Dynastic Potential Crossover Operator.

Autor: Chicano F; ITIS Software, University of Malaga, Spain chicano@lcc.uma.es., Ochoa G; University of Stirling, UK gabriela.ochoa@cs.stir.ac.uk., Whitley LD; Colorado State University, USA whitley@cs.colostate.edu., Tinós R; University of Sao Paulo, Brazil rtinos@ffclrp.usp.br.
Jazyk: angličtina
Zdroj: Evolutionary computation [Evol Comput] 2022 Sep 01; Vol. 30 (3), pp. 409-446.
DOI: 10.1162/evco_a_00305
Abstrakt: An optimal recombination operator for two-parent solutions provides the best solution among those that take the value for each variable from one of the parents (gene transmission property). If the solutions are bit strings, the offspring of an optimal recombination operator is optimal in the smallest hyperplane containing the two parent solutions. Exploring this hyperplane is computationally costly, in general, requiring exponential time in the worst case. However, when the variable interaction graph of the objective function is sparse, exploration can be done in polynomial time. In this article, we present a recombination operator, called Dynastic Potential Crossover (DPX), that runs in polynomial time and behaves like an optimal recombination operator for low-epistasis combinatorial problems. We compare this operator, both theoretically and experimentally, with traditional crossover operators, like uniform crossover and network crossover, and with two recently defined efficient recombination operators: partition crossover and articulation points partition crossover. The empirical comparison uses NKQ Landscapes and MAX-SAT instances. DPX outperforms the other crossover operators in terms of quality of the offspring and provides better results included in a trajectory and a population-based metaheuristic, but it requires more time and memory to compute the offspring.
(© 2021 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.)
Databáze: MEDLINE
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