Revisiting motif finding: do bi-objective metaheuristics surpass single-objective metaheuristics?

ABSTRACT:

Background

The discovery of DNA motifs is essential for studying gene expression and function in many biological systems. Most existing algorithms for motif detection rely on a single optimization criterion or objective function. This study formulates motif finding as a bi-objective optimization problem and investigates whether multi-objective metaheuristics offer potential advantages over single-objective approaches.

Results

We developed four variants of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) incorporating simple, problem-specific genetic operators. Experiments on six benchmark datasets from three organisms demonstrate that our bi-objective approach significantly outperforms the state-of-the-art Artificial Bee Colony (ABC) metaheuristic. Remarkably, NSGA-I

SUBMITTER: Nayeem MA 

PROVIDER: S-EPMC12713252 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature