FPGA implementation of a cellular univariate estimation of distribution algorithm and block-based neural network as an evolvable hardware

This paper presents a hardware implementation of evolvable block-based neural network (BBNN) amd a kind of EDAs called cellular compact genetic algorithm (CCGA) in FPGA. The CCGA and BBNN have cellular-like and array-like structures which are suitable for hardware implementation. The implemented hardware demonstrates the completely intrinsic online evolution in hardware without software running on microprocessor s. This work contributes to the field of evolvable hardware by proposing CCGA and a layer-based architecture to an integration of BBNN and CCGA as a kind of evolvable hardware. In addition, the proposed CCGA efficiently solves the scalable issues by scaling up to the size of BBNN. The presented approach demonstrates a new kind of evolvable hardware.