Cartesian Genetic Programming (CGP) is a graph-based evolutionary representation in which candidate solutions are encoded as directed acyclic grid of computational nodes. In standard CGP, only the output of the full graph is considered for fitness evaluation, although all intermediary (active) node outputs are computed during execution. We introduce Iterative Subgraph Assessment CGP (ISA-CGP), a straightforward extension that treats every active node output as a potential solution: during each individual’s evaluation, all subgraph outputs are assessed alongside the full graph, and the best-performing expression is selected. Favourably, in Symbolic Regression (SR) fitness measurements are usually inexpensive. To validate ISA-CGP, we conduct experiments on eight benchmark problems drawn from the Feynman symbolic regression suite, comparing convergence speed, final model error, and computational effort against standard CGP. Experimental results on the eight Feynman problems demonstrate that ISA-CGP converges more rapidly and attains superior fitness values in most cases. Furthermore, ISA-CGP creates smaller solution programs, indicating less bloated phenotypes which saves computational efforts. These findings suggest that ISA-CGP offers a simple yet effective enhancement to CGP, achieving faster search and better solutions with minimal overhead.