Many automated design approaches require an objective function to determine the quality of a given design. Often, this function depends on a complex relationship between many parameters. Some parameters may be subjective and the relationships difficult to quantify.

This paper presents a method where a neural network is used to evaluate the quality of proposed designs during a genetic algorithm search. In general application of the approach, a human designer would propose candidate designs for a given problem. These candidate designs are used to train a neural network fitness function. Then the genetic algorithm evolves new designs that the human designer might not conceive. In this way, the proposed approach would aid in the brainstorming process.

The method is applied to the genetic design of modular robots for planetary exploration. This application is briefly described and the genetic design method is summarized. Then the neural network structure is explained and the training method is detailed. Finally, the neural network is used with the genetic design method to create a robot for a specific task.

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