Abstract
We propose Neural Estimation of Interaction Outcomes (NEIO), a method that reduces the number of required interactions between candidate solutions and tests in test-based problems. Given the outcomes of a random sample of all solution-test interactions, NEIO uses a neural network to predict the outcomes of remaining interactions and so estimate the fitness of programs. We apply NEIO to genetic programming (GP) problems, i.e. test-based problems in which candidate solutions are programs, while tests are examples of the desired input-output program behavior. In an empirical comparison to several reference methods on categorical GP benchmarks, NEIO attains the highest rank on the success rate of synthesizing correct programs.
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