18 research outputs found
Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
A number of representation schemes have been presented for use within
learning classifier systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using discrete and fuzzy
dynamical system representations within the XCSF learning classifier system. In
particular, asynchronous random Boolean networks are used to represent the
traditional condition-action production system rules in the discrete case and
asynchronous fuzzy logic networks in the continuous-valued case. It is shown
possible to use self-adaptive, open-ended evolution to design an ensemble of
such dynamical systems within XCSF to solve a number of well-known test
problems
Fuzzy Dynamical Genetic Programming in XCSF
A number of representation schemes have been presented for use within
Learning Classifier Systems, ranging from binary encodings to Neural Networks,
and more recently Dynamical Genetic Programming (DGP). This paper presents
results from an investigation into using a fuzzy DGP representation within the
XCSF Learning Classifier System. In particular, asynchronous Fuzzy Logic
Networks are used to represent the traditional condition-action production
system rules. It is shown possible to use self-adaptive, open-ended evolution
to design an ensemble of such fuzzy dynamical systems within XCSF to solve
several well-known continuous-valued test problems.Comment: 2 page GECCO 2011 poster pape
A brief history of learning classifier systems: from CS-1 to XCS and its variants
© 2015, Springer-Verlag Berlin Heidelberg. The direction set by Wilson’s XCS is that modern Learning Classifier Systems can be characterized by their use of rule accuracy as the utility metric for the search algorithm(s) discovering useful rules. Such searching typically takes place within the restricted space of co-active rules for efficiency. This paper gives an overview of the evolution of Learning Classifier Systems up to XCS, and then of some of the subsequent developments of Wilson’s algorithm to different types of learning
A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers
© 2015, Springer Science+Business Media New York. Learning classifier systems (LCS) are population-based reinforcement learners that were originally designed to model various cognitive phenomena. This paper presents an explicitly cognitive LCS by using spiking neural networks as classifiers, providing each classifier with a measure of temporal dynamism. We employ a constructivist model of growth of both neurons and synaptic connections, which permits a genetic algorithm to automatically evolve sufficiently-complex neural structures. The spiking classifiers are coupled with a temporally-sensitive reinforcement learning algorithm, which allows the system to perform temporal state decomposition by appropriately rewarding “macro-actions”, created by chaining together multiple atomic actions. The combination of temporal reinforcement learning and neural information processing is shown to outperform benchmark neural classifier systems, and successfully solve a robotic navigation task
Improving the Scalability of XCS-Based Learning Classifier Systems
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