A Real-Coded Genetic Algorithm for Function Optimization Using the Unimodal Normal Distribution Crossover

DOI Web Site 90 Citations 23 References
  • ONO Isao
    Faculty of Engneering, University of Tokushima
  • SATOH Hiroshi
    Department of Computer Science, National Defence Academy
  • KOBAYASHI Shigenobu
    Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

Bibliographic Information

Other Title
  • 単峰性正規分布交叉UNDXを用いた実数値GAによる関数最適化
  • タンホウセイ セイキ ブンプ コウサ UNDX オ モチイタ ジッスウチ GA ニ ヨル カンスウ サイテキカ

Search this article

Description

<p>This paper presents a new genetic algorithm (GA) for function optimization, considering epistasis among parameters. When a GA is applied to a function to minimize it, parents are expected to lie on some ponds or along some valleys that are promizing areas because of selection pressure as the search goes on. Especially when the function has epistasis among parameters, it has valleys that are not parallel to coordinate axes. In this case, we believe that a crossover should generate children along the valleys in order to focus the search on such promizing area from a view point of search efficiency. We employ the real number vector as a representation and propose the Unimodal Normal Distribution Crossover (UNDX) taking account of epistasis among parameters. The UNDX generates children near the line segment connecting two parents so that the children lie on the valley where the two parents are when the UNDX is applied to a function with epistasis among parameters. We demonstrate that the UNDX can efficiently optimize various functions including multi-modal ones and ones that have epistasis among parameters by applying he UNDX to some famous benchmark functions.</p>

Journal

Citations (90)*help

See more

References(23)*help

See more

Keywords

Details 詳細情報について

Report a problem

Back to top