Evolvable hardware (EHW) is an emerging area of research that uses evolutionary algorithms (EAs) to construct circuits without manual intervention. However, this technique confronts two major issues: the evolution efficiency of structures and the computational efficiency of EAs. To address these issues, we construct a novel virtual reconfigurable circuit (VRC) based on artificial neural network (ANN) architecture and develop a promising EA based on improved simulated annealing (ISA) for the research of EHW. Here, ISA escapes the local optimization through an inner loop and expands new search space through an outer loop. Furthermore, several strategies are proposed for the representation, perturbation and selection of solutions (individuals) to reduce the computational burden throughout the entire evolution process. The use of an Intel Cyclone V field programmable system-on-chip (FPSoC) further accelerates the execution of the algorithm and provides designers with much more high-level processing power than that provided by soft-cores. The obtained results show that the proposed method achieves high-speed processing and improves computational efficiency. The results also show that the method has good flexibility and scalability. INDEX TERMS Evolvable hardware, artificial neural networks, improved simulated annealing, algorithm design and analysis, FPSoC.