ELENA: Epigenetic Learning through Evolved Neural Adaptation

TL;DR

ELENA introduces an epigenetic-inspired evolutionary framework that enhances adaptability in solving complex network optimization problems, outperforming many existing methods by dynamically guiding search processes.

Contribution

The paper presents ELENA, a novel evolutionary algorithm incorporating epigenetic mechanisms for improved adaptability in complex optimization tasks.

Findings

ELENA achieves competitive results on TSP, VRP, and MCP.

ELENA often surpasses state-of-the-art methods.

Epigenetic tags improve search guidance and solution quality.

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. 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.