Interpolation-based classifier generation in XCSF

Stein, Anthony; Eymüller, Christian; Rauh, Dominik; Tomforde, Sven; Hähner, Jörg

doi:10.1109/cec.2016.7744296

Cited by 15 publications

(5 citation statements)

References 26 publications

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“…The first function f 1 , a common 2-dimensional benchmark in global optimization called Eggholder function [13], proved to be quite complex to approximate with XCSF in [26] due to its strong and repetitive curvature and multi-modality in both dimensions. These properties are also featured by the 2-dimensional Sine-in-Sine function f 2 [24], which thereby exhibits a higher degree of variation of the curvature and was applied for XCSF assessment in [24,25].…”

Section: Results In Regression Tasksmentioning

confidence: 99%

Mechanisms to Alleviate Over-Generalization in XCS for Continuous-Valued Input Spaces

Wagner

Stein

2022

SN COMPUT. SCI.

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In the field of rule-based approaches to Machine Learning, the XCS classifier system (XCS) is a well-known representative of the learning classifier systems family. By using a genetic algorithm (GA), the XCS aims at forming rules or so-called classifiers which are as general as possible to achieve an optimal performance level. A too high generalization pressure may lead to over-general classifiers degrading the performance of XCS. To date, no method exists for XCS for real-valued input spaces (XCSR) and XCS for function approximation (XCSF) to handle over-general classifiers ensuring an accurate population. The Absumption mechanism and the Specify operator, both developed for XCS with binary inputs, provide a promising basis for over-generality handling in XCSR and XCSF. This paper introduces adapted versions of Absumption and Specify by proposing different identification and specialization strategies for the application in XCSR and XCSF. To determine their potential, the adapted techniques are evaluated in different classification problems, i.e., common benchmarks and real-world data from the agricultural domain, in a multi-step problem as well as different regression tasks. Our experimental results show that the application of these techniques leads to significant improvements of the accuracy of the generated classifier population in the applied benchmarks, data sets, multi-step problems and regression tasks, especially when they tend to form over-general classifiers. Furthermore, considering the working principle of the proposed techniques, the intended decrease in overall classifier generality can be confirmed.

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Section: Results In Regression Tasksmentioning

confidence: 99%

Mechanisms to Alleviate Over-Generalization in XCS for Continuous-Valued Input Spaces

Wagner

Stein

2022

SN COMPUT. SCI.

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“…Especially for the WBC data set, of which already standard XCS is very capable, the abovementioned effect resulted in a slightly but indeed significant decrease of the average reward received over the entire learning period, even if the prediction error itself can be significantly improved. In order to attenuate this effect, recently proposed interpolation-based action-selection and classifier generation schemes [34,38], found to effectively decrease initial prediction errors, could be employed.…”

Section: Results On Single-step Problemsmentioning

confidence: 99%

XCS Classifier System with Experience Replay

Stein¹,

Maier²,

Rosenbauer³

et al. 2020

Preprint

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XCS constitutes the most deeply investigated classifier system today. It bears strong potentials and comes with inherent capabilities for mastering a variety of different learning tasks. Besides outstanding successes in various classification and regression tasks, XCS also proved very effective in certain multi-step environments from the domain of reinforcement learning. Especially in the latter domain, recent advances have been mainly driven by algorithms which model their policies based on deep neural networks -among which the Deep-Q-Network (DQN) is a prominent representative. Experience Replay (ER) constitutes one of the crucial factors for the DQN's successes, since it facilitates stabilized training of the neural network-based Q-function approximators. Surprisingly, XCSbarely takes advantage of similar mechanisms that leverage stored raw experiences encountered so far. To bridge this gap, this paper investigates the benefits of extending XCS with ER. On the one hand, we demonstrate that for single-step tasks ER bears massive potential for improvements in terms of sample efficiency. On the shady side, however, we reveal that the use of ER might further aggravate well-studied issues not yet solved for XCS when applied to sequential decision problems demanding for long-action-chains.

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“…MARL is also used in [49] to tackle the complexity emerging in MAS domains. Especially the "Extended Classifier System" (XCS) variant by Wilson [50] (including its variants from the OC domain such as [51][52][53][54]), which has been widely used for implementing self-adaptation with runtime learning capabilities. For instance, XCS can be seen as an integral part of OC systems that are said to exhibit self-learning properties.…”

Section: Self-optimisationmentioning

confidence: 99%

The Vision of Self-Management in Cognitive Organic Power Distribution Systems

et al. 2022

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Due to the ongoing trend towards a decarbonisation of energy use, the power system is expected to become the backbone of all energy sectors and thus the fundamental critical infrastructure. High penetration with distributed energy resources demands the coordination of a large number of prosumers, partly controlled by home energy management systems (HEMS), to be designed in such a way that the power system’s operational limits are not violated. On the grid level, distribution management systems (DMS) seek to keep the power system in the normal operational state. On the prosumer level, distributed HEMS optimise the internal power flows by setpoint specification of batteries, photovoltaic generators, or flexible loads. The vision of the ODiS (Organic Distribution System) initiative is to develop an architecture to operate a distribution grid reliably, with high resiliency, and fully autonomously by developing “organic” HEMS and DMS which possess multiple self-x capabilities, collectively referred to as self-management. Thus, ODiS seeks answers to the following question: How can we create the most appropriate models, techniques, and algorithms to develop novel kinds of self-configuring, self-organising, self-healing, and self-optimising DMS that are integrally coupled with the distributed HEMS? In this concept paper, the vision of ODiS is presented in detail based on a thorough review of the state of the art.

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Interpolation-based classifier generation in XCSF

Cited by 15 publications

References 26 publications

Mechanisms to Alleviate Over-Generalization in XCS for Continuous-Valued Input Spaces

Mechanisms to Alleviate Over-Generalization in XCS for Continuous-Valued Input Spaces

XCS Classifier System with Experience Replay

The Vision of Self-Management in Cognitive Organic Power Distribution Systems

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