Learning Classifier Systems: A Complete Introduction, Review, and Roadmap

Urbanowicz, Ryan J.; Moore, Jason H.

doi:10.1155/2009/736398

Cited by 222 publications

(145 citation statements)

References 142 publications

(223 reference statements)

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“…Studies in this field which attempt to apply the integration of EA and ML techniques have been proven to be beneficial in both convergence speed and solution quality. One of the well-known adaptations of this approach is the learning classifier system (LCS) [22] which has become a powerful tool in a wide range of applications.…”

Section: Proposed Intelligent Hybrid Techniquementioning

confidence: 99%

An Intelligent Content Prefix Classification Approach for Quality of Service Optimization in Information-Centric Networking

et al. 2018

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This research proposes an intelligent classification framework for quality of service (QoS) performance improvement in information-centric networking (ICN). The proposal works towards keyword classification techniques to obtain the most valuable information via suitable content prefixes in ICN. In this study, we have achieved the intelligent function using Artificial Intelligence (AI) implementation. Particularly, to find the most suitable and promising intelligent approach for maintaining QoS matrices, we have evaluated various AI algorithms, including evolutionary algorithms (EA), swarm intelligence (SI), and machine learning (ML) by using the cost function to assess their classification performances. With the goal of enabling a complete ICN prefix classification solution, we also propose a hybrid implementation to optimize classification performances by integration of relevant AI algorithms. This hybrid mechanism searches for a final minimum structure to prevent the local optima from happening. By simulation, the evaluation results show that the proposal outperforms EA and ML in terms of network resource utilization and response delay for QoS performance optimization.

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Section: Proposed Intelligent Hybrid Techniquementioning

confidence: 99%

An Intelligent Content Prefix Classification Approach for Quality of Service Optimization in Information-Centric Networking

et al. 2018

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“…Learning Classifier Systems are a class of machine learning algorithms that combine GAs, RL, Supervised Learning (SL) and Rules into a single architecture (Urbanowicz and Moore 2009). A population of rule-based classifiers describes the behavior of some system.…”

Section: Learning Classifier Systemsmentioning

confidence: 99%

Automatic Algorithm Development Using New Reinforcement Programming Techniques

White¹,

Martinez

Rudolph

2012

Computational Intelligence

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Reinforcement Programming (RP) is a new approach to automatically generating algorithms that uses reinforcement learning techniques. This paper introduces the RP approach and demonstrates its use to generate a generalized, in-place, iterative sort algorithm. The RP approach improves on earlier results that use genetic programming (GP). The resulting algorithm is a novel algorithm that is more efficient than comparable sorting routines. RP learns the sort in fewer iterations than GP and with fewer resources. Experiments establish interesting empirical bounds on learning the sort algorithm: A list of size 4 is sufficient to learn the generalized sort algorithm. The training set only requires one element and learning took less than 200,000 iterations. Additionally RP was used to generate three binary addition algorithms: a full adder, a binary incrementer, and a binary adder.

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“…In the domain of symbolic controllers, the Learning Classifier System (LCS) (Bacardit et al 2008, Lanzi 2008, Urbanowicz and Moore 2009 can be considered one of the best options to cope with the fractured state-action space problem in robot learning. LCS represents a comprehensive class of evolutionary systems that solve reinforcement learning problems by evolving a set of classifiers or rules responsible for handling different local parts of input spaces of the problems.…”

Section: Related Workmentioning

confidence: 99%

Automated synthesis of action selection policies for unmanned vehicles operating in adverse environments

Gupta

2011

Auton Robot

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We address the problem of automated action selection policy synthesis for unmanned vehicles operating in adverse environments. We introduce a new evolutionary computation-based approach using which an initial version of the policy is automatically generated and then gradually refined by detecting and fixing its shortcomings. The synthesis technique consists of the automated extraction of the vehicle's exception states and Genetic Programming (GP) for automated composition and optimization of corrective sequences of commands in the form of macro-actions to be applied locally.The focus is specifically on automated synthesis of a policy for Unmanned Surface Vehicle (USV) to efficiently block the advancement of an intruder boat toward a valuable target. This task requires the USV to utilize reactive planning complemented by short-term forward planning to generate specific maneuvers for blocking. The intruder is human-competitive and exhibits a deceptive behavior so that the USV cannot exploit regularity in its attacking behavior. We compared the performance of a hand-coded blocking policy to the performance of a policy that was automatically synthesized. Our results show that the performance of the automatically generated policy exceeds the performance of the hand-coded policy and thus demonstrates the feasibility of the proposed approach.

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Learning Classifier Systems: A Complete Introduction, Review, and Roadmap

Cited by 222 publications

References 142 publications

An Intelligent Content Prefix Classification Approach for Quality of Service Optimization in Information-Centric Networking

An Intelligent Content Prefix Classification Approach for Quality of Service Optimization in Information-Centric Networking

Automatic Algorithm Development Using New Reinforcement Programming Techniques

Automated synthesis of action selection policies for unmanned vehicles operating in adverse environments

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