Victoria University

Improving the Scalability of XCS-Based Learning Classifier Systems

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dc.contributor.advisor Browne, Will
dc.contributor.advisor Zhang, Mengjie Iqbal, Muhammad 2014-03-12T02:09:45Z 2014-03-12T02:09:45Z 2014 2014
dc.description.abstract Using evolutionary intelligence and machine learning techniques, a broad range of intelligent machines have been designed to perform different tasks. An intelligent machine learns by perceiving its environmental status and taking an action that maximizes its chances of success. Human beings have the ability to apply knowledge learned from a smaller problem to more complex, large-scale problems of the same or a related domain, but currently the vast majority of evolutionary machine learning techniques lack this ability. This lack of ability to apply the already learned knowledge of a domain results in consuming more than the necessary resources and time to solve complex, large-scale problems of the domain. As the problem increases in size, it becomes difficult and even sometimes impractical (if not impossible) to solve due to the needed resources and time. Therefore, in order to scale in a problem domain, a systemis needed that has the ability to reuse the learned knowledge of the domain and/or encapsulate the underlying patterns in the domain. To extract and reuse building blocks of knowledge or to encapsulate the underlying patterns in a problem domain, a rich encoding is needed, but the search space could then expand undesirably and cause bloat, e.g. as in some forms of genetic programming (GP). Learning classifier systems (LCSs) are a well-structured evolutionary computation based learning technique that have pressures to implicitly avoid bloat, such as fitness sharing through niche based reproduction. The proposed thesis is that an LCS can scale to complex problems in a domain by reusing the learnt knowledge from simpler problems of the domain and/or encapsulating the underlying patterns in the domain. Wilson’s XCS is used to implement and test the proposed systems, which is a well-tested, online learning and accuracy based LCS model. To extract the reusable building blocks of knowledge, GP-tree like, code-fragments are introduced, which are more than simply another representation (e.g. ternary or real-valued alphabets). This thesis is extended to capture the underlying patterns in a problemusing a cyclic representation. Hard problems are experimented to test the newly developed scalable systems and compare them with benchmark techniques. Specifically, this work develops four systems to improve the scalability of XCS-based classifier systems. (1) Building blocks of knowledge are extracted fromsmaller problems of a Boolean domain and reused in learning more complex, large-scale problems in the domain, for the first time. By utilizing the learnt knowledge from small-scale problems, the developed XCSCFC (i.e. XCS with Code-Fragment Conditions) system readily solves problems of a scale that existing LCS and GP approaches cannot, e.g. the 135-bitMUX problem. (2) The introduction of the code fragments in classifier actions in XCSCFA (i.e. XCS with Code-Fragment Actions) enables the rich representation of GP, which when couples with the divide and conquer approach of LCS, to successfully solve various complex, overlapping and niche imbalance Boolean problems that are difficult to solve using numeric action based XCS. (3) The underlying patterns in a problem domain are encapsulated in classifier rules encoded by a cyclic representation. The developed XCSSMA system produces general solutions of any scale n for a number of important Boolean problems, for the first time in the field of LCS, e.g. parity problems. (4) Optimal solutions for various real-valued problems are evolved by extending the existing real-valued XCSR system with code-fragment actions to XCSRCFA. Exploiting the combined power of GP and LCS techniques, XCSRCFA successfully learns various continuous action and function approximation problems that are difficult to learn using the base techniques. This research work has shown that LCSs can scale to complex, largescale problems through reusing learnt knowledge. The messy nature, disassociation of message to condition order, masking, feature construction, and reuse of extracted knowledge add additional abilities to the XCS family of LCSs. The ability to use rich encoding in antecedent GP-like codefragments or consequent cyclic representation leads to the evolution of accurate, maximally general and compact solutions in learning various complex Boolean as well as real-valued problems. Effectively exploiting the combined power of GP and LCS techniques, various continuous action and function approximation problems are solved in a simple and straight forward manner. The analysis of the evolved rules reveals, for the first time in XCS, that no matter how specific or general the initial classifiers are, all the optimal classifiers are converged through the mechanism ‘be specific then generalize’ near the final stages of evolution. Also that standard XCS does not use all available information or all available genetic operators to evolve optimal rules, whereas the developed code-fragment action based systems effectively use figure and ground information during the training process. Thiswork has created a platformto explore the reuse of learnt functionality, not just terminal knowledge as present, which is needed to replicate human capabilities. en_NZ
dc.language.iso en_NZ
dc.publisher Victoria University of Wellington en_NZ
dc.subject LCS en_NZ
dc.subject XCS en_NZ
dc.subject Scalability en_NZ
dc.subject Learning classifier systems en_NZ
dc.subject Evolutionary computation en_NZ
dc.title Improving the Scalability of XCS-Based Learning Classifier Systems en_NZ
dc.type Text en_NZ
vuwschema.contributor.unit School of Engineering and Computer Science en_NZ
vuwschema.type.vuw Awarded Doctoral Thesis en_NZ Computer Science en_NZ Victoria University of Wellington en_NZ Doctoral en_NZ Doctor of Philosophy en_NZ
vuwschema.subject.anzsrcfor 080101 Adaptive Agents and Intelligent Robotics en_NZ
vuwschema.subject.anzsrcfor 080108 Neural, Evolutionary and Fuzzy Computation en_NZ
vuwschema.subject.anzsrcfor 080109 Pattern Recognition and Data Mining en_NZ
vuwschema.subject.anzsrcseo 970108 Expanding Knowledhe in the Information and Computing Sciences en_NZ

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