On Designing Adaptive Data Structures with Adaptive Data "Sub"-Structures
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This thesis proposes the use of "Adaptive" Data-Structures (ADSs) that invoke reinforcement learning schemes from the theory of Learning Automata (LA). The ADSs work in conjunction with select re-organization rules to update themselves as they receive queries from the Environment. The result of such a process is to minimize the cost of query accesses. The Environments under consideration are those that exhibit a so-called "locality of reference", and are referred to as Non-stationary Environments (NSEs). A hierarchy of data "sub"-structures is used to design Singly-Linked Lists (SLLs) on SLLs, which contains outer and sub-list contexts. The Object Migration Automaton (OMA) family of reinforcement schemes are employed to capture the probabilistic dependence of the elements query accesses from the Environment within sub-lists. The Enhanced-OMA (EOMA), the Pursuit-EOMA (PEOMA), and the Transitivity-PEOMA (TPEOMA) are incorporated into the hierarchical SLLs. The results are currently the state-of-the-art methods for SLLs operating in NSEs.
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Copyright © 2019 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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