Individually Directional Evolutionary Algorithm for Solving Global Optimization Problems Comparative Study

Full Text (PDF, 340KB), PP.12-19

Views: 0 Downloads: 0

Author(s)

Lukasz Kubus 1,*

1. Department of Computer Science Applications, Kielce University of Technology, Kielce, Poland

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2015.09.02

Received: 22 Jan. 2015 / Revised: 6 Apr. 2015 / Accepted: 8 May 2015 / Published: 8 Aug. 2015

Index Terms

Individually Directional Evolutionary Algorithm, Evolutionary Algorithm, Evolutionary Computing, Directed Mutation, Global Optimization

Abstract

Limited applicability of classical optimization methods influence the popularization of stochastic optimization techniques such as evolutionary algorithms (EAs). EAs are a class of probabilistic optimization techniques inspired by natural evolution process, witch belong to methods of Computational Intelligence (CI). EAs are based on concepts of natural selection and natural genetics. The basic principle of EA is searching optimal solution by processing population of individuals. This paper presents the results of simulation analysis of global optimization of benchmark function by Individually Directional Evolutionary Algorithm (IDEA) and other EAs such as Real Coded Genetic Algorithm (RCGA), elite RCGA with the one elite individual, elite RCGA with the number of elite individuals equal to population size. IDEA is a newly developed algorithm for global optimization. Main principle of IDEA is to monitor and direct the evolution of selected individuals of population to explore promising areas in the search space. The idea of IDEA is an independent evolution of individuals in current population. This process is focused on indicating correct direction of changes in the elements of solution vector. This paper presents a flowchart, selection method and genetic operators used in IDEA. Moreover, similar mechanisms and genetic operators are also discussed.

Cite This Paper

Łukasz Kubuś, "Individually Directional Evolutionary Algorithm for Solving Global Optimization Problems-Comparative Study", International Journal of Intelligent Systems and Applications(IJISA), vol.7, no.9, pp.12-19, 2015. DOI:10.5815/ijisa.2015.09.02

Reference

[1]A. F. Ali, Genetic Local Search Algorithm with Self-Adaptive Population Resizing for Solving Global Optimization Problems, International Journal of Information Engineering and Electronic Business, 2014, 6(3): 51-63.
[2]J. Arabas, Lectures on evolutionary algorithms, WNT, Warsaw, 2001 (in Polish).
[3]A. Berry, P. Vamplew, PoD Can Mutate: A Simple Dynamic Directed Mutation Approach for Genetic Algorithms, AISAT 2004: The 2nd International Conference on Artificial Intelligence in Science and Technology, 2004 , 200-205.
[4]A. Berry, P. Vamplew, L. Temby, Accelerating real-valued genetic algorithms using mutation-with-momentum, The 18th Australian Joint Conference on Artificial Intelligence, 2005, 1108-1111.
[5]D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, WNT, Warsaw, 1995 (in Polish).
[6]L. Grad, An example of feed forward neural network structure optimisation with genetic algorithm, Bulletin of The Institute of Automation and Robotics, Warsaw, 2006, (23): 27-36 (in Polish).
[7]I. Korejo, S. Yang, C. Li, A Directed Mutation Operator for Real Coded Genetic Algorithms, Applications of Evolutionary Computation, Springer, 2010, (6024): 491-500.
[8]Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, WNT, Warsaw,1999, (in Polish).
[9]A. Obuchowicz, P. Prętki, Directional distribution-based mutation for evolutionary algorithms, Advanced Control and Diagnosis - ACD 2009 : 7th Workshop, 2009, [6] CD-ROM.
[10]S. K. Pal, C.S Rai, A. P. Singh, Comparative Study of Firefly Algorithm and Particle Swarm Optimization for Noisy Non-Linear Optimization Problems, International Journal of Intelligent Systems and Applications, 2012, 4(10): 50-57.
[11]K. Poczęta, Ł. Kubuś, Supervised and Population Based Learning Algorithms for Fuzzy Cognitive Maps – a Comparative Study, Applications of Information Technologies – theory and practice, Radom, 2014, 96-107.
[12]W. Stach, L. Kurgan, W. Pedrycz, M. Reformat, Evolutionary Development of Fuzzy Cognitive Maps, IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2005, Reno, Nevada, USA, May 22-25, 2005, 619-624.
[13]W. Stach, L. Kurgan, W. Pedrycz, M. Reformat, Genetic Learning of Fuzzy Cognitive Maps, Fuzzy Sets Syst., 2005, 153(3): 371-401.
[14]P. Tang, M. Tseng, Adaptive directed mutation for real-coded genetic algorithms, Applied Soft Computing, 2013, 13(1): 600-614 .
[15]Z. Świątnicki, V. Olej, Generation and Optimization of Fuzzy Neural Networks Structure, Bulletin of The Military University of Technology, Warsaw, 2002, 51(9): 125-138 (in Polish).
[16]X. Yao, Y. Liu, G. Lin, Evolutionary programming made faster, IEEE Transactions on Evolutionary Computation, 1999, 3(2): 82-102.
[17]X. Zhao, X. Gao, A micro evolutionary programming for optimization of continuous space, PPSN VIII workshop on challenges in real world optimization using evolutionary computing, 2004, 17-23.
[18]X. Zhao, X. Gao, Evolutionary programming based on non-uniform mutation, Applied Mathematics and Computation, 2007, 192(1): 1-11.