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• CLASSIFICATION AND ANALYSIS OF EVOLUTIONARY METHODS OF EVA BLOCK LAYOUT

  Y.V. Danilchenko, V.I. Danilchenko, V. M. Kureichik

  2020-07-20

  Abstract ▼

  Currently, there is a large increase in the need for the design and development of radioelectronic
  devices. This is due to increasing requirements for radio-electronic systems, as well as
  the emergence of new generations of semiconductor devices. In this regard, there is a need to develop
  new tools for automated layout of EVA blocks. There are a number of problems that complicate
  the actual representation of knowledge in CAD and are probably solvable at the current level
  of cognitive science development. The problem of stereotyping and the problem of coarsening are
  interrelated and need to create hybrid models of representation. The paper deals with the problem
  of solving the problem of EVA block layout in the design of radio-electronic equipment. The purpose
  of this work is to find ways to optimize the planning of EVA block layout using a genetic
  algorithm. The relevance of the work is that the genetic algorithm can improve the quality of layout
  planning. These algorithms allow you to improve the quality and speed of layout planning. The
  scientific novelty lies in the search and analysis of effective methods for composing EVA blocks
  using genetic algorithms. The main difference from the known comparisons is in the analysis of
  new promising algorithms for composing EVA blocks. Result of work. The paper shows the disadvantages
  of traditional algorithms for searching for a suboptimal EVA plan. Descriptions of modern
  models of evolutionary and other calculations are given. Genetic algorithms have a number of
  important advantages – adaptability to a changing environment, the evolutionary approach makes
  it possible to analyze, Supplement and change the knowledge base depending on changing conditions,
  as well as quickly create optimal solutions. If you apply genetic algorithms and preprocessing
  heuristics to provide optimal initial solutions, you can achieve more productive use of
  algorithms. Known genetic algorithms converge quickly, but they lose population diversity, which
  affects the quality of the solution. To balance data, the solution is corrected using efficient operators
  or stable mutation.