Evolutionary Algorithms & Neural Networks: Two Paths Towards Intelligent Engineering

There have been various AI algorithms developed to achieve intelligent engineering. Among these, two major computational paradigms are evolutionary algorithms and neural networks. Evolutionary algorithms are inspired by natural biological evolution. They search through a population of solutions and then choose and keep the ones that work well, thereafter creating new variations through crossover and mutation; finally, they refine the solutions incrementally according to fitness evaluation. On the other hand, neural networks are trained on input data, learn complex relationships between the data, and predict outputs based on the patterns they have learned.

These two algorithms have different functions in research methodology. Evolutionary algorithms are used for searching and optimization, while neural networks are used for learning and prediction. This means that when exploring entirely new designs, evolutionary algorithms are used to explore engineering solutions, while when predicting the direction or trend of an event or project, neural networks are used to analyze existing knowledge to more quickly analyze relationships between events or to complete performance evaluations.

As can be seen from the above, these two are not in competition but rather are compatible. Although both algorithms have their own drawbacks (computational cost, data dependency), the results obtained by using these algorithms will create more efficient and adaptive design systems if the data is based on complete and high-quality experimental/simulation results.

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