Machine Coded Genetic Algorithms For Real Parameter Optimization Problems

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors M. Hakan Satman
Journal/Conference Name Gazi University Journal of Science
Paper Category
Paper Abstract In this paper, we introduce a new encoding-decoding strategy for the floating-point genetic algorithms and we call the genetic algorithms which use this strategy Machine Coded Genetic Algorithms. We suggest applying classical crossover and mutation operations on the byte representations of real values which are already encoded in memory. This is equivalent to use a 256-unary alphabet with 8 genes for a single real value. Use of byte representations makes the classical genetic operators interpretable in floating-point chromosomes and increases the search capabilities in a wide range without losing accuracy. This strategy also decreases the computation time needed for the genetic operators. Simulation studies show that our strategy performs well on many test functions by means of converging to global optimum and time efficiency. Key Words : Genetic algorithms, Chromosome encoding, Real parameter optimization.
Date of publication 2013
Code Programming Language R
Comment

Copyright Researcher 2021