Defining implicit objective functions for design problems

ABSTRACT

In many design tasks it is difficult to explicitly define an objective function. This paper uses machine learning to derive an objective in a feature space based on selected examples of previous designs, thus implicitly capturing the features that distinguish that set from others without requiring a predetermined measure of fitness. A genetic algorithm is used to generate new designs, and these are shown to recognisably display the appropriate features. It is demonstrated that the range of relevant features and optimal solutions is easily varied in proportion to the examples selected to define the objective. Methods for improving the function for GA search are discussed.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.5 Implementation
          Subjects: Interactive systems

Additional Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.5 Implementation
          Subjects: Special architectures

  J. Computer Applications
  J.6 COMPUTER-AIDED ENGINEERING
      Subjects: Computer-aided design (CAD)

General Terms:
Algorithms, Design, Experimentation

Keywords:
architecture, design/synthesis, fitness evaluation, genetic algorithm, machine learning