NegaBent, No Regrets: Evolving Spectrally Flat Boolean Functions
Abstract
Negabent Boolean functions are defined by having a flat magnitude spectrum under the nega-Hadamard transform. They exist in both even and odd dimensions, and the subclass of functions that are simultaneously bent and negabent (bent-negabent) has attracted interest due to the combined optimal periodic and negaperiodic spectral properties. In this work, we investigate how evolutionary algorithms can be used to evolve (bent-)negabent Boolean functions. Our experimental results indicate that evolutionary algorithms, especially genetic programming, are a suitable approach for evolving negabent Boolean functions, and we successfully evolve such functions in all dimensions we consider.
Fields of Study & Subjects
Evolutionary Algorithms and Applications
Coding theory and cryptography
Metaheuristic Optimization Algorithms Research
Publication details
- Journal / source
- Genetic and Evolutionary Computation Conference Companion (GECCO Companion)
- Pages
- 589–592
- Publication date
- 13 July 2026
- Language
- EN