Learning Classifier System (LCS)-based System
(Redirected from Genetics-Based Machine Learning)
Jump to navigation
Jump to search
A Learning Classifier System (LCS)-based System is a rule-based learning system that combines a temporal difference learning or a supervised learning algorithm with a genetic algorithm.
- Context:
- It can solve to solve a Machine Learning Classification Tasks and Reinforcement Learning Tasks.
- It can range from being a Michigan-Style Classifier System to being a Pittsburgh-Style Classifier System.
- It can range from being a Sequential Classifier System to being an Improved Classifier System.
- It can range from being a Distributed Classifier System to being a Parallel Learning Classifier System.
- It can range from being a Strength-Based Learning Classifier System to being an Accuracy-Based Learning Classifier System.
- Example(s):
- ACS, ACS2;
- ADAM;
- ALECSYS;
- Animat CS;
- ATNoSFERES, ATNoSFERES-II;
- AXCS;
- BCS;
- BioHEL;
- BOOLE, BOOLE++, NEWBOOLE;
- CB-HXCS;
- CS-1;
- CFCS2;
- ClaDia;
- COGIN;
- CXCS;
- DXCS;
- ELF;
- ExSTraCS;
- EpiCS;
- EpiXCS;
- GABIL;
- GALE, GALE2;
- GA-Miner;
- GARGLE;
- GAssist;
- GCS, rGCS;
- GIL;
- GOFER, GOFER-1;
- HCS;
- iLCS;
- LS-1, LS-2, Fuzzy LCS;
- LCSE;
- MACS;
- MCS;
- MILCS;
- MOLCS;
- MOLeCS;
- NAX;
- NCS;
- NLCS;
- OCS;
- PICS;
- REGAL;
- RUDI;
- SAMUEL;
- SBXCS;
- SCS;
- Standard CS;
- TCS;
- UCS, Fuzzy UCS;
- XACS;
- XCS [1];
- XCSCA;
- XCSF;
- XCSFG;
- XCSFGC;
- XCSFGH;
- XCSFNN;
- XCSI;
- XCSM;
- XCSMH;
- XCSR;
- XCSTS;
- X-NFCS;
- X-NCS;
- YACS;
- YCS;
- YCSL;
- XCSQ;
- ZCCS;
- ZCS;
- ZCSM.
- …
- Counter-Example(s):
- See: Credit Assignment; Reinforcement Learning; Rule Learning, Online Learning, Offline Learning, Artificial Adaptive System, Complex Adaptive Systems.
References
2019
- (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Learning_classifier_system Retrieved:2019-1-12.
- Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (e.g. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning).[1] Learning classifier systems seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions (e.g. behavior modeling[2], classification[3] [4], data mining [5][6], regression[7], function approximation[8], or game strategy). This approach allows complex solution spaces to be broken up into smaller, simpler parts.
The founding concepts behind learning classifier systems came from attempts to model complex adaptive systems, using rule-based agents to form an artificial cognitive system (i.e. artificial intelligence).
- Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (e.g. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning).[1] Learning classifier systems seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions (e.g. behavior modeling[2], classification[3] [4], data mining [5][6], regression[7], function approximation[8], or game strategy). This approach allows complex solution spaces to be broken up into smaller, simpler parts.
- ↑ Urbanowicz & Moore, 2009
- ↑ Dorigo, Marco (1995). “Alecsys and the AutonoMouse: Learning to control a real robot by distributed classifier systems". Machine Learning. 19 (3): 209–240. doi:10.1007/BF00996270. ISSN 0885-6125.
- ↑ Bernadó-Mansilla, Ester; Garrell-Guiu, Josep M. (2003-09-01). “Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks". Evolutionary Computation. 11 (3): 209–238. doi:10.1162/106365603322365289. ISSN 1063-6560. PMID 14558911.
- ↑ Urbanowicz, Ryan J.; Moore, Jason H. (2015-04-03). "ExSTraCS 2.0: description and evaluation of a scalable learning classifier system". Evolutionary Intelligence. 8 (2–3): 89–116. doi:10.1007/s12065-015-0128-8. ISSN 1864-5909. PMC 4583133. PMID 26417393.
- ↑ Bernadó, Ester; Llorà, Xavier; Garrell, Josep M. (2001-07-07). Lanzi, Pier Luca; Stolzmann, Wolfgang; Wilson, Stewart W., eds. Advances in Learning Classifier Systems. Lecture Notes in Computer Science. Springer Berlin Heidelberg. pp. 115–132. doi:10.1007/3-540-48104-4_8. ISBN 9783540437932.
- ↑ Bacardit, Jaume; Butz, Martin V. (2007-01-01). Kovacs, Tim; Llorà, Xavier; Takadama, Keiki; Lanzi, Pier Luca; Stolzmann, Wolfgang; Wilson, Stewart W., eds. Learning Classifier Systems. Lecture Notes in Computer Science. Springer Berlin Heidelberg. pp. 282–290. CiteSeerX 10.1.1.553.4679. doi:10.1007/978-3-540-71231-2_19. ISBN 9783540712305.
- ↑ Urbanowicz, Ryan; Ramanand, Niranjan; Moore, Jason (2015-01-01). Continuous Endpoint Data Mining with ExSTraCS: A Supervised Learning Classifier System. Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation. GECCO Companion '15. New York, NY, USA: ACM. pp. 1029–1036. doi:10.1145/2739482.2768453. ISBN 9781450334884.
- ↑ Butz, M. V.; Lanzi, P. L.; Wilson, S. W. (2008-06-01). "Function Approximation With XCS: Hyperellipsoidal Conditions, Recursive Least Squares, and Compaction". IEEE Transactions on Evolutionary Computation. 12 (3): 355–376. doi:10.1109/TEVC.2007.903551. ISSN 1089-778X.
2017
- (Lanzi, 2017) ⇒ Pier Luca Lanzi. (2017). "Classifier Systems". In: (Sammut & Webb, 2017). DOI:10.1007/978-1-4899-7687-1_941
- QUOTE: Classifier systems are rule-based systems that combine temporal difference learning or supervised learning with a genetic algorithm to solve classification and reinforcement learning problems. Classifier systems come in two flavors: Michigan classifier systems, which are designed for online learning, but can also tackle offline problems; and Pittsburgh classifier systems, which can only be applied to offline learning.
2009
- (Urbanowicz & Moore, 2009) ⇒ Ryan J. Urbanowicz, and Jason H. Moore. (2009). "Learning Classifier Systems: A Complete Introduction, Review, and Roadmap".; In: Journal of Artificial Evolution and Applications, 2009. Hindawi Publishing Corporation. doi:10.1155/2009/736398
- QUOTE: ... we can begin to describe the LCS algorithm. At the heart of this algorithm is the idea that, when dealing with complex systems, seeking a single best-fit model is less desirable than evolving a population of rules which collectively model that system. LCSs represent the merger of different fields of research encapsulated within a single algorithm. Figure 1 illustrates the field hierarchy that founds the LCS algorithmic concept.
.
- QUOTE: ... we can begin to describe the LCS algorithm. At the heart of this algorithm is the idea that, when dealing with complex systems, seeking a single best-fit model is less desirable than evolving a population of rules which collectively model that system. LCSs represent the merger of different fields of research encapsulated within a single algorithm. Figure 1 illustrates the field hierarchy that founds the LCS algorithmic concept.
