A neurocomputing model for real coded genetic algorithm with the minimal generation gap

Abstract

This paper proposes using neural networks (NN) to implement a real coded genetic algorithm (GA) with the center of gravity crossover (CGX) and the minimal generation gap (MGG) model. With all genetic operations of GA including selection, crossover, mutation and evaluation implemented with NN modules, this approach can realize in parallel genetic operations on the whole chromosome to achieve the maximum parallel realization potential of the MGG model of the GA. At the same time expensive hardware for field programmable gate arrays (FPGA) and the high speed memory of hardware for GA can be avoided. The performance of our solution is validated with a suite of benchmark test functions. This paper suggests that implementing GA with NN is a promising research direction for greatly reducing the running time of GA.

Magnetic field shimming of a permanent magnet using a combination of pieces of permanent magnets and a single-channel shim coil for skeletal age assessment of children

“…The x-and y-gradient coils were designed as a combination of a circular arc and third-order Bezier curve with the position and center angle optimized using a genetic algorithm with a minimal generation gap model (GA/MGG) [39,40]. The maximum number of turns was 30 and the coil gap was set to 120 mm.…”