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research-article

Evolving Hidden Genes in Genetic Algorithms For Systems Architecture Optimization

[+] Author and Article Information
Ossama Abdelkhalik

Department of Mechanical Engineering and Engineering Mechanics, Michigan Tech Univesity, Houghton, Michigan 49931
ooabdelk@mtu.edu

Shadi Darani

Department of Mechanical Engineering and Engineering Mechanics, Michigan Tech Univesity, Houghton, Michigan 49931
sahmadid@mtu.edu

1Corresponding author.

ASME doi:10.1115/1.4040207 History: Received May 24, 2017; Revised April 21, 2018

Abstract

The concept of hidden genes was recently introduced in genetic algorithms to handle systems architecture optimization problems where the number of design variables is variable. Selecting the hidden genes in a chromosome determines the architecture of the solution. This paper presents two categories of mechanisms for selecting (assigning) the hidden genes in the chromosomes of genetic algorithms. These mechanisms dictate how the chromosome evolve in the presence of hidden genes. In the proposed mechanisms, a tag is assigned for each gene; this tag determines whether the gene is hidden or not. In the first category of mechanisms, the tags evolve using stochastic operations. Eight different variations in this category are proposed and compared through numerical testing. The second category introduces logical operations for tags evolution. Both categories are tested on the problem of interplanetary trajectory optimization for a space mission to Jupiter, as well as on mathematical optimization problems. Several numerical experiments were designed and conducted to optimize the selection of the hidden genes algorithm parameters. The numerical results presented in this paper demonstrate that the proposed concept of tags and the assignment mechanisms enable the hidden genes genetic algorithms to find better solutions.

Copyright (c) 2018 by ASME
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