High level comparison between genetic algorithms vs neural networks:
“a genetic algorithms are search-based optimization algorithms used to find optimal or near-optimal solutions for search problems and optimization problems. Neural networks, on the other hand, are mathematical models that map between complex inputs and outputs. They can classify elements that are not previously known.
Genetic algorithms usually perform well on discrete data, whereas neural networks usually perform efficiently on continuous data.
Genetic algorithms can fetch new patterns, while neural networks use training data to classify a network.
A genetic algorithm doesn’t always require derivative information to solve a problem.
Genetic algorithms calculate the fitness function repeatedly to get a good solution. That’s why it takes a good amount of time to compute a reasonable solution. Neural networks, in general, take much less time for the classification of new input.”