|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Title:||Quasi-Optimal Recombination Operator|
|Citation:||Chicano F, Ochoa G, Whitley D & Tinós R (2019) Quasi-Optimal Recombination Operator. In: Liefooghe A & Paquete L (eds.) Evolutionary Computation in Combinatorial Optimization. Lecture Notes in Computer Science, 11452. EvoCOP 2019: European Conference on Evolutionary Computation in Combinatorial Optimization, Leipzig, Germany, 24.04.2019-26.04.2019. Cham, Switzerland: Springer International Publishing, pp. 131-146. https://doi.org/10.1007/978-3-030-16711-0_9|
|Series/Report no.:||Lecture Notes in Computer Science, 11452|
|Conference Name:||EvoCOP 2019: European Conference on Evolutionary Computation in Combinatorial Optimization|
|Conference Dates:||2019-04-24 - 2019-04-26|
|Conference Location:||Leipzig, Germany|
|Abstract:||The output of an optimal recombination operator for two parent solutions is a solution with the best possible value for the objective function among all the solutions fulfilling the gene transmission property: the value of any variable in the offspring must be inherited from one of the parents. This set of solutions coincides with the largest dynastic potential for the two parent solutions of any recombination operator with the gene transmission property. In general, exploring the full dynastic potential is computationally costly, but if the variables of the objective function have a low number of non-linear interactions among them, the exploration can be done in O(4β(n+m)+n2) time, for problems with n variables, m subfunctions and β a constant. In this paper, we propose a quasi-optimal recombination operator, called Dynastic Potential Crossover (DPX), that runs in O(4β(n+m)+n2) time in any case and is able to explore the full dynastic potential for low-epistasis combinatorial problems. We compare this operator, both theoretically and experimentally, with two recently defined efficient recombination operators: Partition Crossover (PX) and Articulation Points Partition Crossover (APX). The empirical comparison uses NKQ Landscapes and MAX-SAT instances.|
|Status:||AM - Accepted Manuscript|
|Rights:||This is a post-peer-review, pre-copyedit version of an article published in Liefooghe A & Paquete L (eds.) Evolutionary Computation in Combinatorial Optimization. Lecture Notes in Computer Science, 11452. EvoCOP 2019: European Conference on Evolutionary Computation in Combinatorial Optimization, Leipzig, Germany, 24.04.2019-26.04.2019. Cham, Switzerland: Springer International Publishing, pp. 131-146. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-16711-0_9|
|QuasiOptimalRecombination.pdf||Fulltext - Accepted Version||347.53 kB||Adobe PDF||View/Open|
This item is protected by original copyright
Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/
If you believe that any material held in STORRE infringes copyright, please contact firstname.lastname@example.org providing details and we will remove the Work from public display in STORRE and investigate your claim.