ÖZELLEŞTİRİLMİŞ ALFA ERKEK (ALPHA MALE) GENETİK ALGORİTMANIN EVRİMSEL ALGORİTMALARLA PERFORMANS KARŞILAŞTIRMASI
Alfa erkek genetik algoritmalar cinsiyet farkı gözeten ve hayvan gruplarının hareketlerini taklit eden topluluk tabanlı bir optimizasyon aracıdır. Algoritma, dişilerin eş seçimi ile oluşturduğu sosyal olarak bölünmüş birey topluluklarına dayanmaktadır. Çalışmada, Alfa Erkek Genetik Algoritma’nın çaprazlama ve hibritleşme operatörü olarak sırasıyla Doğrusal Çaprazlama ve Hooke-Jeeves yöntemi kullanılması önerilmiştir. Çalışma kapsamında özelleştirilmiş algoritma ile Genetik Algoritmalar, Diferansiyel Evrim, Parçacık Sürü Optimizasyonu ve Yapay Arı Kolonisi Optimizasyonu gibi iyi bilinen algoritmalar arasındaki performans farklılıklarını ortaya çıkarabilmek için bilinen test fonksiyonları ile bir simülasyon çalışması gerçekleştirilmiştir. Simülasyon sonuçları, özelleştirilmiş algoritmanın çoğu durumda daha iyi performans sergilediğini göstermiştir.
PERFORMANCE COMPARISON OF THE SPECIALIZED ALPHA MALE GENETIC ALGORITHM WITH SOME EVOLUTIONARY ALGORITHMS
Alpha Male Genetic Algorithms are sexist and population basedoptimization tools that mimic the swarm behavior of animals. The algorithm consists on asocially partitioned population of individuals where the partitions are formed by sexualselection of females. In this paper, we suggest to use Linear Crossover and Hooke-Jeevesmethod for crossover and hybridization operators of Alpha Male Genetic Algorithms,respectively. We perform a simulation study using a set of well-known test functions toreveal performance differences between the specialized algorithm and some other wellknown optimization techniques including Genetic Algorithms, Differential Evolution,Particle Swarm Optimization, and Artificial Bee Colony Optimization. Simulation resultsshow that the specialized algorithm outperforms its counterparts in most of the cases.
___
- Allenson, R. (1992). Genetic algorithms with gender for multi-function optimisation.
Edinburgh Parallel Computing Centre, Edinburgh, Scotland, Tech. Rep. EPCCSS92-01.
- Ansotegui, C.,Sellmann, M., & Tierney, K. (2009). A gender-based genetic algorithm for
the automatic conguration of algorithms. International Conference on Principles
and Practice of Constraint Programming, 142-157.
- Drezner, T.& Drezner, Z. (2006). Gender-specic genetic algorithms. INFOR: Information
Systems and Operational Research, 44(2), 117-127.
- Drezner, Z. (2008). Extensive experiments with hybrid genetic algorithms for the solution
of the quadratic assignment problem. Computers & Operations Research, 35(3),
717-736.
- Drezner, Z.& Drezner, T. D. (2018). The alpha male genetic algorithm. IMA Journal of
Management Mathematics.
- Eberhart, R.& Kennedy, J. (1995). A new optimizer using particle swarm theory. In Micro
Machine and Human Science, 1995. MHS'95,Proceedings of the Sixth International
Symposium on, 39-43.
- Esquivel, S. C., Leiva, H. A.,& Gallard, R. H. (1999). Multiplicity in genetic algorithms to
face multicriteria optimization. Evolutionary Computation, 1999. CEC 99.
Proceedings of the 1999 Congress on, 1, 85-90.
- Goldberg, D. (1989). Genetic algorithms: search and optimization algorithms.
- Herrera, F., Lozano, M.,& Sanchez, A. M. (2003). A taxonomy for the crossover operator
for real-coded genetic algorithms: An experimental study. International Journal of
Intelligent Systems, 18(3), 309-338.
- Holland, J. H. (1975). Adaptation in natural and articial systems. An introductory analysis
with application to biology, control, and articial intelligence. Ann Arbor, MI:
University of Michigan Press, 439-444.
- Sanchez-Velazco, J.& Bullinaria, J. A., (2003). Sexual selection with competitive/cooperative operators for genetic algorithms. Neural Networks and Computational
Intelligence, 191-196.
- Karaboga, D.& Basturk, B. (2007). A powerful and efficient algorithm for numerical
function optimization: articial bee colony (abc) algorithm. Journal of Global
Optimization, 39(3), 459-471.
- Lis, J.& Eiben, A. E. (1997). A multi-sexual genetic algorithm for multiobjective
optimization. Evolutionary Computation, 1997., IEEE International Conference on,
59-64.
- Mishra, S. K. (2006). Some new test functions for global optimization and performance of
repulsive particle swarm method.
- Moser, I. (2009). Hooke-jeeves revisited. Evolutionary Computation, 2009. CEC'09. IEEE
Congress on, 2670-2676.
- Rejeb, J.& AbuElhaij, M. (2000). New gender genetic algorithm for solving graph
partitioning problems. Circuits and Systems, 2000. Proceedings of the 43rd IEEE
Midwest Symposium on, 444-446.
- Satman, M. H. (2015). Hybridization of floating-point genetic algorithms using hookejeeves algorithm as an intelligent mutation operator. Journal of Mathematical and
Computational Science, 5(3), 320.
- Satman, M. H.& Akadal, E. (2017). Machine-coded genetic operators and their
performances in floating-point genetic algorithms. International Journal of
Advanced Mathematical Sciences, 5(1), 8-19.
- Storn, R.& Price, K. (1997). Differential evolution-a simple and efficient heuristic for
global optimization over continuous spaces. Journal of Global Optimization, 11(4),
341-359.
- Wagner, S.& Affenzeller, M. (2005). Sexualga: Gender-specic selection for genetic
algorithms. Proceedings of the 9th World Multi-Conference on Systemics,
Cybernetics and Informatics (WMSCI), 4, 76-81.
- Vrajitoru, D. (2002). Simulating gender separation with genetic algorithms. Proceedings of
the 4th Annual Conference on Genetic and Evolutionary Computation. 634-641.
Morgan Kaufmann Publishers Inc., 2002.