On utilizing the transitivity pursuit-enhanced object partitioning to optimize self-organizing lists-on-lists

Abstract

In this paper, the Transitivity Pursuit-Enhanced Object Migration Automata (TPEOMA) is used to capture the dependence of elements in a hierarchical Singly-Linked-Lists on Singly-Linked-Lists (SLLs-on-SLLs) “adaptive” data structure. In doing so, the TPEOMA-enhanced hierarchical SLLs-on-SLLs learns the probability distribution of elements in a Non-stationary Environment. In this framework, we divide the hierarchical Singly-Linked-Lists on Singly-Linked-Lists (SLLs-on-SLLs) into an outer and inner list context. The inner-list context is itself a SLLs containing sub-elements of the list, while the outer-list context contains these sublist partitions as its primitive elements. The elements belonging to a particular sublist partition are determined using the TPEOMA reinforcement learning scheme from the theory of Learning Automata. The idea of Transitivity builds on the Pursuit concept that injects a noise filter into the EOMA to filter divergent queries from the Environment, thereby increasing the likelihood of training the Automaton to approximate the “true” distribution of the Environment. The Transitivity phenomenon can infer “dependent” query pairs from non-accessed elements in the transitivity relation based on the statistical distribution of the queried elements. The TPEOMA-enhanced hierarchical SLLs-on-SLLs schemes results in superior performances to the MTF and TR schemes as well as to the EOMA-enhanced hierarchical SLLs-on-SLLs schemes in NSEs.