Graph coloring problem (GCP) is an NP-complete combinatorial optimization problem. Its computational complexity motivated many efforts to get approximate solutions through different meta-heuristics, such as several variants of evolutionary algorithms. On the other hand, membrane algorithms have appeared as alternative hybrid techniques merging together the structure and operators of membrane systems, along with the capabilities of optimization algorithms inside each membrane. This paper explores the ability of a new variants of one-level membrane systems using a recent variant of evolutionary algorithm dynamically using different genetic operators depending on the best fitness found. The experimental results presented show that this new algorithm, called DOGAPS, outperforms the dynamic evolutionary algorithm, with the extra value provided by the membrane system. Additionally, the role of some parameters involved in our algorithm are analyzed, including the number of membranes, iterations per membrane or mutation rate.
Considering a class R comprising recognizer membrane systems with the capability of providing polynomial-time and uniform solutions for NP-complete problems (referred to as a “presumably efficient” class), the corresponding polynomial-time complexity class PMCR encompasses both the NP and co – NP classes. Specifically, when R represents the class of recognizer presumably efficient cell-like P systems that incorporate object evolution rules, communication rules, and dissolution rules, PMCR includes both the DP and co – DP classes. Here, DP signifies the class of languages that can be expressed as the difference between any two languages in NP (it is worth noting that NP ⊆ DP and co – NP ⊆ co – DP). As DP-complete problems are believed to be more complex than NP-complete problems, they serve as promising candidates for studying the P vs NP problem. This outcome has previously been established within the realm of recognizer P systems with active membranes. In this paper, we extend this result to encompass any class R of presumably efficient recognizer tissue-like membrane systems by presenting a detailed protocol for transforming solutions of NP-complete problems into solutions of DP-complete problems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.