In this paper, the effect of developmental plasticity is investigated in Cartesian Genetic Programming (CGP); an evolutionary algorithm that uses a directed graph to represent its genetic architecture. Developmental Plasticity is the adaptability of an organism to change in its surrounding environment. A Developmental Output is used to computationally develop the phenotype that has already been passed through a genetic evolution. To manifest the idea of developmental plasticity in the form of digital circuits, binary multiplexing functions are used in the CGP implementation. Two experiments-prime number test and image recognition test-are conducted so that to analyze the effect of Developmental Plasticity in CGP. Simulation results demonstrate that the plasticity based CGP achieves better performance when compared to conventional CGP in terms of its adaptability and learning in general. Keywords-developmental plasticity, genetic programming, machine learning, computational developmentI.