尤其是对核磁共振图像超分辨率感兴趣的学者和技术开发者。 使用场景及目标:①适用于需要提升T2WI核磁共振图像分辨率的应用场景;②目标是通过跨模态信息融合提高图像质量,解决传统单模态方法难以克服的高频细节丢失问题;③为临床诊断提供更高质量的影像资料,帮助医生更准确地识别病灶。 其他说明:论文不仅提供了详细的网络架构设计与实现代码,还深入探讨了跨模态噪声的本质、高频信息约束的实现方式以及渐进式特征匹配的具体过程。此外,作者还对模型进行了量化处理,使得该方法可以在资源受限环境下高效运行。阅读时应重点关注论文中提到的技术创新点及其背后的原理,理解如何通过跨模态信息融合提升图像重建效果。
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