Table 1 A summary of related literature

Dao et al. [4]

Global optimization

Adaptive restart and elite chromosomes in genetic algorithm

Performed better than the benchmark approaches

Population size modification is only at a restart; a new population may not guarantee the early achievement of global optima

Potuzak [3]

Road traffic network load balancing

Distributed genetic algorithm computation and traffic division

Reduced computation time reported

Suffers from local optima challenge

Potuzak [28]

Road traffic network load balancing

Distributed genetic algorithm and graph coarsening

Reduced computation time reported

Suffers from local optima challenges as well as spectral and cut guarantees challenges associated with graph coarsening

Luis et al. [13, 14]

Throughput: traffic flow enhancement

Standard genetic algorithm and cellular automata simulations

Achieved between 9.21 and 36.98% throughput improvement

Associated challenges of standard genetic algorithms

Basak [29]

Minimize the effect of a constant control operator

Adaptive mutation approach in genetic algorithm

Better performance reported

Focused on a single parameter of the genetic algorithm

Muzid [30]

Global optimization

Fuzzy logic approach to determining crossover and mutation probability

Better results compared to the standard genetic algorithm technique

The choice of population size and boundaries remain a challenge. Non-adaptability of fuzzy logic is a problem

Villalba-morales and Ramírez-echeverry [31]

Steel trusses optimization in three-dimensional space

Multi-chromosome and self-adaptive parameters

35% weight minimization achieved

Hamming cliffs, uninformed precision, and uneven schema importance are challenges associated with binary-coded genetic algorithms

Mao et al. [15]

Traffic control optimization

Combined genetic algorithm and machine learning regression

Reported 43% minimization of average waiting time

Suffers from computational complexity and requires that the machine learning regression model be adequately trained

Al-Madi and Hnaif [32]

Minimizing congestion and duration

Human community-based genetic algorithm

Achieved 83% and 13% in congestion and duration minimization

Possibility and impact of ineffective modeling of problem constraints