  Neuro-encoded expression programming(NEEP) that aims to offer a novel
continuous representation of combinatorial encoding for genetic programming
methods is proposed in this paper. Genetic programming with linear
representation uses nature-inspired operators (e.g., crossover, mutation) to
tune expressions and finally search out the best explicit function to simulate
data. The encoding mechanism is essential for genetic programmings to find a
desirable solution efficiently. However, the linear representation methods
manipulate the expression tree in discrete solution space, where a small change
of the input can cause a large change of the output. The unsmooth landscapes
destroy the local information and make difficulty in searching. The
neuro-encoded expression programming constructs the gene string with recurrent
neural network (RNN) and the weights of the network are optimized by powerful
continuous evolutionary algorithms. The neural network mappings smoothen the
sharp fitness landscape and provide rich neighborhood information to find the
best expression. The experiments indicate that the novel approach improves
training efficiency and reduces test errors on several well-known symbolic
regression problems.
