NEVA: A Neuromorphic Evolutionary Algorithm
International Conference on Optimization and Learning (OLA) 2025, Dubai, United Arab Emirates
Abstract
WNeuromorphic computing (NC) introduces a novel paradigm called Spiking Neural Networks (SNNs), representing a major shift from traditional digital computing. NC leverages spiking neurons, adaptive synapses, event-driven processing, and biologically-inspired learning mechanisms to develop efficient, brain-like systems optimized for real-time, parallel processing and low power consumption.
In this paper, we propose an algorithm that integrates the principles of evolutionary algorithms (EAs) with NC to create efficient and energy-aware metaheuristics. The proposed neuromorphic EA (NEVA) has been mapped on a SNN which involves defining the neuron model, information encoding, network architecture, and learning rules. To our knowledge this is the first EA designed using the NC paradigm. Computational experiments on QUBO, 3-SAT, and knapsack problems show the efficiency of the proposed NEVA algorithm. By designing a neuromorphic memetic algorithm that combines EAs with local search, the results have been improved both in terms of solution quality and search time.