You are here

Home » Content

UNIFORM POPULATION IN GENETIC ALGORITHMS

Journal Name:

  • Istanbul University Journal of Electrical & Electronics Engineering

Publication Year:

  • 2002

Key Words:

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
The most of researchers dealing with genetic algorithms apply variation on the genetic operators. Some of them propose new genetic algotihm types. However, none of them deals with generating population of good quality, initially. In this paper, we propose a method for generating initial population and the method includes all types of chromosome encoding. The goodness of generated population by proposed method is also illustrated by applying this population and random initial population to multi-modal functions such as Griewank, Michalewicz and Rastrigin. For the sake of simplicity, all functions are selected as functions of two variables.
Bookmark/Search this post with
FULL TEXT (PDF): 
PDF icon arastirmax-uniform-population-genetic-algorithms.pdf
  • 2
495-504
  • English

REFERENCES

References: 

1. R.E. Keller, W. Banzhaf,“ Evolution of
Genetic Code on a Hard Problem“, Proc. Of
the Genetic and Evolutionary Computation
Conference, San Fransisco, California, pp:50-
56, July 7-11, 2001.
2. S. Luke, “When Short Runs Beat Long
Runs”, Proc. Of the Genetic and Evolutionary
Uniform Population In Genetic Algorithms
Ali KARCI and Ahmet ARSLAN
503
Computation Conference, San Fransisco,
California, pp:74-80, July 7-11, 2001.
3. R. Poli, N. F. McPhee, « Exact Schema
Theory for GP and Variable-Length Gas w
Homologous Crossover”, Proc. Of the
Genetic and Evolutionary Computation
Conference, San Fransisco, California,
pp:104-111, July 7-11, 2001.
4. R. Poli, J.E. Rowe, N. F. McPhee, «Markov
Chain Models for GP and Variable-Length
Gas w Homologous Crossover”, Proc. Of the
Genetic and Evolutionary Computation
Conference, San Fransisco, California,
pp:112-119, July 7-11, 2001.
5. W.M. Spears, “The Role of Mutation and
Recombination in Evolutionary Algorithms”,
PhD dissertation at George Mason
University, Fairfax, Virginia, 1998.
6. M. Srinivas, L.M. Patnaik, “Genetic Search :
Analysis Using Fitness Moments”, IEEE on
Knowledge and Data Engineering, vol:8,
no:1, pp:120-133, 1996.
7. P. J. Angeline, “Two Self-Adaptive
Crossover Operations for Genetic
Programming”, Advances in Genetic
Programming: Volume 2.
8. J.A. Vasconcelos, J.A. Ramirez, R.H.C.
Takahashi, R.R. Saldanha, “Improvement in
Genetic Algorithms”, IEEE Trans. On
Magnetics, vol:37, no:5, pp:3414-3417, 2001.
9. N.Y. Nikolaev, H. Iba, “Regularization
Approach to Inductive Genetic
Programming”, IEEE Trans. On Evolutionary
Computation, vol:5, no:4, pp:359-375, 2001.
10. B. Edmonds, “Meta-Genetic Programming:
Co-evolving the Operators of Variation”,
Turk J. Elec. Engin. Vol:9, no:1, pp:13-29,
2001.
11. P.D. Stroud, “Kalman-Extended Genetic
Algorithm for Search in Nonstationary
Environments with Noisy Fitness
Evaluations”, IEEE Trans. On Evolutionary
Computation, vol:5, no:1, pp:66-77, 2001.
12. A. Karcý, A. Çýnar, “Comparison of Uniform
Distributed Initial Population Method and
Random Initial Population Method in Genetic
Search”, The 15th International Symposium
on Computer and Information Sciences,
Istanbul, Turkey, 159-166, 2000.
13. A. Karcý, A. Arslan, “Bidirectional
Evolutionary Heuristic for the Minimum
Vertex-Cover Problem”, Journal of
Computers and Electrical Engineering, (will
be appeared).
14. Y.-W. Leung, Y. Ewang, éAn Orthogonal
Genetic Algorithm with Quantization for
Global Numerical Optimization“, IEEE
Trans. On Evolutionary Computation, vol:5,
no:1, pp:41-53, 2001.
15. H.E. Aguirre, K. Tanaka, S. Oshita,
“Increasing the Robustness of Distributed
Genetic Algorithms by Parallel Cooperative-
Competitive Genetic Operators”, Proc. Of the
Genetic and Evolutionary Computation
Conference, San Fransisco, California,
pp:195-202, July 7-11, 2001.
16. J. Branke,”Reducing the Sampling Variance
When Searching for Robust Solutions”, Proc.
Of the Genetic and Evolutionary
Computation Conference, San Fransisco,
California, pp:235-242, July 7-11, 2001.
17. M. Elhadef, D.A. Coley,” Adaptive Mutation
for Semi-Separable Problems”, Proc. Of the
Genetic and Evolutionary Computation
Conference, San Fransisco, California,
pp:306-312, July 7-11, 2001.
18. J.E. Rowe, N.F. McPhee, “The Effect of
Crossover and Mutation Operators on
Variable Lengrh Linear Structures”, Proc. Of
the Genetic and Evolutionary Computation
Conference, San Fransisco, California,
pp:535-542, July 7-11, 2001.
19. C. Schumacher, M.D. Vose, L.D. Whitley,
“The No Free Lunch and Problem
Description Length”, Proc. Of the Genetic
and Evolutionary Computation Conference,
San Fransisco, California, pp:565-570, July
7-11, 2001.
20. A. Simoes, E. Costa, “On Biologically
Inspired Genetic Operators: Transformation
in the Standard Genetic Algorithm”, Proc. Of
the Genetic and Evolutionary Computation
Conference, San Fransisco, California,
pp:584-591, July 7-11, 2001.
21. J. Smith, “Modelling GAs with Self Adaptive
Mutation Rates”, Proc. Of the Genetic and
Evolutionary Computation Conference, San
Fransisco, California, pp:599-606, July 7-11,
2001.
22. R. P. Srivastava, D.E. Goldberg,
“Verification of the Theory of Genetic
Algorithm Continuation”, Proc. Of the
Genetic and Evolutionary Computation
Conference, San Fransisco, Californ

Thank you for copying data from http://www.arastirmax.com