ELENA: Epigenetic Learning through Evolved Neural Adaptation

Optimization of complex networks is one of the fundamental challenges in computer science research. With the progression of computational resources availability, a great variety of conceptually different algorithms have been presented over the past decades to achieve competitive results in the domain of network optimization. Many approaches, such as Lin-Kernighan-Helsgaun heuristic [1], Genetic Algorithm variations [2,3,4], Ant Colony Optimization [5], k-opt local search [6,7] with sequential improvements have gained acknowledgment from both research community and industry across logistics, telecommunications, and biotechnology verticals. The Traveling Salesman Problem (TSP) [8], first formalized by Karl Menger in 1930, remains a cornerstone problem that has driven network optimization algorithmic innovations for decades. The Vehicle Routing Problem (VRP) [9,10], introduced by Dantzig and Ramser in 1959, extends TSP's complexity by incorporating multiple vehicles and capacity constraints, finding direct applications in logistics and delivery. The Maximum Clique Problem (MCP) [11], important for social network analysis, computational biochemistry and wireless network allocation, focuses on finding the largest complete subgraph within a network.