On Designing Adaptive Data Structures with Adaptive Data "Sub"-Structures
Ekaba Bisong · 2018
Data structures are key pillars for optimizing computational efficiency, as they contribute in no small measure to enhancing the "speed" in the accessing and subsequent processing of data.The need for enhancing speed is critical for almost all applications and domains, and this is most relevant when real-time or near real-time efficiency is desired.This thesis proposes the use of "Adaptive" Data-Structures (ADSs) that invoke reinforcement learning schemes from the theory of Learning Automata (LA).These operate in conjunction with select re-organization rules to update themselves as they receive queries from the Environment of interaction.The result of such a process is the subsequent minimization of the cost associated with 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) First and foremost, I give thanks to the Lord, my God, for He is good, and His mercies endure forever.Let all the earth praise the Name of the Lord God and His Son Jesus Christ, who is blessed forever and ever.I am particularly grateful to my Supervisor Prof. B. John Oommen.He has been a rock and a support to me.He taught me all that I know about the field of learning automata, and by extension, the broader body of reinforcement learning.Prof. Oommen was my mentor and received me as his son.He was an example of what it means to live in holiness and righteousness.I am privileged to sit and learn under him.I would like to offer special thanks to my parents