51 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
An Improved Continuous-Action Extended Classifier Systems for Function Approximation
AbstractDue to their structural simplicity and superior generalization capability, Extended Classifier Systems (XCSs) are gaining popularity within the Artificial Intelligence community. In this study an improved XCS with continuous actions is introduced for function approximation purposes. The proposed XCSF uses “prediction zones,” rather than distinct “prediction values,” to enable multi-member match sets that would allow multiple rules to be evaluated per training step. It is shown that this would accelerate the training procedure and reduce the computational cost associated with the training phase. The improved XCSF is also shown to produce more accurate rules than the classical classifier system when it comes to approximating complex nonlinear functions
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
Optimality-based Analysis of XCSF Compaction in Discrete Reinforcement Learning
Learning classifier systems (LCSs) are population-based predictive systems
that were originally envisioned as agents to act in reinforcement learning (RL)
environments. These systems can suffer from population bloat and so are
amenable to compaction techniques that try to strike a balance between
population size and performance. A well-studied LCS architecture is XCSF, which
in the RL setting acts as a Q-function approximator. We apply XCSF to a
deterministic and stochastic variant of the FrozenLake8x8 environment from
OpenAI Gym, with its performance compared in terms of function approximation
error and policy accuracy to the optimal Q-functions and policies produced by
solving the environments via dynamic programming. We then introduce a novel
compaction algorithm (Greedy Niche Mass Compaction - GNMC) and study its
operation on XCSF's trained populations. Results show that given a suitable
parametrisation, GNMC preserves or even slightly improves function
approximation error while yielding a significant reduction in population size.
Reasonable preservation of policy accuracy also occurs, and we link this metric
to the commonly used steps-to-goal metric in maze-like environments,
illustrating how the metrics are complementary rather than competitive
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
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