ELENA: Epigenetic Learning through Evolved Neural Adaptation
This work addresses adaptation issues in metaheuristic algorithms for network optimization, which is incremental as it builds on existing evolutionary approaches with epigenetic enhancements.
The paper tackles the problem of metaheuristic algorithms struggling with adaptation in dynamic or high-dimensional search spaces by introducing ELENA, an evolutionary framework that incorporates epigenetic mechanisms, achieving competitive results that often surpass state-of-the-art methods on network optimization tasks like TSP, VRP, and MCP.
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.