ABSTRACT
In this paper, we undertake an investigation on the effect of balanced and unbalanced crossover operators against the problem of finding non-linear balanced Boolean functions: we consider three different balanced crossover operators and compare their performances with classic one-point crossover. The statistical comparison shows that the use of balanced crossover operators gives GA a definite advantage over one-point crossover.
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- Jiah-Shing Chen and Jia-Leh Hou. 2006. A Combination Genetic Algorithm with Applications on Portfolio Optimization. In IEA/AIE (Lecture Notes in Computer Science), Vol. 4031. Springer, 197--206. Google ScholarDigital Library
- Salvador García, Daniel Molina, Manuel Lozano, and Francisco Herrera. 2009. A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 Special Session on Real Parameter Optimization. J. Heuristics 15, 6 (2009), 617--644. Google ScholarDigital Library
- Luca Mariot and Alberto Leporati. 2015. A Genetic Algorithm for Evolving Plateaued Cryptographic Boolean Functions. In Theory and Practice of Natural Computing - Fourth International Conference, TPNC 2015, Mieres, Spain, December 15--16, 2015. Proceedings. 33--45. Google ScholarDigital Library
- Luca Mariot, Stjepan Picek, Domagoj Jakobovic, and Alberto Leporati. 2017. Evolutionary algorithms for the design of orthogonal latin squares based on cellular automata. In GECCO. ACM, 306--313. Google ScholarDigital Library
- Luca Mariot, Stjepan Picek, Domagoj Jakobovic, and Alberto Leporati. 2018. Evolutionary Search of Binary Orthogonal Arrays. In PPSN(1) (Lecture Notes in Computer Science), Vol. 11101. Springer, 121--133.Google Scholar
- Thorsten Meinl and Michael R. Berthold. 2009. Crossover operators for multiobjective k-subset selection. In GECCO. ACM, 1809--1810. Google ScholarDigital Library
- William Millan, Andrew J. Clark, and Ed Dawson. 1998. Heuristic Design of Crypto graphic ally Strong Balanced Boolean Functions. In EUROCRYPT (Lecture Notes in Computer Science), Vol. 1403. Springer, 489--499.Google Scholar
- Stjepan Picek, Marin Golub, and Domagoj Jakobovic. 2011. Evaluation of Crossover Operator Performance in Genetic Algorithms with Binary Representation. In ICIC (3) (LNCS), Vol. 6840. Springer, 223--230. Google ScholarDigital Library
- Stjepan Picek, Domagoj Jakobovic, and Marin Golub. 2013. On the recombination operator in the real-coded genetic algorithms. In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2013, Cancun, Mexico, June 20--23, 2013. 3103--3110.Google ScholarCross Ref
Index Terms
- Does constraining the search space of GA always help?: the case of balanced crossover operators
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