TY - JOUR

T1 - A distance between populations for one-point crossover in genetic algorithms

AU - Manzoni, Luca

AU - Vanneschi, Leonardo

AU - Mauri, Giancarlo

N1 - Manzoni, L., Vanneschi, L., & Mauri, G. (2012). A distance between populations for one-point crossover in genetic algorithms. Theoretical Computer Science, 429, 213-221. https://doi.org/10.1016/j.tcs.2011.12.041

PY - 2012/4/20

Y1 - 2012/4/20

N2 - Genetic algorithms use transformation operators on the genotypic structures of the individuals to carry out a search. These operators define a neighborhood. To analyze various dynamics of the search process, it is often useful to define a distance in this space. In fact, using an operator-based distance can make the analysis more accurate and reliable than using distances which have no relationship with the genetic operators. In this paper we define a distance which is based on the standard one-point crossover. Given that the population strongly affects the neighborhood induced by the crossover, we first define a crossover-based distance between populations. Successively, we show that it is naturally possible to derive from this function a family of distances between individuals. Finally, we also introduce an algorithm to compute this distance efficiently.

AB - Genetic algorithms use transformation operators on the genotypic structures of the individuals to carry out a search. These operators define a neighborhood. To analyze various dynamics of the search process, it is often useful to define a distance in this space. In fact, using an operator-based distance can make the analysis more accurate and reliable than using distances which have no relationship with the genetic operators. In this paper we define a distance which is based on the standard one-point crossover. Given that the population strongly affects the neighborhood induced by the crossover, we first define a crossover-based distance between populations. Successively, we show that it is naturally possible to derive from this function a family of distances between individuals. Finally, we also introduce an algorithm to compute this distance efficiently.

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U2 - 10.1016/j.tcs.2011.12.041

DO - 10.1016/j.tcs.2011.12.041

M3 - Article

AN - SCOPUS:84858341871

SN - 0304-3975

VL - 429

SP - 213

EP - 221

JO - Theoretical Computer Science

JF - Theoretical Computer Science

ER -