The embryonic architecture, which draws inspiration from the biological process of ontogeny, has built-in mechanisms for self-repair. The entire genome is stored in the embryonic cells, allowing the data to be replicated in healthy cells in the event of a single cell failure in the embryonic fabric. A specially designed genetic algorithm (GA) is used to evolve the configuration information for embryonic cells. Any failed embryonic cell must be indicated via the proposed Built-in Selftest (BIST) the module of the embryonic fabric. This paper recommends an effective centralized BIST design for a novel embryonic fabric. Every embryonic cell is scanned by the proposed BIST in case the self-test mode is activated. The centralized BIST design uses less hardware than if it were integrated into each embryonic cell. To reduce the size of the data, the genome or configuration data of each embryonic cell is decoded using Cartesian Genetic Programming (CGP). The GA is tested for the 1-bit adder and 2-bit comparator circuits that are implemented in the embryonic cell. Fault detection is possible at every function of the cell due to the BIST module’s design. The CGP format can also offer gate-level fault detection. Customized GA and BIST are combined with the novel embryonic architecture. In the embryonic cell, self-repair is accomplished via data scrubbing for transient errors.

The study concentrates on two different genetic programming approaches for determining passenger car equivalent (PCE) values and observing the impact on capacity estimation at urban unsignalized intersections. Considering heterogeneous traffic conditions, a new PCE value is introduced to encompass sustainable modes of public transit vehicles, specifically slow-moving three-wheelers (SM3W), commonly known as E-Rickshaws. Since PCE value is considered an important parameter for capacity calculations, the present study considered 14 unsignalized intersections located in Ranchi city of India. An automatic plate recognition system is employed to have the count of vehicular traffic. The methodologies include age-layered population structure genetic programming (ALPSGP), and the offspring selection genetic programming (OSGP) approach that incorporates static and dynamic variables. Based on the significance test and ranking of the genetic programming (GP) models, the OSGP model is recommended as the most appropriate model for heterogeneous traffic. Sensitivity analysis reported that lagging headway (����) is the most contributing factor in PCE estimation. The PCE value of SM3W is found to be 0.81 and that could be incorporated as a new classification of vehicles in Indo-HCM. It is observed that evaluated capacity based on PCE values of OSGP performed admirably in both normal and congested traffic situations.