Table 1 Experimental settings and parameter values for EAs.

Population size (\(\mu\))

The size of the population is set to 100, following the recommended practice of ensuring a sufficiently large and diverse population for effective evolutionary search. A larger population helps explore the search space comprehensively.

Maximum number of generations

The maximum number of generations is predetermined as 100, equivalent to a total of 10,000 function evaluations. This setting controls the termination condition of the evolutionary process, ensuring a finite and bounded search.

Uniform crossover probability (pc)

The probability of applying uniform crossover is fixed at a value of 0.8. This reflects a preference for a higher likelihood of generating offspring with well-balanced genetic information inherited from both parents, promoting exploration and exploitation in the search space.

Mutation probability (\(p_m\))

The mutation operator, responsible for introducing diversity and facilitating exploration of unexplored regions in the search space, is assigned a probability represented by \(p_m\), specifically set to 0.2. This setting controls the likelihood of mutation occurring in each generation.

Proposed heuristic GO-based (\(p_m\))

A proposed mutation operator based on Gene Ontology (GO) is incorporated into the algorithms, also assigned a probability of \(p_m=0.2\). This specialized mutation operator aims to inject domain-specific knowledge into the search process.

Evaluation metrics

The evaluation metrics discussed in Section  “Evaluation measures” are rigorously analyzed and reported based on the average results obtained from conducting 30 independent runs. This approach of averaging outcomes across multiple runs provides a comprehensive and robust assessment of the algorithms’ performance, ensuring that the reported results are statistically significant and representative of their overall effectiveness in finding optimal solutions.