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A Comparative Study of Gender Assignment in a Standard Genetic Algorithm

  • K. Tahera
  • R. N. Ibrahim
  • P. B. Lochert
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 6)

The genetic algorithm is a population-based heuristic search algorithm which has become a popular method for solving optimisation problems. The concept of the genetic algorithm was inspired by nature and was then successfully developed by John Holland in 1975. The basic concepts borrowed from nature are: randomness, fitness, inheritance, and creation of a new species. The genetic algorithm was developed based on the fact that successful matching of parents will tend to produce better off spring. This idea is supported by the building block theory [7]. The individuals in a population (i.e., the parents) are selected based on Darwin's law of natural selection and survival of the fittest. The genetic information of the parents is exchanged in the hope of producing improved offspring. Occasionally, a mutation operator randomly changes genes to produce new individuals. For a detailed review of the GA concept, see Haupt and Haupt [6].

Keywords

Genetic Algorithm Mechanical Design Threshold Limit Tournament Selection Population Member 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • K. Tahera
    • 1
  • R. N. Ibrahim
    • 1
  • P. B. Lochert
    • 1
  1. 1.Mechanical EngineeringMonash UniversityClaytonAustralia

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