Elena: epigenetic learning through evolved neural adaptation

Abstract 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. While recent approaches have attempted to address adaptability through various mechanisms, they typically require problem-specific tuning or sacrifice generalization capability for performance, failing to maintain consistent efficiency across different optimization tasks and varying problem scales. 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’s 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.