Inferring the structure and operation of a computing model, given some observations of its behavior, is in general a desirable but daunting task. In this paper we try to solve a constrained version of this problem. We consider a P system Π with active membranes and using cooperative rewriting, communication, and division rules and a collection of pairs of its consecutive configurations. Then, we feed this collection of configurations as input to a (μ+λ) evolutionary algorithm that evolves a population of (initially random) P systems, each with its own rules, with the aim of obtaining an individual that approximates Π as well as possible. We discuss the results obtained on different benchmark problems, designed to test the ability to infer cooperative rewriting, communication, and membrane division rules. We will also provide a description of how fitness results are influenced by different setting of the hyperparameters of the evolutionary algorithm. The results show that the proposed approach is able to find correct solutions for small problems, and it is a promising research direction for the automatic synthesis of P systems.

Leporati, A., Manzoni, L., Mauri, G., Pietropolli, G., Zandron, C. (2023). Inferring P systems from their computing steps: An evolutionary approach. SWARM AND EVOLUTIONARY COMPUTATION, 76(February 2023) [10.1016/j.swevo.2022.101223].

Inferring P systems from their computing steps: An evolutionary approach

Leporati A.
;
Manzoni L.;Mauri G.;Zandron C.
2023

Abstract

Inferring the structure and operation of a computing model, given some observations of its behavior, is in general a desirable but daunting task. In this paper we try to solve a constrained version of this problem. We consider a P system Π with active membranes and using cooperative rewriting, communication, and division rules and a collection of pairs of its consecutive configurations. Then, we feed this collection of configurations as input to a (μ+λ) evolutionary algorithm that evolves a population of (initially random) P systems, each with its own rules, with the aim of obtaining an individual that approximates Π as well as possible. We discuss the results obtained on different benchmark problems, designed to test the ability to infer cooperative rewriting, communication, and membrane division rules. We will also provide a description of how fitness results are influenced by different setting of the hyperparameters of the evolutionary algorithm. The results show that the proposed approach is able to find correct solutions for small problems, and it is a promising research direction for the automatic synthesis of P systems.
Articolo in rivista - Articolo scientifico
(μ+ λ)-EA; Evolutionary algorithms; P systems;
English
7-dic-2022
2023
76
February 2023
101223
none
Leporati, A., Manzoni, L., Mauri, G., Pietropolli, G., Zandron, C. (2023). Inferring P systems from their computing steps: An evolutionary approach. SWARM AND EVOLUTIONARY COMPUTATION, 76(February 2023) [10.1016/j.swevo.2022.101223].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/403355
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