Hybrid Architectures for Evolutionary Computing Algorithms

Abstract : This report documents interim progress for an in-house project aimed at identifying, developing and evaluating applications of evolutionary computing methods to hard optimization problem test cases on a single PC computer, a cluster of computers, and hardware FPGA platforms. We surveyed evolutionary computing literature and chose to focus on the Generic Algorithm, GA. We had the GA test three case problems, Non-Linear Coupled Ordinary Differential Equation, ODE, Parameterization, the DNA Code Word Library Generation, and the Networked Senior Power Management Policy Problem. The first test problem used an ODE biomodel for Antigen-Antibody binding that was of interest to a PI for a DARPA SIMBIOSYS program we managed. We developed prototype optimization software tools in three programming environments. Labview, Matlab, and compiled C, and demonstrated speed-ups on the order of 100-1000x by moving to C. We parallelized the C codes using Message Passing interface and demonstrated good linear speed-ups on a cluster. Our GA solution for the second test case problem. DNA Code Word Library Generation, was also parallelized, and was faster than any algorithm found in the literature. Finally, we began developing a hardware accelerated version of GA for the DNA Code Word Problem as a first step toward a distributed hardware implementation.

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