Bio-inspired Information and Communication Technologies. 11th EAI International Conference, BICT 2019, Pittsburgh, PA, USA, March 13–14, 2019, Proceedings

Research Article

Membrane Computing Aggregation (MCA): An Upgraded Framework for Transition P-Systems

  • @INPROCEEDINGS{10.1007/978-3-030-24202-2_15,
        author={Alberto Arteta and Luis Mingo and Nuria Gomez and Yanjun Zhao},
        title={Membrane Computing Aggregation (MCA): An Upgraded Framework for Transition P-Systems},
        proceedings={Bio-inspired Information and Communication Technologies. 11th EAI International Conference, BICT 2019, Pittsburgh, PA, USA, March 13--14, 2019, Proceedings},
        proceedings_a={BICT},
        year={2019},
        month={7},
        keywords={Membrane system Membrane computing Natural computing},
        doi={10.1007/978-3-030-24202-2_15}
    }
    
  • Alberto Arteta
    Luis Mingo
    Nuria Gomez
    Yanjun Zhao
    Year: 2019
    Membrane Computing Aggregation (MCA): An Upgraded Framework for Transition P-Systems
    BICT
    Springer
    DOI: 10.1007/978-3-030-24202-2_15
Alberto Arteta1,*, Luis Mingo2,*, Nuria Gomez2,*, Yanjun Zhao1,*
  • 1: Troy University
  • 2: Polytechnic University of Madrid
*Contact email: aarteta@troy.edu, lfmingo@eui.upm.es, ngomez@eui.upm.es, yjzhao@troy.edu

Abstract

MCA (Membrane computing aggregation is experimental computational frame. It is inspired by the inner properties of membrane cells (Bio-inspired system). It is capable of problem solving activities by maintaining a special, “meaningful” relationship with the internal/external environment, integrating its self-reproduction processes within the information flow of incoming and outgoing signals. Because these problem solving capabilities, MCA admits a crucial evolutionary tuning by mutations and recombination of theoretical genetic “bridges” in a so called “aggregation” process ruled by a hierarchical factor that enclosed those capabilities. Throughout the epigenetic capabilities and the cytoskeleton and cell adhesion functionalities, MCA model gain a complex population dynamics specifics and high scalability. Along its developmental process, it can differentiate into meaningful computational tissues and organs that respond to the conditions of the environment and therefore “solve” the morphogenetic/configurational problem. MCA, above all, represents the potential for a new computational paradigm inspired in the higher level processes of membrane cells, endowed with quasi universal processing capabilities beyond the possibilities of cellular automata of and agent processing models.