Table 9.

Advantages and disadvantages of evolutionary algorithms.

Algorithm	Advantages	Disadvantages
Genetic Algorithm (GA)	convenient for a wide range of problems good for both discrete and continuous variables global search capabilities enables exploration of a diverse search space	high computational cost computational cost scales with problem complexity often needs additional techniques to handle constraints
Memetic Algorithm (MA)	combines global search with local search (meme) adaptable and customizable can incorporate problem-specific knowledge effective constraint handling fast convergence due to enhanced local search	complex algorithm non-trivial and problem-specific design difficult parameter tuning computationally complex and demanding may become stuck in a local optimum
Differential Evolution (DE)	good for continuous data low algorithm complexity minimal number of control parameters for tuning usable for noisy and non-differentiable fitness functions robust user-friendly	poor for discrete data performs worse with upscaled search spaces may become stuck in local optima lack of population diversity sensitive to control parameter tuning lower convergence speed