Trading between quality and non-functional properties of median filter in embedded systems

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Zdenek Vasicek and Vojtech Mrazek",
  title =        "Trading between quality and non-functional properties
                 of median filter in embedded systems",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2017",
  volume =       "18",
  number =       "1",
  month =        mar,
  pages =        "45--82",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 improvement, Cartesian genetic programming, Median
                 function, Comparison network, Permutation principle,
                 Median filter",
  ISSN =         "1389-2576",
  language =     "english",
  URL =          "",
  DOI =          "doi:10.1007/s10710-016-9275-7",
  size =         "38 pages",
  abstract =     "Genetic improvement has been used to improve
                 functional and non-functional properties of software.
                 In this paper, we propose a new approach that applies a
                 genetic programming (GP)-based genetic improvement to
                 trade between functional and non-functional properties
                 of existing software. The paper investigates
                 possibilities and opportunities for improving
                 non-functional parameters such as execution time, code
                 size, or power consumption of median functions
                 implemented using comparator networks. In general, it
                 is impossible to improve non-functional parameters of
                 the median function without accepting occasional errors
                 in results because optimal implementations are
                 available. In order to address this issue, we proposed
                 a method providing suitable compromises between
                 accuracy, execution time and power consumption.
                 Traditionally, a randomly generated set of test vectors
                 is employed so as to assess the quality of GP
                 individuals. We demonstrated that such an approach may
                 produce biased solutions if the test vectors are
                 generated inappropriately. In order to measure the
                 accuracy of determining a median value and avoid such a
                 bias, we propose and formally analyse new quality
                 metrics which are based on the positional error
                 calculated using the permutation principle introduced
                 in this paper. It is shown that the proposed method
                 enables the discovery of solutions which show a
                 significant improvement in execution time, power
                 consumption, or size with respect to the accurate
                 median function while keeping errors at a moderate
                 level. Non-functional properties of the discovered
                 solutions are estimated using data sets and validated
                 by physical measurements on physical microcontrollers.
                 The benefits of the evolved implementations are
                 demonstrated on two real-world problems---sensor data
                 processing and image processing. It is concluded that
                 data processing software modules offer a great
                 opportunity for genetic improvement. The results
                 revealed that it is not even necessary to determine the
                 median value exactly in many cases which helps to
                 reduce power consumption or increase performance. The
                 discovered implementations of accurate, as well as
                 approximate median functions, are available as C
                 functions for download and can be employed in a custom


Genetic Programming entries for Zdenek Vasicek Vojtech Mrazek