Assessment of Complex Adaptive System Changeability Using a Learning Classifier System

2019 ◽  
Vol 13 (3) ◽  
pp. 2177-2188
Author(s):  
Jose Avalos ◽  
Michael W. Grenn ◽  
Blake Roberts
2019 ◽  
Vol 1 (11) ◽  
Author(s):  
Keivan Borna ◽  
Shokoofeh Hoseini ◽  
Mohammad Ali Mehdi Aghaei

Abstract Many different classification algorithms can be use in order to analyze, classify and predict data. Learning classifier system (LCS) which is known as a genetic base machine learning system, combines the machine learning with evolutionary computing and other heuristics to produce an adaptive system that learns to solve a particular problem. This paper uses the Michigan style LCS, in the context of bank customer satisfaction to classify customers into two different groups: unsatisfied/satisfied customers. Three different Rule Compaction strategies are used to compare the rule population’s accuracy and micro/macro population size. The result specifies features that mostly influence prediction.


Glottotheory ◽  
2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Csaba Földes

AbstractThis paper deals with constellations in which, as consequences of linguistic interculturality, elements of two or more languages encounter each other and result in something partially or completely new, an – occasionally temporary – “third quality”, namely hybridity. The paper contributes to the meta-discourse and theory formation by questioning the concept, term and content of “linguistic hybridity”. It also submits a proposal for a typology of linguistic-communicative hybridity that consists of the following prototypical main groups, each with several subtypes: (1) language-cultural, (2) semiotic, (3) medial, (4) communicative, (5) systematic, (6) paraverbal and (7) nonverbal hybridity. At last, the paper examines hybridity as an explanatory variable for language change. In conclusion, hybridity is generally a place of cultural production, with special regard to communication and language it is potentially considered as an incubator of linguistic innovation. Hybridity can be seen as the engine and as the result of language change, or language development. It represents an essential factor by which language functions and develops as a complex adaptive system. Hybridity operates as a continuous cycle. By generating innovation, it triggers language change, which in turn, leads to further and new hybridizations. The processuality of hybridity creates diversity, while at the same time it can cause the vanishing of diversity.


2012 ◽  
Vol 212-213 ◽  
pp. 536-542
Author(s):  
Qiong Su ◽  
Shi Hua He

Based on complex adaptive system theory, the characteristics of water resources allocation system of river basin are analyzed. Evolutionary mechanisms and process of complex adaptive water resources allocation system in Dianchi basin are researched, and also characteristics of "learning". A complex adaptive system model of water-resource allocation is established during analyzing the influence factors and the reaction rules of water consumer agents and water provider agents. And based on this model, water resources in Dianchi basin is allocated only under Dianchi water provider and Zhangjiu river Yunlong reservoir water provider by using the platform of matlab. Finally, corresponding calculation results and conclusions are concluded.


2010 ◽  
Vol 19 (01) ◽  
pp. 275-296 ◽  
Author(s):  
OLGIERD UNOLD

This article introduces a new kind of self-adaptation in discovery mechanism of learning classifier system XCS. Unlike the previous approaches, which incorporate self-adaptive parameters in the representation of an individual, proposed model evolves competitive population of the reduced XCSs, which are able to adapt both classifiers and genetic parameters. The experimental comparisons of self-adaptive mutation rate XCS and standard XCS interacting with 11-bit, 20-bit, and 37-bit multiplexer environment were provided. It has been shown that adapting the mutation rate can give an equivalent or better performance to known good fixed parameter settings, especially for computationally complex tasks. Moreover, the self-adaptive XCS is able to solve the problem of inappropriate for a standard XCS parameters.


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