In this paper, we propose a systematic design of evolutionary optimization, namely Multimodal Memetic Framework (MMF), to effectively search the vast complex energy landscape. Our proposed memetic framework is implemented in hierarchical stages with the optimization of each stage performed in parallel in three different states: Exploratory, Exploitative and Central. Each state, with its own set of sub-populations, either explores or exploits by beneficial mixing of potential solutions to direct the search towards a global solution. Instead of implementing identical genetic operators, the proposed approach employs different selection and survival criteria in each state according to their designated task. The Exploratory state employs a knowledge-based initial population generation technique with appropriately tuned genetic operators to guide the search to the “nearest peak”. The Exploitative state fine-tunes the individuals representing different regions by applying a building block based local search. Finally, by utilizing the imbibed knowledge from different peaks, the Central state carries out information-exchange among the highly fit solutions for exploring the undiscovered regions. The information exchange employs a novel non-random parental selection technique to distribute the reproduction opportunity intelligently among the individuals for making cross-over more effective. The method has been tested on a set of various benchmark protein sequences for 2D and 3D lattice models. The experimental results demonstrate the superiority of the proposed method over other state-of-the-art algorithms.

在本文中，我们提出了一种进化优化的系统设计，即多峰模因框架（MMF），以有效地搜索广阔的复杂能源格局。我们提出的模因框架是在分层阶段实现的，每个阶段的优化都在三种不同状态下并行执行：探索性，开发性和中央性。每个州都有自己的子人口集，可以通过有益地混合潜在解决方案来进行探索或利用，以将搜索引导至全球解决方案。提议的方法不是实施相同的遗传算子，而是根据其指定任务在每个州采用不同的选择和生存标准。探索州采用基于知识的初始种群生成技术，并通过适当调整的遗传算子将搜索引导到“最近的高峰”。开发性状态通过应用基于构建块的本地搜索来微调代表不同区域的个体。最后，通过利用来自不同峰值的吸收知识，中央国家在高度适合的解决方案之间进行信息交换，以探索未发现的地区。信息交换采用一种新颖的非随机父母选择技术，在个体之间智能地分配繁殖机会，从而使交叉更有效。该方法已针对2D和3D晶格模型在一组各种基准蛋白质序列上进行了测试。