An improved genetic algorithm for designing optimal temporal patterns of neural stimulation.

Journal Article (Journal Article)


Electrical neuromodulation therapies typically apply constant frequency stimulation, but non-regular temporal patterns of stimulation may be more effective and more efficient. However, the design space for temporal patterns is exceedingly large, and model-based optimization is required for pattern design. We designed and implemented a modified genetic algorithm (GA) intended for design optimal temporal patterns of electrical neuromodulation.


We tested and modified standard GA methods for application to designing temporal patterns of neural stimulation. We evaluated each modification individually and all modifications collectively by comparing performance to the standard GA across three test functions and two biophysically-based models of neural stimulation.

Main results

The proposed modifications of the GA significantly improved performance across the test functions and performed best when all were used collectively. The standard GA found patterns that outperformed fixed-frequency, clinically-standard patterns in biophysically-based models of neural stimulation, but the modified GA, in many fewer iterations, consistently converged to higher-scoring, non-regular patterns of stimulation.


The proposed improvements to standard GA methodology reduced the number of iterations required for convergence and identified superior solutions.

Full Text

Duke Authors

Cited Authors

  • Cassar, IR; Titus, ND; Grill, WM

Published Date

  • December 2017

Published In

Volume / Issue

  • 14 / 6

Start / End Page

  • 066013 -

PubMed ID

  • 28747582

Pubmed Central ID

  • PMC5730972

Electronic International Standard Serial Number (EISSN)

  • 1741-2552

International Standard Serial Number (ISSN)

  • 1741-2560

Digital Object Identifier (DOI)

  • 10.1088/1741-2552/aa8270


  • eng