JACIII Vol.20 No.3 pp. 429-437
doi: 10.20965/jaciii.2016.p0429


AEGA: A New Real-Coded Genetic AlgorithmTaking Account of Extrapolation

Kento Uemura and Isao Ono

Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
4259 Nagatsuta, Midori-ku, Yokohama, 226-8502 Kanagawa, Japan

October 26, 2015
January 27, 2016
Online released:
May 19, 2016
May 19, 2016
real-coded genetic algorithms, adaptive extrapolation RCGA, black-box function optimization, ridge structures, implicit active constraints

This study proposes a new real-coded genetic algorithm (RCGA) taking account of extrapolation, which we call adaptive extrapolation RCGA (AEGA). Real-world problems are often formulated as black-box function optimization problems and sometimes have ridge structures and implicit active constraints. mAREX/JGG is one of the most powerful RCGAs that performs well against these problems. However, mAREX/JGG has a problem of search inefficiency. To overcome this problem, we propose AEGA that generates offspring outside the current population in a more stable manner than mAREX/JGG. Moreover, AEGA adapts the width of the offspring distribution automatically to improve its search efficiency. We evaluate the performance of AEGA using benchmark problems and show that AEGA finds the optimum with fewer evaluations than mAREX/JGG with a maximum reduction ratio of 45%. Furthermore, we apply AEGA to a lens design problem that is known as a difficult real-world problem and show that AEGA reaches the known best solution with approximately 25% fewer evaluations than mAREX/JGG.

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Last updated on Mar. 28, 2017