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Hyperparameter optimisation in differential evolution using Summed Local Difference Strings, A Rugged But Easily Calculated Landscape For Combinatorial Search Problems

Husanbir Singh Pannu¹ and Douglas B. Kell²

1. Department of Computer Science and Engineering, Thapar Institute
   Patiala India 147004
   hspannu@thapar.edu
2. Research Chair in Systems Biology / Director of GeneMill
   University of Liverpool UK L69 3BX
   Douglas.Kell@liverpool.ac.uk

Abstract

We analyse the effectiveness of differential evolution hyperparameters in large-scale search problems, i.e. those with very many variables or vector elements, using a novel objective function that is easily calculated from the vector/string itself. The objective function is simply the sum of the differences between adjacent elements. For both binary and real-valued elements whose smallest and largest values are min and max in a vector of length N, the value of the objective function ranges between 0 and (N-1) × (max-min) and can thus easily be normalised if desired. String length, population size and generations for computational iterations have been studied. Finally, a neural network is trained by systematically varying three hyper-parameters, viz population (NP), mutation factor (F) and crossover rate (CR), and two output target variables are collected (a) median (b) maximum cost function values from 10-trial experiments and compared with SMAC3 and OPTUNA against grid and random search.

Key words

Rugged landscape, differential evolution, neural networks, machine learning, optimization

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS240628061P

Publication information

Volume 22, Issue 4 (September 2025)
Year of Publication: 2025
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Pannu, H. S., Kell, D. B.: Hyperparameter optimisation in differential evolution using Summed Local Difference Strings, A Rugged But Easily Calculated Landscape For Combinatorial Search Problems. Computer Science and Information Systems, Vol. 22, No. 4, 1555-1575. (2025), https://doi.org/10.2298/CSIS240628061P