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High Performance Evolutionary Computing

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5 Author(s)
Nunez, E. ; USASMDC Adv. Res. Center, COLSA Corp., Huntsville, AL ; Banks, E.R. ; Agarwal, P. ; McBride, M.
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Evolutionary computing (EC) comprises a family of global optimization techniques that start with a random population of potential solutions and then evolve more fit solutions over many generations. To accomplish this increase in fitness, EC uses basic operations like selection, recombination, and mutation. Because of its compute- intensive nature, EC research is an obvious candidate for hosting on HPC clusters or systems. EC requires high performance computers (HPC) because the selection process needs to evaluate the fitness of each member of a population of solutions, so the more fit individuals may propagate their characteristics to the next generation of solutions. This requirement becomes even more acute because the evaluation process must be iterated over a very large number of generations. In this paper, we provide a general overview of EC, its applicability to a broad range of problems. In particular, we focus on some subclasses of EC known as genetic programming (GP), genetic algorithms (GA), hybrids, and other EC forms. This paper also discusses the architectural issues of hosting EC on a HPC cluster, and the related issue of population management. Two possible EC architectures are presented: (1) a single chromosome evaluator that treats a pool of cluster nodes as evaluators for an individual solution, and (2) a parallel evolver that manages a sub-population of solutions at each node. Advantages and disadvantages of each approach will be discussed. EC may be applied to a wide variety of problems. Applications of EC include schedule optimization, robotic navigation, image enhancement/processing, discrimination of buried unexploded ordnance, discovery of innovative electronic filter and controller designs, lens design optimization, radar response modeling, and many more. EC excels at solving high-dimensional and nonlinear problems. HPC resources have enabled the broader application of EC optimization techniques. However, at present, EC is underutilized in the- HPC environment. This paper raises awareness of EC's general applicability and its power when coupled with HPC resources

Published in:

HPCMP Users Group Conference, 2006

Date of Conference:

June 2006