Virtual reconfigurable architecture for evolving combinational logic circuits

A virtual reconfigurable architecture(VRA)-based evolvable hardware is proposed for automatic synthesis of combinational logic circuits at gate-level.The proposed VRA is implemented by a Celoxica RC1000 peripheral component interconnect(PCI)board with an Xilinx Virtex xcv2000E field programmable gate array(FPGA).To improve the quality of the evolved circuits,the VRA works through a two-stage evolution: finding a functional circuit and minimizing the number of logic gates used in a feasible circuit.To optimize the algorithm performance in the two-stage evolutionary process and set free the user from the time-consuming process of mutation parameter tuning,a self-adaptive mutation rate control(SAMRC)scheme is introduced.In the evolutionary process,the mutation rate control parameters are encoded as additional genes in the chromosome and also undergo evolutionary operations.The efficiency of the proposed methodology is tested with the evolutions of a 4-bit even parity function,a 2-bit multiplier,and a 3-bit multiplier.The obtained results demonstrate that our scheme improves the evolutionary design of combinational logic circuits in terms of quality of the evolved circuit as well as the computational effort,when compared to the existing evolvable hardware approaches.

Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms

Dynamic optimization problems(DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are invalid. In this article, a technology named ranking-based mutation operator(RMO) is presented to enhance the previous differential evolution(DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms.