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Intelligent decision strategy for adaptive resource management in wireless cognitive network

Zhenbang Wang, ZhenYong Wang
2012 7th International Conference on Communications and Networking in China  
Non-Dominated Sorting Genetic Algorithm and Fuzzy Decision Making are introduced in learning-reasoning strategy to abstract cognition information to "knowledge", and save the "knowledge" into history-case  ...  In this paper, an intelligent decision strategy with learning-reasoning mechanism and decision-evaluation process is proposed to classify, select and optimize the large adjustable parameters for network  ...  Learning-reasoning mechanism Learning-reasoning mechanism is composed of nondominated sorting genetic algorithm and fuzzy decision process.  ... 
doi:10.1109/chinacom.2012.6417459 dblp:conf/chinacom/WangW12 fatcat:frmj53dcdjb3nnlc6j7ygo4jqa

Event-driven learning classifier systems for online soccer games

Yuji Sato, Ryutaro Kanno
2005 Proceedings of the 2005 conference on Genetic and evolutionary computation - GECCO '05  
decision-making algorithms in real time.  ...  A hybrid system combining an existing strategy algorithm and a classifier system is also employed.  ...  Second, the appearance of users with advanced techniques has generated a need for decision-making algorithms under even more complicated environments.  ... 
doi:10.1145/1068009.1068374 dblp:conf/gecco/SatoK05 fatcat:o5zllbdbgfamdbn52eftlsmapq

Learning to play like a human: case injected genetic algorithms for strategic computer gaming

Sushil J. Louis, Chris Miles, Kevin Schum, Alex F. Sisti
2006 Modeling and Simulation for Military Applications  
We use case injected genetic algorithms to learn how to competently play computer strategy games that involve long range planning across complex dynamics.  ...  Results show that with an appropriate representation, case injection is effective at biasing the genetic algorithm toward producing plans that contain important strategic elements used by human players  ...  Results show that case injection combined with a flexible representation biases the genetic algorithm toward producing strategies similar to those learned from human players.  ... 
doi:10.1117/12.668273 fatcat:eltykjmy2fbktgwptqt53hjwzu

Decision analytics and machine learning in economic and financial systems

Qifeng Qiao, Peter A. Beling
2016 Environment Systems and Decisions  
Decision analytics may be viewed as the combined use of predictive modeling techniques (e.g., forecasting and machine learning) and prescriptive decision frameworks (e.g., optimization and simulation)  ...  Decision analytics has long been used in the domains of economic and financial systems, with credit scoring being an example of an early success, and the clear trend is to the development of ever more  ...  Mullei and Beling (1998) used genetic algorithms to learn technical decision rules that then are the basis for trading decisions.  ... 
doi:10.1007/s10669-016-9601-x fatcat:idaougsjbvamph5lozot42dn6m

A Study on use of Artificial Intelligence for Stock Market Prediction - An Exploratory Re

B Nagesh, N Umesha
2018 Zenodo  
It summarizes the findings of systematic approach over building trade system and an application of artificial intelligence (mainly genetic algorithms and neural networks).  ...  Artificial intelligence helps in conceiving large quartiles of data there by making investment decisions more accurate.  ...  Strategies of Genetic Algorithm Pairs trading This is a long short ideally market neutral strategy enabling traders to profit from transient discrepancies in relatives value of close substitute.  ... 
doi:10.5281/zenodo.1403551 fatcat:kr63e62ou5e5fjwk2oqkcjtw4q

Page 3236 of Mathematical Reviews Vol. , Issue 97E [page]

1997 Mathematical Reviews  
In order to realize this, we introduce a formal context for this problem and a genetic algorithm on decision trees.  ...  (B-RUCA-MI; Antwerp) Genetic algorithms and trees. Il. Strategy trees (the variable width case). Comput. Artificial Intelligence 14 (1995), no. 5, 417-434.  ... 

Self-adaptive hybrid genetic algorithm using an ant-based algorithm

Tarek A. El-Mihoub, Adrian Hopgood, Ibrahim A. Aref
2014 2014 IEEE International Symposium on Robotics and Manufacturing Automation (ROMA)  
In this paper, a novel form of hybridization between an ant-based algorithm and a genetic-local hybrid algorithm is proposed.  ...  An ant colony optimization algorithm is used to monitor the behavior of a genetic-local hybrid algorithm and dynamically adjust its control parameters to optimize the exploitation exploration balance according  ...  The decision is taken locally based on the current ant's state and using the decision policy given in Equation 1. The ant's tour ends by choosing one of the available learning strategies.  ... 
doi:10.1109/roma.2014.7295881 fatcat:lsymjyogpjestigimdj52f2cnu

Can Genetic Algorithms Explain Experimental Anomalies?

Marco Casari
2004 Computational Economics  
Agent interaction is simulated using an individual learning genetic algorithm, where agents have constraints in their working memory, a limited ability to maximize, and experiment with new strategies.  ...  Instead of positing individual-specific utility functions, we model decision makers as selfish and identical.  ...  The genetic algorithm decision maker can be described as follow. A strategy is identified by a single real number.  ... 
doi:10.1007/s10614-004-4197-5 fatcat:gf4esybuwrhfnm2qafojwb3l7m

Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm

Man Leung Wong, Yuan Yuan Guo
2008 Decision Support Systems  
We also apply the method to a data mining problem and compare the performance of the discovered Bayesian networks with the models generated by other learning algorithms.  ...  This paper proposes a novel method for learning Bayesian networks from incomplete databases in the presence of missing values, which combines an evolutionary algorithm with the traditional Expectation  ...  Different genetic operators have been designed and employed to find individuals with higher scores.  ... 
doi:10.1016/j.dss.2008.01.002 fatcat:jk5wsgyonndytjeoiqofxbssw4

Social creativity as a function of agent cognition and network properties: A computer model

Siddhartha Bhattacharyya, Stellan Ohlsson
2010 Social Networks  
 "Genetic Learning of Decision Rules in Financial Markets", with K. Mehta, INFORMS National Meeting, Montreol, 1998.  "Forma Theoretic Genetic Search Over Probability Spaces", with M.D.  ...  Algorithms", with G.J.  ... 
doi:10.1016/j.socnet.2010.04.001 fatcat:ibtxflpv5zgwpnznrktd5yqkui

Chapter 19 Agent-Based Models and Human Subject Experiments [chapter]

John Duffy
2006 Handbook of Computational Economics  
Abstract This chapter examines the relationship between agent-based modeling and economic decision-making experiments with human subjects.  ...  used a genetic algorithm to evolve strategies (it did).  ...  At the same time, most of the studies treat the genetic algorithm as a kind of black box generator of new-and-improved decisions or strategies, without much regard to the interpretation of genetic operators  ... 
doi:10.1016/s1574-0021(05)02019-8 fatcat:3sdkbfigsrh5lhfcxspzor6hky

A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree

Micheal Olaolu Arowolo, Marion Olubunmi Adebiyi, Adebiyi Ayodele Ariyo, Olatunji Julius Okesola
2021 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
In this study, a genetic algorithm (GA) pre-processor, to obtain reduced dimensionality of data with kth nearest neighbor (KNN) and decision tree classifiers are proposed to classify discrete genetic structures  ...  Scientists have used machine learning algorithms with relevant achievement for gene expression data results TELKOMNIKA Telecommun Comput El Control  A genetic algorithm approach for predicting ribonucleic  ... 
doi:10.12928/telkomnika.v19i1.16381 fatcat:lwdhlxkqufd3fcgsdcd3y2glca

Enhancing Greedy Policy Techniques for Complex Cost-Sensitive Problems [chapter]

Camelia Vidrighin, Rodica Potole
2008 Greedy Algorithms  
These assumptions and the search strategy employed constitute the bias of the learning algorithm. This bias restricts the area of successful application of the algorithm.  ...  Each machine learning algorithm makes assumptions regarding the underlying problems it needs to solve.  ...  The greedy strategy takes the best local decision, with no evaluation of further effort.  ... 
doi:10.5772/6357 fatcat:kgswd7govncwtcau2gko5l3abi

Learning and behavioral stability

Thomas Riechmann
1999 Journal of evolutionary economics  
It is shown that genetic algorithm learning is a compound of three different learning schemes. First, every particular scheme is analyzed.  ...  On the other hand, recent economic literature uses genetic algorithms as a metaphor for social learning.  ...  The Compound: Genetic Algorithm Learning Thus, does genetic algorithm learning lead to behavioral stability?  ... 
doi:10.1007/s001910050082 fatcat:tbisav3afja6hpfkvjbxe3amda

Page 4956 of Mathematical Reviews Vol. , Issue 99g [page]

1999 Mathematical Reviews  
A brief but lucid description of several mainstream variants of genetic algorithms is given and their relationship to evolutionary strategies and evolutionary computation is presented.  ...  (electronic)], we gave an online scheme for learning a very general class of decision regions, together with conditions both on the parametrization and on the sequence of input examples under which good  ... 
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