CLASSIFICATION AND ANALYSIS OF EVOLUTIONARY METHODS OF EVA BLOCK LAYOUT
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.
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