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Ulaştırma ve Lojistik Kongreleri

alphanumeric journal

The Journal of Operations Research, Statistics, Econometrics and Management Information Systems

Machine Coded Compact Genetic Algorithms for Real Parameter Optimization Problems


Mehmet Hakan Satman, Ph.D.

Emre Akadal, Ph.D.


In this paper, we extend the Compact Genetic Algorithm (CGA) for real-valued optimization problems by dividing the total search process into three stages. In the first stage, an initial vector of probabilities is generated. The initial vector contains the probabilities of bits having 1 depending on the bit locations as defined in the IEEE-754 standard. In the second stage, a CGA search is applied on the objective function using the same encoding scheme. In the last stage, a local search is applied using the result obtained by the previous stage as the starting point. A simulation study is performed on a set of well-known test functions to measure the performance differences. Simulation results show that the improvement in search capabilities is significant for many test functions in many dimensions and different levels of difficulty.

Keywords: Evolutionary Optimization, Genetic Algorithm, Optimization, Simulation

Jel Classification: C63, C80

Suggested citation

Satman, M. H. & Akadal, E. (). Machine Coded Compact Genetic Algorithms for Real Parameter Optimization Problems. Alphanumeric Journal, 8(1), 43-58. https://doi.org/10.17093/alphanumeric.576919


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Volume 8, Issue 1, 2020


alphanumeric journal

Volume 8, Issue 1, 2020

Pages 43-58

Received: June 12, 2019

Accepted: June 5, 2020

Published: June 30, 2020

Full Text [805.1 KB]

2020 Satman, MH., Akadal, E.

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