• Title/Summary/Keyword: Elitist Preserving strategy

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A Study on Improvement of Genetic Algorithm Operation Using the Restarting Strategy (재시동 조건을 이용한 유전자 알고리즘의 성능향상에 관한 연구)

  • 최정묵;이진식;임오강
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.15 no.2
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    • pp.305-313
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    • 2002
  • The genetic algorithm(GA), an optimization technique based on the theory of natural selection, has proven to be relatively robust means to search for global optimum. It is converged near to the global optimum point without auxiliary information such as differentiation of function. When studying some optimization problems with continuous variables, it was found that premature saturation was reached that is no further improvement in the object function could be found over a set of iterations. Also, the general GA oscillates in the region of the new global optimum point so that the speed of convergence is decreased. This paper is to propose the concept of restarting and elitist preserving strategy as a measure to overcome this difficulty. Some benchmark examples are studied involving 3-bar truss and cantilever beam with plane stress elements. The modifications to GA improve the speed of convergence.

Genetic Algorithms for Tire Mixing Process Scheduling (타이어 정련 공정 스케줄링을 위한 유전자 알고리즘)

  • Ahn, Euikoog;Park, Sang Chul
    • Korean Journal of Computational Design and Engineering
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    • v.18 no.2
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    • pp.129-137
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    • 2013
  • This paper proposed the scheduling method for tire mixing processes using the genetic algorithm. The characteristics of tire mixing process have the manufacturing routing, operation machine and operation time by compound types. Therefore, the production scheduling has to consider characteristics of the tire mixing process. For the reflection of the characteristics, we reviewed tire mixing processes. Also, this paper introduces the genetic algorithm using the crossover and elitist preserving selection strategy. Fitness is measured by the makespan. The proposed genetic algorithm has been implemented and tested with two examples. Experimental results showed that the proposed algorithm is superior to conventional heuristic algorithm.