Despite the success of metaheuristic algorithms in solving complex network optimization problems, they often struggle with adaptation, especially in dynamic or high-dimensional search spaces. Traditional approaches can become stuck in local optima, leading to inefficient exploration and suboptimal solutions. Most of the widely accepted advanced algorithms do well either on highly complex or smaller search spaces due to the lack of adaptation. To address these limitations, we present ELENA (Epigenetic Learning through Evolved Neural Adaptation), a new evolutionary framework that incorporates epigenetic mechanisms to enhance the adaptability of the core evolutionary approach. ELENA leverages compressed representation of learning parameters improved dynamically through epigenetic tags that serve as adaptive memory. Three epigenetic tags (mutation resistance, crossover affinity, and stability score) assist with guiding solution space search, facilitating a more intelligent hypothesis landscape exploration. To assess the framework performance, we conduct experiments on three critical network optimization problems: the Traveling Salesman Problem (TSP), the Vehicle Routing Problem (VRP), and the Maximum Clique Problem (MCP). Experiments indicate that ELENA achieves competitive results, often surpassing state-of-the-art methods on network optimization tasks.

翻译：尽管元启发式算法在解决复杂网络优化问题方面取得了成功，但其在适应性方面往往存在不足，尤其是在动态或高维搜索空间中。传统方法容易陷入局部最优，导致探索效率低下和解的质量欠佳。由于缺乏适应性，大多数被广泛接受的先进算法要么在高度复杂、要么在较小的搜索空间中表现良好。为了应对这些局限性，我们提出了ELENA（通过进化神经适应的表观遗传学习），这是一种新的进化框架，它融合了表观遗传机制以增强核心进化方法的适应性。ELENA利用学习参数的压缩表示，这些参数通过作为自适应记忆的表观遗传标签动态改进。三种表观遗传标签（突变抗性、交叉亲和力和稳定性分数）有助于指导解空间搜索，促进更智能的假设空间探索。为了评估该框架的性能，我们在三个关键的网络优化问题上进行了实验：旅行商问题（TSP）、车辆路径问题（VRP）和最大团问题（MCP）。实验表明，ELENA取得了具有竞争力的结果，在网络优化任务上常常超越最先进的方法。