Gregor Papa
ABSTRACT
The paper presents the parameter-less implementation of an evolutionary-based search. It does not need any predefined control parameters values, which are usually used for genetic algorithms and similar techniques. Efficiency of the proposed algorithm was evaluated by CEC2006 benchmark functions and a real-world product optimization problem.
- J. Brest, S. Greiner, B. Bošković, M. Mernik, and V. Žumer. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation, 10(6):646--657, December 2006. Google Scholar
Digital Library
- J. Gomez. Self adaptation of operator rates in evolutionary algorithms. In GECCO 2004, pages 1162--1173, 2004.Google ScholarCross Ref
- G. R. Harik and F. G. Lobo. A parameter-less genetic algorithm. In GECCO 1999, pages 258--265, July 1999.Google Scholar
- J. Liang, T. Runarsson, E. Mezura-Montes, M. Clerc, P. Suganthan,C. C. Coello, and K. Deb. Problem definitions and evaluation criteria for the cec 2006 special session on constrained real-parameter optimization. Technical Report 2006005, Nanyang Technological University, Singapore, March 2006.Google Scholar
- G. Papa. Concurrent operation scheduling and unit allocation with an evolutionary technique in the process of integrated-circuit design. PhD thesis, University of Ljubljana, Ljubljana, Slovenia, October 2002.Google Scholar
- G. Papa and B. Koroušić-Seljak. An artificial intelligence approach to the efficiency improvement of a universal motor. Engineering Applications of Artificial Intelligence, 18(1):47--55, February 2005. Google ScholarDigital Library
- T. Tušar, P. Korošec, G. Papa, B. Filipič, and J. Šilc. A comparative study of stochastic optimization methods in electric motor design. Applied Intelligence, 27(2):101--111, October 2007. Google ScholarDigital Library
Index Terms
- Parameter-less evolutionary search
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