A New Real-Coded Genetic Algorithm Using the Adaptive Selection Network for Detecting Multiple Optima   [RGA] [GA] [AS]

by

Oshima, D., Miyamae, A., Sakuma, J., Kobayashi, S. and Ono, I.

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Info: 2009 IEEE Congress on Evolutionary Computation (Conference proceedings), 2009, p. -
Abstract:
The purpose of this paper is to propose a new real-coded genetic algorithm (RCGA) [RGA] [GA] named Networked Genetic Algorithm [GA] (NGA) that intends to find multiple optima simultaneously in deceptive globally multimodal landscapes. Most current techniques [ML] such as niching for finding multiple optima take into account big valley landscapes or non-deceptive globally multimodal landscapes [ML] but not deceptive ones called UVlandscapes. Adaptive Neighboring Search (ANS) is a promising approach for finding multiple optima in UV-landscapes. ANS uses a restricted mating scheme with a crossover-like mutation in order to find optima in deceptive globally multimodal landscapes. [ML] However, ANS has a fundamental problem that it does not find all the optima simultaneously in many cases. NGA overcomes the problem by an adaptive parent-selection scheme and an improved crossover-like mutation. We show the effectiveness of NGA over ANS in terms of the number [TNO] of detected optima in a single run on Fletcher and Powell functions as benchmark problems [BP] that are known to have UV-landscapes. We also analyze the behavior of NGA to confirm that the adaptive parent-selection scheme contributes the performance of NGA.
Notes:
CEC 2009 - A joint meeting of the IEEE, the EPS and the IET.}, IEEE Catalog Number: CFP09ICE-CDR
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BibTex:
@inproceedings(Oshima:2009:cec,
  author = "Dan Oshima and Atsushi Miyamae and Jun Sakuma and Shigenobu Kobayashi and Isao Ono  ",
  title = "A New Real-Coded Genetic Algorithm Using the Adaptive Selection Network for Detecting Multiple Optima",
  booktitle = "2009 IEEE Congress on Evolutionary Computation",
  year = 2009,
  editor = "Andy Tyrrell",
  pages = {--},
  address = "Trondheim, Norway",
  month = "18-21 May",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN = "978-1-4244-2959-2",
  file = {P563.pdf},
  url = {},
  size = {},
  abstract =	{The purpose of this paper is to propose a new real-coded genetic algorithm (RCGA) named Networked Genetic Algorithm (NGA) that intends to find multiple optima simultaneously in deceptive globally multimodal landscapes. Most current techniques such as niching for finding multiple optima take into account big valley landscapes or non-deceptive globally multimodal landscapes but not deceptive ones called UVlandscapes. Adaptive Neighboring Search (ANS) is a promising approach for finding multiple optima in UV-landscapes. ANS uses a restricted mating scheme with a crossover-like mutation in order to find optima in deceptive globally multimodal landscapes. However, ANS has a fundamental problem that it does not find all the optima simultaneously in many cases. NGA overcomes the problem by an adaptive parent-selection scheme and an improved crossover-like mutation. We show the effectiveness of NGA over ANS in terms of the number of detected optima in a single run on Fletcher and Powell functions as benchmark problems that are known to have UV-landscapes. We also analyze the behavior of NGA to confirm that the adaptive parent-selection scheme contributes the performance of NGA. },
  notes =	{CEC 2009 - A joint meeting of the IEEE, the EPS and the IET.},
IEEE Catalog Number: CFP09ICE-CDR},
)