Minimising Testing in Genetic Programming

The cost of optimisation can be reduced by evaluating candidate designs on only a fraction of all possible use cases. We show how genetic programming (GP) can avoid overfitting and evolve general solutions from fitness test suites as small as just one dynamic training case. Search effort can be greatly reduced.

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