标题： ELENA：通过进化神经适应的表观遗传学习 显示英文标题

标题： ELENA: Epigenetic Learning through Evolved Neural Adaptation

摘要： 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，但它们在适应性方面常常面临挑战，尤其是在动态或高维搜索空间中。 传统方法可能会陷入局部最优，导致探索效率低下和次优解。 大多数被广泛接受的先进算法由于缺乏适应性，在高度复杂或较小的搜索空间中表现良好。 为了解决这些限制，我们提出了ELENA（通过进化神经适应进行表观遗传学习），这是一种新的进化框架，结合了表观遗传机制以增强核心进化方法的适应性。 ELENA利用通过表观遗传标记动态改进的学习参数压缩表示作为自适应记忆。 三个表观遗传标记（突变抗性、交叉亲和力和稳定性评分）有助于引导解空间搜索，促进更智能的假设景观探索。 为了评估框架性能，我们在三个关键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取得了有竞争力的结果，通常在网络优化任务中超越了最先进的方法。 显示英文摘要

摘要： 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.