A Hybrid Expert System for Generating Stock Trading Signals
|
|
- Horace O’Neal’
- 8 years ago
- Views:
Transcription
1 World cadey of Scence, Engneerng and Technology Internatonal Journal of Coputer, Electrcal, utoaton, Control and Inforaton Engneerng Vol:10, No:7, 2016 Hybrd Expert Syste for Generatng Stock Tradng Sgnals Hosen Hasheh ahar, Mohaad Hossen Fazel Zarand, kbar Esfahanpour Internatonal Scence Index, Coputer and Inforaton Engneerng Vol:10, No:7, 2016 waset.org/publcaton/ bstract In ths paper, a hybrd expert syste s developed by usng fuzzy genetc network prograng wth renforceent learnng (GNP-RL). In ths syste, the frae-based structure of the syste uses the tradng rules extracted by GNP. These rules are extracted by usng techncal ndces of the stock prces n the tranng te perod. For developng ths syste, we appled fuzzy node transton and decson akng n both processng and judgent nodes of GNP-RL. Consequently, usng these ethod not only dd ncrease the accuracy of node transton and decson akng n GNP's nodes, but also extended the GNP's bnary sgnals to ternary tradng sgnals. In the other words, n our proposed Fuzzy GNP-RL odel, a No Trade sgnal s added to conventonal uy or Sell sgnals. Fnally, the obtaned rules are used n a frae-based syste pleented n Kappa-PC software. Ths developed tradng syste has been used to generate tradng sgnals for ten copanes lsted n Tehran Stock Exchange (TSE). The sulaton results n the testng te perod shows that the developed syste has ore favorable perforance n coparson wth the uy and Hold strategy. Keywords Fuzzy genetc network prograng, hybrd expert syste, techncal tradng sgnal, Tehran stock exchange. I. INTRODUCTION XPERT systes as odern technologcal tools havng the Eablty to perfor soe specfc assgned tasks, has ganed ore and ore popularty through the te. ecause of the attractveness of stock prces, they have been under consderaton n recent years, and nuerous researches have been done to forecast the or to realze ther behavor n arkets. In ths regard, techncal ndces and ndcators have been used to generate stock tradng rules based on ther prces and exchange volue. On the ground that choosng an approprate techncal ndex or ndces at the rght te seeed coplcated, researchers tred to apply evolutonary algorths such as rtfcal Neural Networks (NN) [1], Genetc lgorth (G) [2], Genetc Prograng (GP), Genetc Network Prograng (GNP) etc. to overcoe ths challenge. GP [3] as an extenson of G [4] havng a tree structure, has been wdely used for generatng techncal tradng rules [5], [6]. Furtherore, GNP, as an extenson of GP [7], represents each ndvdual as a network havng three knds of nodes naed start node, processng nodes and judgent nodes whch are connected to each other approprately. H. Hasheh ahar s wth the Departent of Industral Engneerng and Manageent Systes, rkabr Unversty of Technology, Tehran, Iran (correspondng author; e-al: hash@aut.ac.r). M. H. F. Zarand and. Esfahanpour are wth the Departent of Industral Engneerng and Manageent Systes, rkabr Unversty of Technology, Tehran, Iran (e-al: zarand@aut.ac.r. esfahaa@aut.ac.r). GNP was used for extractng tradng rules base on candlestck charts n [8] for the frst te. In [9], [10], Renforceent Learnng (RL) was added to GNP by consderng two sub nodes for each node. y addng RL to GNP, the ore desrable the sub node perfors n decson akng, the ore the probablty of vstng t n the followng becoes. fter that n [11] control nodes were added to GNP, and ade t possble to be used for portfolo optzaton. In [12], [13] Chen et al. used Te daptng GNP for the portfolo optzaton. ecause the trend of prces s changng, usng a prce wndow whch shfts through the te durng the evoluton phase akes the ndvduals adapt to changng trends. Fuzzy GNP for the frst te was proposed by Sendar et al. [14]. In ther paper, they used the fuzzy judgent nodes for decdng the next node consderng that fuzzy judgent nodes whch deterne the next node probablstcally nstead of soe judgent nodes wth a threshold, ade the odel ore realstc. In [15], Mabu et al. appled GNP for generatng stock tradng rules. In ther research, durng the evoluton phase dfferent rules were generated out of the elte ndvdual and were accuulated n the approprate rule pool. Dfferent rule pools were created based on the prce's trend and the generated tradng sgnal. t last, the decson akng was based on accuulated rules n the rule pools. In followng years, Chen and Wang proposed a Rsk-djusted GNP for portfolo optzaton [16]. Consequently, they used condtonal Sharp Rato as the ftness functon of ther Rsk-djusted odel. In condtonal Sharp Rato, the devaton of return fro ts average value wll be dvded by Condtonal Value-at-Rsk. In bref, n ths paper usng fuzzy processng nodes and applyng tradng rules n a frae-based structure contrbuted perforance enhanceent to the conventonal GNP-RL. In the followng, the pleentaton of these contrbutons and ther effectveness wll coe under delberaton. The rest of the paper s organzed as follows. In Secton II, the proposed Fuzzy GNP-RL s descrbed. In Secton III, pleentaton of the proposed frae-based expert syste n Kappa-PC s explaned, and n Secton IV, results are reported. Fnally, the paper closes wth our concluson. II. FUZZY GNP In GNP, each ndvdual s a network havng three knds of nodes. s shown n Fg. 1, t conssts of a start node and several judgent and processng nodes. The start node just specfes the frst node, and does not have any other role n perforance of the graph structure. In judgent nodes, a Internatonal Scholarly and Scentfc Research & Innovaton 10(7)
2 World cadey of Scence, Engneerng and Technology Internatonal Journal of Coputer, Electrcal, utoaton, Control and Inforaton Engneerng Vol:10, No:7, 2016 Internatonal Scence Index, Coputer and Inforaton Engneerng Vol:10, No:7, 2016 waset.org/publcaton/ techncal ndcator wll be used for judgents, and n processng nodes, the decson to uy, Sell or No Trade wll be ade. In vrtue of GNP's graph structure, t s possble to use nodes repeatedly. Consequently, ths possblty culnates n copact structure of GNP, and prevents bloatng phenoenon whch s ore prevalent n GP [9].. Genotype of Fuzzy GNP-RL In Fg. 2, the structure of judgent and processng nodes and the genes correspondng to the are presented. S J P S Start Node Judgent Node Processng Node J1 J2 J5 J4 Fg. 1 asc Structure of GNP In gene representaton, NT represents the node type. In fact, NT 0 eans the node s start node, and NT 1 shows processng nodes, and NT 2 represents judgent nodes. The. RL n Fuzzy GNP-RL The learnng phase of ths odel s based on Sarsa algorth [17]. s shown n Fg. 2, n the proposed GNP odel, each node conssts of two sub nodes, and one of the s chosen accordng to ther Q values. Here, each node s consdered as "state" and the selecton of the sub node s defned as "acton". The sub nodes are chosen usng є-greedy polcy. ased on that, the sub node wth the greater Q value s selected wth probablty of 1-є and the other way around. fter choosng J3 J6 J7 Fg. 2 Judgent and Processng Nodes' Structure d represents the te delay correspondng to each knd of nodes. The te delay s the te that t takes to transt fro node to node j. y settng te delay, the nuber of vsted nodes durng each tradng day can be anaged. The te delay for the start node s zero, and for the processng nodes s equal to 5, and for judgent nodes s consdered 1 te unt. In addton, each day conssts of fve te unts. Therefore, ore than one trade cannot be executed n each tradng day. For each sub node, Q represents the Q value needed for RL. The ID shows the dentfcaton nuber of each sub node. For judgent nodes, t specfes the techncal ndex whch should be used, and n processng nodes t ndcates the functon of the sub node whch can be uy or Sell. The and are paraeters whch are used n ebershp functon of each sub node. In judgent of sub nodes, C and C show the nuber of the next node. ased on the techncal ndex, one of the wll be chosen as the next node. If the judgent result s, then the next node wll be C and the other way around. y contrast, there s not condtonal branch n processng nodes, and they always refer to C as ther next node. the approprate sub node and perforng ts task, the Q values are updated as: where Q p Q p r Q Q ) (1) ( jq p Q shows the Q value of the current sub node, and p Q jq shows the Q value of the next sub node. s learnng rate, s the dscount rate and r s obtaned reward snce the prevous processng node. In ths regard, choosng a sub node whch akes proft wll ncrease ts Q value, and consequently wll Internatonal Scholarly and Scentfc Research & Innovaton 10(7)
3 World cadey of Scence, Engneerng and Technology Internatonal Journal of Coputer, Electrcal, utoaton, Control and Inforaton Engneerng Vol:10, No:7, 2016 Internatonal Scence Index, Coputer and Inforaton Engneerng Vol:10, No:7, 2016 waset.org/publcaton/ ncrease ts probablty to be chosen n the followng transtons. ddng RL provdes GNP wth ths ablty to have onlne learnng [9]. In ths way, the learnng s not confned to evoluton phase (offlne learnng), and durng calculaton of the ftness functon of each ndvdual the syste can beneft fro that. C. Node Transton n Fuzzy GNP-RL t frst, the procedure starts wth the start node, and then based on the type of the next node the decson vares as explaned n the followng. 1) Judgent Nodes: f the node s a judgent node, a techncal ndex s selected accordng to ts dentfcaton nuber ( ID ). Each techncal ndex has ts own Iportance Index (IMX) whch shows whether buyng or sellng sgnal s ore lkely to appear. The result obtaned by IMX wll be used later n the next processng node to generate tradng sgnal. s an exaple, n Fg. 3 the IMX for Rate of Devaton (ROD) ndex s ndcated. IMX Rate of Devaton Fg. 3 IMX for Rate of Devaton (ROD) [18] In the next step, the odel should deterne the next transton node usng fuzzy judgent ethod. s shown n gene representaton, each sub node has two fuzzy paraeters whch are used to create ts ebershp functon. y havng and the ebershp functon of judgent and processng nodes wll be created as t can be seen n Fg Fg. 4 Mebershp Functon The procedure of deternng the next node usng the ebershp functon s as: - If ndex value < then the next node would be C - If ndex value > then the next node would be C - If < ndex value < then the next node wll be chosen probablstcally. So, the next node would be C wth the probablty of ( x ), and C wll be selected wth the probablty of x ) 1 ( x ), where x ) and ( ( ( x ) show ndex value's ebershp degree n and. 2) Processng Nodes: n processng nodes, t s necessary to calculate as presented n (2) n order to generate tradng t sgnal. 1 ' t IMX ( ) ' (2) I ' ' I ' where I s the set of judgent nodes beng vsted untl the last processng node. In ths paper, nspred by [14], we extended the GNP-RL and appled fuzzy processng nodes. In ths case, we can generate tradng sgnal usng ebershp functon of processng nodes whch are exactly slar to Fg. 4. Usng fuzzy processng nodes oblges by provdng ternary sgnals. In fact, as t can be seen n followng, usng fuzzy processng node akes t possble to generate No Trade sgnal. Ths possblty helps the odel see ore realstc on the ground that n a lot of stuatons not tradng aybe ore benefcal n coparson wth buyng or sellng. To suarze, generatng sgnal n fuzzy processng nodes s as: In processng nodes wth ID 1 (uyng Nodes): - If t then No Trade - If t then uy - If t then No Trade wth the probablty of ( x ) and uy wth probablty of ( x ) 1 ( x ) In processng nodes wth ID 2(Sellng Nodes): - If t then Sell - If t then No Trade - If t then Sell wth the probablty of ( x ) and No Trade wth probablty of x ) 1 ( x ) ( fter generatng the sgnal, the odel transts to C D. Operators of Fuzzy GNP-RL Fuzzy GNP-RL uses genetc operators such as Crossover and Mutaton n the evoluton phase. For executng operatons, the ndvduals are selected as parents by Tournaent Selecton ethod. The Crossover operator s done as explaned below. - Two parents wll be selected usng tournaent selecton. - One node s selected fro the frst parent wth the probablty of p. c - Selected node wll be exchanged wth ts correspondng node n the other parent. - Two obtaned offsprng wll be consdered as ndvduals of next generaton. Internatonal Scholarly and Scentfc Research & Innovaton 10(7)
4 World cadey of Scence, Engneerng and Technology Internatonal Journal of Coputer, Electrcal, utoaton, Control and Inforaton Engneerng Vol:10, No:7, 2016 Internatonal Scence Index, Coputer and Inforaton Engneerng Vol:10, No:7, 2016 waset.org/publcaton/ The Mutaton operator s executed on one ndvdual. The paraeters ID, C and C are utated as follows. - One ndvdual s selected usng Tournaent selecton as a parent - Each paraeter of selected ndvdual s chosen for utaton wth the probablty of p - Selected paraeters are changed randoly - New ndvdual wll be consdered as an ndvdual of next generaton For utatng and, we use the ethod proposed n [14]. In ths ethod, these paraeter are not utated unforly, but by passng through generaton they change less. The process of utatng fuzzy paraeters s explaned below. - Fuzzy paraeters of each sub node s selected wth the probablty of p - Selected paraeters are utated usng (3). x (, ), 0 ' t U x f x (3) x ( t, x L), f 1 where ' x and x are fuzzy paraeters after and before utaton, and s a rando dgt. U and L are upper bound and lower bound of the paraeter, and t s the current generaton nuber. In addton, ( t, y) s calculated usng (4). 2 t 1 T ( t, y) y 1 r (4) where T s the axu nuber of generatons. - New paraeters consttute the paraeters of utated ndvdual whch transfers to new generaton. Fnally, the flowchart of evoluton phase and extractng tradng rules s ndcated n Fg. 5. E. Desgnng the Frae Structure fter generatng tradng rules obtaned by Fuzzy GNP-RL, we use the n a frae structure to develop our syste. To desgn the frae-based syste, we have used Kappa-PC software. For each stock, a frae s desgned havng slots whch conssts of prce's te seres and extracted rules. ddtonally, there s a frae naed "Syste" whch starts the syste and exposes the generated sgnal. The procedure of generatng tradng sgnals n ths frae-based syste s shown n Fg. 6. III. PPLICTION OF THE SYSTEM IN TEHRN STOCK EXCHNGE In ths secton, we have used the proposed Fuzzy GNP-RL to extract tradng rules for ten stocks traded n TSE. These stocks are selected aong stocks wth the hghest lqudty n the recent years, and are chosen fro dfferent sectors of the arket n order to keep the portfolo well-dversfed. N=N+1 No No No Start Create Intal Populaton N=1 Tradng Tradng Perod Ends? N=Nuber of Indvduals? Yes Mutaton Crossover Last Generaton? Yes Extractng Rules Stop Yes Fg. 5 Flowchart of Evoluton Phase and Extractng Rules [18] Frae of Selected Stock Generatng Tradng Sgnal Tradng Sgnal Rule ase Do You have ths Stock n Hand? Calculatng Indces Syste Frae nnouncng the Sgnal What s Stock s Nae? End Start Fg. 6 Procedure of Generatng Sgnals n the Frae Structure In evoluton phase of the Fuzzy GNP-RL, we use a wndow wth the length of 25 days to evaluate the perforance of ndvduals n ths te perod by calculatng ther ftness functon. To ake the proposed odel ore realstc, ths te wndow shfts through te. In ths case, the ndvduals wll adapt to prce's trend changes. In detal, every two teratons the te wndow shfts one day ahead. Internatonal Scholarly and Scentfc Research & Innovaton 10(7)
5 World cadey of Scence, Engneerng and Technology Internatonal Journal of Coputer, Electrcal, utoaton, Control and Inforaton Engneerng Vol:10, No:7, 2016 Internatonal Scence Index, Coputer and Inforaton Engneerng Vol:10, No:7, 2016 waset.org/publcaton/ Ftness and Reward The reward that s used for updatng Q values n RL s calculated by (5). In that the Reward needs sellng prce to be calculated, t occurs only n sellng processng nodes. Reward = Sellng Prce uyng Prce (5) Moreover, the ftness functon whch s used to evaluate ndvduals n the evoluton phase s as follows. Ftness = Total Proft n the Te Wndow (6). Techncal Indces Techncal ndces are used n judgent nodes to specfy the next node, and to estate the IMX value. In ths paper, we used eght knds of techncal ndces whch were: Relatve Strength Index (RSI), Rate of Devaton (ROD), Rate of Change (ROC), Volue Rato (VR), Stochastcs, Ranked Correlaton Index (RCI), Golden/Dead Cross, Movng verage Convergence Dvergence. The frst sx of the are calculated n three dfferent te perods wth the length of 5, 10 and 15 days. s a result, totally we had 20 (6*3+2=20) techncal ndces for the judgng task. Furtherore, MCD and Golden/Dead Cross ndcators are calculated by usng 5-day Movng verage and 25-day Movng verage of prces. C. Data In order to generate tradng rules, the odel evolves through tranng te perod, and then the perforance of the syste s evaluated n testng te perod. The tranng and testng te perods' length for selected stocks are clarfed n the followng. - Tranng Perod: conssts of 250 tradng days - Testng Perod: conssts of 125 tradng days endng n January 2, D. Paraeter Settngs The paraeters used n the proposed Fuzzy GNP-RL odel are deterned through related studes [15], [19] and shown n Table I. The ntal connecton between nodes, the dentfcaton nuber of sub nodes and fuzzy paraeters are deterned randoly n frst generaton. Moreover, the ntal Q values are set as zero. E. Results s t can be seen n followng, the proposed odel s appled to generate tradng sgnal for 10 copanes lsted n Table II. t frst, the tradng rules are extracted fro the elte ndvdual evolved over the tranng te perod, and then the rules are appled to generate tradng sgnal n testng perod. The results show that the proposed odel outperfors the uy and Hold (&H) strategy. TLE I PRMETER SETTING Populaton sze 301 Nuber of generatons 500 Mutaton 180 Nuber of nodes 61 Crossover 120 Nuber of judgent nodes 40 Elte 1 Nuber of processng nodes 20 Mutaton rate ( p ) 0.02 Learnng rate ( ) 0.1 Crossover rate ( p ) c 0.1 Dscount rate ( ) 0.4 Tournaent sze 2 Є-greedy paraeter 0.1 TLE II RETURNS OTINED Y USING FUZZY GNP-RL Copany &H &S Pars Khodro Co % -5.56% Telecouncaton Copany of Iran 14.07% 5.07% Shazand Petrochecal Co % 2.81% Ghadr Investent Co % 1.7% ank Saderat Iran -7.58% -6.83% Iran Constructon Investent Co % % zarab Industres Co % 21.26% Darou Pakhsh Pharaceutcal Manufacturng Co % 3.91% Islac Republc of Iran Shppng Lnes (IRISL) 2.43% 7.49% Natonal Iranan Lead & Znc Co % 39.39% verage -1.30% 4.42% The results show that applyng developed syste akes a postve return n ths specfc te perod although the prces have a downward trend. IV. CONCLUSION In ths paper, a hybrd expert syste to generate stock tradng sgnals s developed applyng tradng rules n the frae-based structure desgned for the syste. These tradng rules are extracted by usng Fuzzy GNP-RL. Extracted rules are based on techncal ndces of the stock prces. pplyng fuzzy processng nodes provdes the proposed odel wth the ablty to generate ternary sgnals. In order to evaluate the perforance of the developed syste, t s used to generate sgnals for 10 stocks whch are traded n TSE. The results ndcated that the developed syste outperfors the uy and Hold strategy. In fact, usng fuzzy nodes along wth RL contrbutes to generate ore accurate tradng sgnals. REFERENCES [1] Seng-cho, T.C., et al., stock selecton DSS cobnng I and techncal analyss. nnals of Operatons Research, : p [2] auer, R.J., Genetc algorths and nvestent strateges. Vol : John Wley & Sons. [3] Koza, J.R., Genetc prograng: on the prograng of coputers by eans of natural selecton. Vol : MIT press. [4] Holland, J., dapton n natural and artfcal systes. nn rbor MI: The Unversty of Mchgan Press, [5] Mousav, S.,. Esfahanpour, and M.H.F. Zarand, novel approach to dynac portfolo tradng syste usng ulttree genetc prograng. Knowledge-ased Systes, : p [6] Esfahanpour,. and S. Mousav, genetc prograng odel to generate rsk-adjusted techncal tradng rules n stock arkets. Expert Systes wth pplcatons, (7): p [7] Hrasawa, K., et al. Coparson between genetc network prograng (GNP) and genetc prograng (GP). n Evolutonary Coputaton, Proceedngs of the 2001 Congress on IEEE. Internatonal Scholarly and Scentfc Research & Innovaton 10(7)
6 World cadey of Scence, Engneerng and Technology Internatonal Journal of Coputer, Electrcal, utoaton, Control and Inforaton Engneerng Vol:10, No:7, 2016 Internatonal Scence Index, Coputer and Inforaton Engneerng Vol:10, No:7, 2016 waset.org/publcaton/ [8] Izu, Y., et al. Tradng rules on the stock arkets usng genetc network prograng wth candlestck chart. n Evolutonary Coputaton, CEC IEEE Congress on IEEE. [9] Mabu, S., K. Hrasawa, and J. Hu, graph-based evolutonary algorth: genetc network prograng (GNP) and ts extenson usng renforceent learnng. Evolutonary Coputaton, (3): p [10] Mabu, S., et al. Stock tradng rules usng genetc network prograng wth actor-crtc. n Evolutonary Coputaton, CEC IEEE Congress on IEEE. [11] Chen, Y., et al., portfolo optzaton odel usng Genetc Network Prograng wth control nodes. Expert Systes wth pplcatons, (7): p [12] Chen, Y., S. Mabu, and K. Hrasawa, odel of portfolo optzaton usng te adaptng genetc network prograng. Coputers & operatons research, (10): p [13] Chen, Y., et al. Constructng portfolo nvestent strategy based on te adaptng genetc network prograng. n Evolutonary Coputaton, CEC'09. IEEE Congress on IEEE. [14] Sendar, S., S. Mabu, and K. Hrasawa. Fuzzy genetc Network Prograng wth Renforceent Learnng for oble robot navgaton. n Systes, Man, and Cybernetcs (SMC), 2011 IEEE Internatonal Conference on IEEE. [15] Mabu, S., et al., Enhanced decson akng echans of rule-based genetc network prograng for creatng stock tradng sgnals. Expert Systes wth pplcatons, (16): p [16] Chen, Y. and X. Wang, hybrd stock tradng syste usng genetc network prograng and ean condtonal value-at-rsk. European Journal of Operatonal Research, (3): p [17] Chen, Y., et al. Tradng rules on stock arkets usng genetc network prograng wth sarsa learnng. n Proceedngs of the 9th annual conference on Genetc and evolutonary coputaton CM. [18] Chen, Y., et al., genetc network prograng wth learnng approach for enhanced stock tradng odel. Expert Systes wth pplcatons, (10): p [19] Mabu, S., et al. Generatng stock tradng sgnals based on atchng degree wth extracted rules by genetc network prograng. n SICE nnual Conference 2010, Proceedngs of IEEE. Internatonal Scholarly and Scentfc Research & Innovaton 10(7)
Forecasting the Direction and Strength of Stock Market Movement
Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract - Stock market s one of the most complcated systems
More informationAn Electricity Trade Model for Microgrid Communities in Smart Grid
An Electrcty Trade Model for Mcrogrd Countes n Sart Grd Tansong Cu, Yanzh Wang, Shahn Nazaran and Massoud Pedra Unversty of Southern Calforna Departent of Electrcal Engneerng Los Angeles, CA, USA {tcu,
More informationTwo-Phase Traceback of DDoS Attacks with Overlay Network
4th Internatonal Conference on Sensors, Measureent and Intellgent Materals (ICSMIM 205) Two-Phase Traceback of DDoS Attacks wth Overlay Network Zahong Zhou, a, Jang Wang2, b and X Chen3, c -2 School of
More informationCONSTRUCTION OF A COLLABORATIVE VALUE CHAIN IN CLOUD COMPUTING ENVIRONMENT
CONSTRUCTION OF A COLLAORATIVE VALUE CHAIN IN CLOUD COMPUTING ENVIRONMENT Png Wang, School of Econoy and Manageent, Jangsu Unversty of Scence and Technology, Zhenjang Jangsu Chna, sdwangp1975@163.co Zhyng
More informationLeast Squares Fitting of Data
Least Squares Fttng of Data Davd Eberly Geoetrc Tools, LLC http://www.geoetrctools.co/ Copyrght c 1998-2016. All Rghts Reserved. Created: July 15, 1999 Last Modfed: January 5, 2015 Contents 1 Lnear Fttng
More informationRecurrence. 1 Definitions and main statements
Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.
More informationBANDWIDTH ALLOCATION AND PRICING PROBLEM FOR A DUOPOLY MARKET
Yugoslav Journal of Operatons Research (0), Nuber, 65-78 DOI: 0.98/YJOR0065Y BANDWIDTH ALLOCATION AND PRICING PROBLEM FOR A DUOPOLY MARKET Peng-Sheng YOU Graduate Insttute of Marketng and Logstcs/Transportaton,
More informationCourse outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy
Fnancal Tme Seres Analyss Patrck McSharry patrck@mcsharry.net www.mcsharry.net Trnty Term 2014 Mathematcal Insttute Unversty of Oxford Course outlne 1. Data analyss, probablty, correlatons, vsualsaton
More information1. Introduction. Graham Kendall School of Computer Science and IT ASAP Research Group University of Nottingham Nottingham, NG8 1BB gxk@cs.nott.ac.
The Co-evoluton of Tradng Strateges n A Mult-agent Based Smulated Stock Market Through the Integraton of Indvdual Learnng and Socal Learnng Graham Kendall School of Computer Scence and IT ASAP Research
More informationAnts Can Schedule Software Projects
Ants Can Schedule Software Proects Broderck Crawford 1,2, Rcardo Soto 1,3, Frankln Johnson 4, and Erc Monfroy 5 1 Pontfca Unversdad Católca de Valparaíso, Chle FrstName.Name@ucv.cl 2 Unversdad Fns Terrae,
More informationA hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm
Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(7):1884-1889 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 A hybrd global optmzaton algorthm based on parallel
More informationHow Much to Bet on Video Poker
How Much to Bet on Vdeo Poker Trstan Barnett A queston that arses whenever a gae s favorable to the player s how uch to wager on each event? Whle conservatve play (or nu bet nzes large fluctuatons, t lacks
More informationA Secure Password-Authenticated Key Agreement Using Smart Cards
A Secure Password-Authentcated Key Agreement Usng Smart Cards Ka Chan 1, Wen-Chung Kuo 2 and Jn-Chou Cheng 3 1 Department of Computer and Informaton Scence, R.O.C. Mltary Academy, Kaohsung 83059, Tawan,
More informationVirtual machine resource allocation algorithm in cloud environment
COMPUTE MOELLIN & NEW TECHNOLOIES 2014 1(11) 279-24 Le Zheng Vrtual achne resource allocaton algorth n cloud envronent 1, 2 Le Zheng 1 School of Inforaton Engneerng, Shandong Youth Unversty of Poltcal
More informationA Fuzzy Optimization Framework for COTS Products Selection of Modular Software Systems
Internatonal Journal of Fuy Systes, Vol. 5, No., June 0 9 A Fuy Optaton Fraework for COTS Products Selecton of Modular Software Systes Pankaj Gupta, Hoang Pha, Mukesh Kuar Mehlawat, and Shlp Vera Abstract
More informationAn Analytical Model of Web Server Load Distribution by Applying a Minimum Entropy Strategy
Internatonal Journal of Coputer and Councaton Engneerng, Vol. 2, No. 4, July 203 An Analytcal odel of Web Server Load Dstrbuton by Applyng a nu Entropy Strategy Teeranan Nandhakwang, Settapong alsuwan,
More informationStatistical Approach for Offline Handwritten Signature Verification
Journal of Computer Scence 4 (3): 181-185, 2008 ISSN 1549-3636 2008 Scence Publcatons Statstcal Approach for Offlne Handwrtten Sgnature Verfcaton 2 Debnath Bhattacharyya, 1 Samr Kumar Bandyopadhyay, 2
More informationTourism Demand Forecasting by Improved SVR Model
Internatonal Journal of u- and e- Servce, Scence and Technology, pp403-4 http://dxdoorg/0457/junesst058538 Tours Deand Forecastng by Iproved SVR Model L Me Departent of Socal Servces,Zhengzhou Tours College,Henan,PRChna
More informationAn Alternative Way to Measure Private Equity Performance
An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate
More informationA Statistical Model for Detecting Abnormality in Static-Priority Scheduling Networks with Differentiated Services
A Statstcal odel for Detectng Abnoralty n Statc-Prorty Schedulng Networks wth Dfferentated Servces ng L 1 and We Zhao 1 School of Inforaton Scence & Technology, East Chna Noral Unversty, Shangha 0006,
More informationA GENETIC ALGORITHM-BASED METHOD FOR CREATING IMPARTIAL WORK SCHEDULES FOR NURSES
82 Internatonal Journal of Electronc Busness Management, Vol. 0, No. 3, pp. 82-93 (202) A GENETIC ALGORITHM-BASED METHOD FOR CREATING IMPARTIAL WORK SCHEDULES FOR NURSES Feng-Cheng Yang * and We-Tng Wu
More informationOptimal Choice of Random Variables in D-ITG Traffic Generating Tool using Evolutionary Algorithms
Optmal Choce of Random Varables n D-ITG Traffc Generatng Tool usng Evolutonary Algorthms M. R. Mosav* (C.A.), F. Farab* and S. Karam* Abstract: Impressve development of computer networks has been requred
More informationA Genetic Programming Based Stock Price Predictor together with Mean-Variance Based Sell/Buy Actions
Proceedngs of the World Congress on Engneerng 28 Vol II WCE 28, July 2-4, 28, London, U.K. A Genetc Programmng Based Stock Prce Predctor together wth Mean-Varance Based Sell/Buy Actons Ramn Rajaboun and
More informationMooring Pattern Optimization using Genetic Algorithms
6th World Congresses of Structural and Multdscplnary Optmzaton Ro de Janero, 30 May - 03 June 005, Brazl Moorng Pattern Optmzaton usng Genetc Algorthms Alonso J. Juvnao Carbono, Ivan F. M. Menezes Luz
More informationCalculation of Sampling Weights
Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a two-stage stratfed cluster desgn. 1 The frst stage conssted of a sample
More informationTraffic-light a stress test for life insurance provisions
MEMORANDUM Date 006-09-7 Authors Bengt von Bahr, Göran Ronge Traffc-lght a stress test for lfe nsurance provsons Fnansnspetonen P.O. Box 6750 SE-113 85 Stocholm [Sveavägen 167] Tel +46 8 787 80 00 Fax
More informationOptimal Bidding Strategies for Generation Companies in a Day-Ahead Electricity Market with Risk Management Taken into Account
Amercan J. of Engneerng and Appled Scences (): 8-6, 009 ISSN 94-700 009 Scence Publcatons Optmal Bddng Strateges for Generaton Companes n a Day-Ahead Electrcty Market wth Rsk Management Taken nto Account
More informationMATHEMATICAL ENGINEERING TECHNICAL REPORTS. Sequential Optimizing Investing Strategy with Neural Networks
MATHEMATICAL ENGINEERING TECHNICAL REPORTS Sequental Optmzng Investng Strategy wth Neural Networks Ryo ADACHI and Akmch TAKEMURA METR 2010 03 February 2010 DEPARTMENT OF MATHEMATICAL INFORMATICS GRADUATE
More informationTraffic Demand Forecasting for EGCS with Grey Theory Based Multi- Model Method
IJCSI Internatonal Journal of Coputer Scence Issues, Vol., Issue, No, January 3 ISSN (Prnt): 694-784 ISSN (Onlne): 694-84 www.ijcsi.org 6 Traffc Deand Forecastng for EGCS wth Grey Theory Based Mult- Model
More informationNumber of Levels Cumulative Annual operating Income per year construction costs costs ($) ($) ($) 1 600,000 35,000 100,000 2 2,200,000 60,000 350,000
Problem Set 5 Solutons 1 MIT s consderng buldng a new car park near Kendall Square. o unversty funds are avalable (overhead rates are under pressure and the new faclty would have to pay for tself from
More informationII. THE QUALITY AND REGULATION OF THE DISTRIBUTION COMPANIES I. INTRODUCTION
Fronter Methodology to fx Qualty goals n Electrcal Energy Dstrbuton Copanes R. Rarez 1, A. Sudrà 2, A. Super 3, J.Bergas 4, R.Vllafáfla 5 1-2 -3-4-5 - CITCEA - UPC UPC., Unversdad Poltécnca de Cataluña,
More informationbenefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).
REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or
More informationResearch Article Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading
Hndaw Publshng Corporaton e Scentfc World Journal, Artcle ID 914641, 12 pages http://dx.do.org/10.1155/2014/914641 Research Artcle Integrated Model of Multple Kernel Learnng and Dfferental Evoluton for
More informationStochastic Models of Load Balancing and Scheduling in Cloud Computing Clusters
Stochastc Models of Load Balancng and Schedulng n Cloud Coputng Clusters Sva Theja Magulur and R. Srkant Departent of ECE and CSL Unversty of Illnos at Urbana-Chapagn sva.theja@gal.co; rsrkant@llnos.edu
More informationInternational Journal of Industrial Engineering Computations
Internatonal Journal of Industral ngneerng Coputatons 3 (2012) 393 402 Contents lsts avalable at GrowngScence Internatonal Journal of Industral ngneerng Coputatons hoepage: www.growngscence.co/jec Suppler
More informationAnt Colony Optimization for Economic Generator Scheduling and Load Dispatch
Proceedngs of the th WSEAS Int. Conf. on EVOLUTIONARY COMPUTING, Lsbon, Portugal, June 1-18, 5 (pp17-175) Ant Colony Optmzaton for Economc Generator Schedulng and Load Dspatch K. S. Swarup Abstract Feasblty
More informationModule 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..
More informationInstitute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic
Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange
More informationSUPPLIER FINANCING AND STOCK MANAGEMENT. A JOINT VIEW.
SUPPLIER FINANCING AND STOCK MANAGEMENT. A JOINT VIEW. Lucía Isabel García Cebrán Departamento de Economía y Dreccón de Empresas Unversdad de Zaragoza Gran Vía, 2 50.005 Zaragoza (Span) Phone: 976-76-10-00
More informationAn Interest-Oriented Network Evolution Mechanism for Online Communities
An Interest-Orented Network Evoluton Mechansm for Onlne Communtes Cahong Sun and Xaopng Yang School of Informaton, Renmn Unversty of Chna, Bejng 100872, P.R. Chna {chsun,yang}@ruc.edu.cn Abstract. Onlne
More informationSCHEDULING OF CONSTRUCTION PROJECTS BY MEANS OF EVOLUTIONARY ALGORITHMS
SCHEDULING OF CONSTRUCTION PROJECTS BY MEANS OF EVOLUTIONARY ALGORITHMS Magdalena Rogalska 1, Wocech Bożeko 2,Zdzsław Heduck 3, 1 Lubln Unversty of Technology, 2- Lubln, Nadbystrzycka 4., Poland. E-mal:rogalska@akropols.pol.lubln.pl
More informationA Cryptographic Key Binding Method Based on Fingerprint Features and the Threshold Scheme
A Cryptographc Key ndng Method Based on Fngerprnt Features and the Threshold Schee 1 Ln You, 2 Guowe Zhang, 3 Fan Zhang 1,3 College of Councaton Engneerng, Hangzhou Danz Unv., Hangzhou 310018, Chna, ryouln@gal.co
More informationLecture 2: Single Layer Perceptrons Kevin Swingler
Lecture 2: Sngle Layer Perceptrons Kevn Sngler kms@cs.str.ac.uk Recap: McCulloch-Ptts Neuron Ths vastly smplfed model of real neurons s also knon as a Threshold Logc Unt: W 2 A Y 3 n W n. A set of synapses
More informationBasic Queueing Theory M/M/* Queues. Introduction
Basc Queueng Theory M/M/* Queues These sldes are created by Dr. Yh Huang of George Mason Unversty. Students regstered n Dr. Huang's courses at GMU can ake a sngle achne-readable copy and prnt a sngle copy
More informationData Broadcast on a Multi-System Heterogeneous Overlayed Wireless Network *
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 24, 819-840 (2008) Data Broadcast on a Mult-System Heterogeneous Overlayed Wreless Network * Department of Computer Scence Natonal Chao Tung Unversty Hsnchu,
More informationStochastic Models of Load Balancing and Scheduling in Cloud Computing Clusters
01 Proceedngs IEEE INFOCOM Stochastc Models of Load Balancng and Schedulng n Cloud Coputng Clusters Sva heja Magulur and R. Srkant Departent of ECE and CSL Unversty of Illnos at Urbana-Chapagn sva.theja@gal.co;
More information1. Measuring association using correlation and regression
How to measure assocaton I: Correlaton. 1. Measurng assocaton usng correlaton and regresson We often would lke to know how one varable, such as a mother's weght, s related to another varable, such as a
More informationResearch Article Load Balancing for Future Internet: An Approach Based on Game Theory
Appled Matheatcs, Artcle ID 959782, 11 pages http://dx.do.org/10.1155/2014/959782 Research Artcle Load Balancng for Future Internet: An Approach Based on Gae Theory Shaoy Song, Tngje Lv, and Xa Chen School
More informationCHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol
CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK Sample Stablty Protocol Background The Cholesterol Reference Method Laboratory Network (CRMLN) developed certfcaton protocols for total cholesterol, HDL
More informationFeature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College
Feature selecton for ntruson detecton Slobodan Petrovć NISlab, Gjøvk Unversty College Contents The feature selecton problem Intruson detecton Traffc features relevant for IDS The CFS measure The mrmr measure
More informationUsing Series to Analyze Financial Situations: Present Value
2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated
More informationThe Development of Web Log Mining Based on Improve-K-Means Clustering Analysis
The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.
More informationStochastic Models of Load Balancing and Scheduling in Cloud Computing Clusters
Stochastc Models of Load Balancng and Schedulng n Cloud Coputng Clusters Sva Theja Magulur and R. Srkant Departent of ECE and CSL Unversty of Illnos at Urbana-Chapagn sva.theja@gal.co; rsrkant@llnos.edu
More informationSPEE Recommended Evaluation Practice #6 Definition of Decline Curve Parameters Background:
SPEE Recommended Evaluaton Practce #6 efnton of eclne Curve Parameters Background: The producton hstores of ol and gas wells can be analyzed to estmate reserves and future ol and gas producton rates and
More informationSoftware project management with GAs
Informaton Scences 177 (27) 238 241 www.elsever.com/locate/ns Software project management wth GAs Enrque Alba *, J. Francsco Chcano Unversty of Málaga, Grupo GISUM, Departamento de Lenguajes y Cencas de
More informationStudy on Model of Risks Assessment of Standard Operation in Rural Power Network
Study on Model of Rsks Assessment of Standard Operaton n Rural Power Network Qngj L 1, Tao Yang 2 1 Qngj L, College of Informaton and Electrcal Engneerng, Shenyang Agrculture Unversty, Shenyang 110866,
More informationQuality of Service Analysis and Control for Wireless Sensor Networks
Qualty of ervce Analyss and Control for Wreless ensor Networs Jaes Kay and Jeff Frol Unversty of Veront ay@uv.edu, frol@eba.uv.edu Abstract hs paper nvestgates wreless sensor networ spatal resoluton as
More informationMultiple-Period Attribution: Residuals and Compounding
Multple-Perod Attrbuton: Resduals and Compoundng Our revewer gave these authors full marks for dealng wth an ssue that performance measurers and vendors often regard as propretary nformaton. In 1994, Dens
More informationNaglaa Raga Said Assistant Professor of Operations. Egypt.
Volue, Issue, Deceer ISSN: 77 8X Internatonal Journal of Adanced Research n Coputer Scence and Software Engneerng Research Paper Aalale onlne at: www.jarcsse.co Optal Control Theory Approach to Sole Constraned
More informationA Load-Balancing Algorithm for Cluster-based Multi-core Web Servers
Journal of Computatonal Informaton Systems 7: 13 (2011) 4740-4747 Avalable at http://www.jofcs.com A Load-Balancng Algorthm for Cluster-based Mult-core Web Servers Guohua YOU, Yng ZHAO College of Informaton
More informationthe Manual on the global data processing and forecasting system (GDPFS) (WMO-No.485; available at http://www.wmo.int/pages/prog/www/manuals.
Gudelne on the exchange and use of EPS verfcaton results Update date: 30 November 202. Introducton World Meteorologcal Organzaton (WMO) CBS-XIII (2005) recommended that the general responsbltes for a Lead
More informationBlending Roulette Wheel Selection & Rank Selection in Genetic Algorithms
Internatonal Journal of Machne Learnng and Computng, Vol. 2, o. 4, August 2012 Blendng Roulette Wheel Selecton & Rank Selecton n Genetc Algorthms Rakesh Kumar, Senor Member, IACSIT and Jyotshree, Member,
More informationMaintenance Scheduling by using the Bi-Criterion Algorithm of Preferential Anti-Pheromone
Leonardo ournal of Scences ISSN 583-0233 Issue 2, anuary-une 2008 p. 43-64 Mantenance Schedulng by usng the B-Crteron Algorthm of Preferental Ant-Pheromone Trantafyllos MYTAKIDIS and Arstds VLACHOS Department
More informationA Novel Dynamic Role-Based Access Control Scheme in User Hierarchy
Journal of Coputatonal Inforaton Systes 6:7(200) 2423-2430 Avalable at http://www.jofcs.co A Novel Dynac Role-Based Access Control Schee n User Herarchy Xuxa TIAN, Zhongqn BI, Janpng XU, Dang LIU School
More informationMining Feature Importance: Applying Evolutionary Algorithms within a Web-based Educational System
Mnng Feature Importance: Applyng Evolutonary Algorthms wthn a Web-based Educatonal System Behrouz MINAEI-BIDGOLI 1, and Gerd KORTEMEYER 2, and Wllam F. PUNCH 1 1 Genetc Algorthms Research and Applcatons
More informationTraffic-light extended with stress test for insurance and expense risks in life insurance
PROMEMORIA Datum 0 July 007 FI Dnr 07-1171-30 Fnansnspetonen Författare Bengt von Bahr, Göran Ronge Traffc-lght extended wth stress test for nsurance and expense rss n lfe nsurance Summary Ths memorandum
More information10.2 Future Value and Present Value of an Ordinary Simple Annuity
348 Chapter 10 Annutes 10.2 Future Value and Present Value of an Ordnary Smple Annuty In compound nterest, 'n' s the number of compoundng perods durng the term. In an ordnary smple annuty, payments are
More informationCan Auto Liability Insurance Purchases Signal Risk Attitude?
Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang
More informationMulti-objective Model of Power Terminate Plan in Multi-zone Peak Load Shifting Control
008 Aercan Control Conference Westn Seattle Hotel Seattle Washngton USA June -3 008 ThBI0.4 Mult-objectve Model of Power Ternate Plan n Mult-zone Peak Load Shftng Control Yang L Ja Lu Lqun Gao and Zh Kong
More informationScatter search approach for solving a home care nurses routing and scheduling problem
Scatter search approach for solvng a home care nurses routng and schedulng problem Bouazza Elbenan 1, Jacques A. Ferland 2 and Vvane Gascon 3* 1 Département de mathématque et nformatque, Faculté des scences,
More informationForecasting the Demand of Emergency Supplies: Based on the CBR Theory and BP Neural Network
700 Proceedngs of the 8th Internatonal Conference on Innovaton & Management Forecastng the Demand of Emergency Supples: Based on the CBR Theory and BP Neural Network Fu Deqang, Lu Yun, L Changbng School
More informationTHE APPLICATION OF DATA MINING TECHNIQUES AND MULTIPLE CLASSIFIERS TO MARKETING DECISION
Internatonal Journal of Electronc Busness Management, Vol. 3, No. 4, pp. 30-30 (2005) 30 THE APPLICATION OF DATA MINING TECHNIQUES AND MULTIPLE CLASSIFIERS TO MARKETING DECISION Yu-Mn Chang *, Yu-Cheh
More informationScan Detection in High-Speed Networks Based on Optimal Dynamic Bit Sharing
Scan Detecton n Hgh-Speed Networks Based on Optal Dynac Bt Sharng Tao L Shgang Chen Wen Luo Mng Zhang Departent of Coputer & Inforaton Scence & Engneerng, Unversty of Florda Abstract Scan detecton s one
More informationSciences Shenyang, Shenyang, China.
Advanced Materals Research Vols. 314-316 (2011) pp 1315-1320 (2011) Trans Tech Publcatons, Swtzerland do:10.4028/www.scentfc.net/amr.314-316.1315 Solvng the Two-Obectve Shop Schedulng Problem n MTO Manufacturng
More informationOn the Optimal Control of a Cascade of Hydro-Electric Power Stations
On the Optmal Control of a Cascade of Hydro-Electrc Power Statons M.C.M. Guedes a, A.F. Rbero a, G.V. Smrnov b and S. Vlela c a Department of Mathematcs, School of Scences, Unversty of Porto, Portugal;
More informationHow Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence
1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh
More informationNEURO-FUZZY INFERENCE SYSTEM FOR E-COMMERCE WEBSITE EVALUATION
NEURO-FUZZY INFERENE SYSTEM FOR E-OMMERE WEBSITE EVALUATION Huan Lu, School of Software, Harbn Unversty of Scence and Technology, Harbn, hna Faculty of Appled Mathematcs and omputer Scence, Belarusan State
More informationImplementation of Deutsch's Algorithm Using Mathcad
Implementaton of Deutsch's Algorthm Usng Mathcad Frank Roux The followng s a Mathcad mplementaton of Davd Deutsch's quantum computer prototype as presented on pages - n "Machnes, Logc and Quantum Physcs"
More informationWhat is Candidate Sampling
What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble
More informationCalculating the high frequency transmission line parameters of power cables
< ' Calculatng the hgh frequency transmsson lne parameters of power cables Authors: Dr. John Dcknson, Laboratory Servces Manager, N 0 RW E B Communcatons Mr. Peter J. Ncholson, Project Assgnment Manager,
More informationOptimized ready mixed concrete truck scheduling for uncertain factors using bee algorithm
Songklanakarn J. Sc. Technol. 37 (2), 221-230, Mar.-Apr. 2015 http://www.sst.psu.ac.th Orgnal Artcle Optmzed ready mxed concrete truck schedulng for uncertan factors usng bee algorthm Nuntana Mayteekreangkra
More information1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP)
6.3 / -- Communcaton Networks II (Görg) SS20 -- www.comnets.un-bremen.de Communcaton Networks II Contents. Fundamentals of probablty theory 2. Emergence of communcaton traffc 3. Stochastc & Markovan Processes
More information"Research Note" APPLICATION OF CHARGE SIMULATION METHOD TO ELECTRIC FIELD CALCULATION IN THE POWER CABLES *
Iranan Journal of Scence & Technology, Transacton B, Engneerng, ol. 30, No. B6, 789-794 rnted n The Islamc Republc of Iran, 006 Shraz Unversty "Research Note" ALICATION OF CHARGE SIMULATION METHOD TO ELECTRIC
More informationRELIABILITY, RISK AND AVAILABILITY ANLYSIS OF A CONTAINER GANTRY CRANE ABSTRACT
Kolowrock Krzysztof Joanna oszynska MODELLING ENVIRONMENT AND INFRATRUCTURE INFLUENCE ON RELIABILITY AND OPERATION RT&A # () (Vol.) March RELIABILITY RIK AND AVAILABILITY ANLYI OF A CONTAINER GANTRY CRANE
More informationImproved Mining of Software Complexity Data on Evolutionary Filtered Training Sets
Improved Mnng of Software Complexty Data on Evolutonary Fltered Tranng Sets VILI PODGORELEC Insttute of Informatcs, FERI Unversty of Marbor Smetanova ulca 17, SI-2000 Marbor SLOVENIA vl.podgorelec@un-mb.s
More informationPower-of-Two Policies for Single- Warehouse Multi-Retailer Inventory Systems with Order Frequency Discounts
Power-of-wo Polces for Sngle- Warehouse Mult-Retaler Inventory Systems wth Order Frequency Dscounts José A. Ventura Pennsylvana State Unversty (USA) Yale. Herer echnon Israel Insttute of echnology (Israel)
More informationA Hybrid Approach to Evaluate the Performance of Engineering Schools
A Hybrd Approach to Evaluate the Perforance of Engneerng Schools School of Engneerng Unversty of Brdgeport Brdgeport, CT 06604 ABSTRACT Scence and engneerng (S&E) are two dscplnes that are hghly receptve
More informationA DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION. Michael E. Kuhl Radhamés A. Tolentino-Peña
Proceedngs of the 2008 Wnter Smulaton Conference S. J. Mason, R. R. Hll, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION
More informationLearning with Imperfections A Multi-Agent Neural-Genetic Trading System. with Differing Levels of Social Learning
Proceedngs of the 4 IEEE Conference on Cybernetcs and Intellgent Systems Sngapore, 1-3 December, 4 Learnng wth Imperfectons A Mult-Agent Neural-Genetc Tradng System wth Dfferng Levels of Socal Learnng
More informationINTRODUCTION TO MERGERS AND ACQUISITIONS: FIRM DIVERSIFICATION
XV. INTODUCTION TO MEGES AND ACQUISITIONS: FIM DIVESIFICATION In the ntroducton to Secton VII, t was noted that frs can acqure assets by ether undertakng nternally-generated new projects or by acqurng
More informationPlanning for Marketing Campaigns
Plannng for Marketng Campagns Qang Yang and Hong Cheng Department of Computer Scence Hong Kong Unversty of Scence and Technology Clearwater Bay, Kowloon, Hong Kong, Chna (qyang, csch)@cs.ust.hk Abstract
More information1 Example 1: Axis-aligned rectangles
COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture # 6 Scrbe: Aaron Schld February 21, 2013 Last class, we dscussed an analogue for Occam s Razor for nfnte hypothess spaces that, n conjuncton
More informationDesign and Development of a Security Evaluation Platform Based on International Standards
Internatonal Journal of Informatcs Socety, VOL.5, NO.2 (203) 7-80 7 Desgn and Development of a Securty Evaluaton Platform Based on Internatonal Standards Yuj Takahash and Yoshm Teshgawara Graduate School
More informationGanesh Subramaniam. American Solutions Inc., 100 Commerce Dr Suite # 103, Newark, DE 19713, USA
238 Int. J. Sulaton and Process Modellng, Vol. 3, No. 4, 2007 Sulaton-based optsaton for ateral dspatchng n Vendor-Managed Inventory systes Ganesh Subraana Aercan Solutons Inc., 100 Coerce Dr Sute # 103,
More informationProject Networks With Mixed-Time Constraints
Project Networs Wth Mxed-Tme Constrants L Caccetta and B Wattananon Western Australan Centre of Excellence n Industral Optmsaton (WACEIO) Curtn Unversty of Technology GPO Box U1987 Perth Western Australa
More informationLaddered Multilevel DC/AC Inverters used in Solar Panel Energy Systems
Proceedngs of the nd Internatonal Conference on Computer Scence and Electroncs Engneerng (ICCSEE 03) Laddered Multlevel DC/AC Inverters used n Solar Panel Energy Systems Fang Ln Luo, Senor Member IEEE
More informationSPECIALIZED DAY TRADING - A NEW VIEW ON AN OLD GAME
August 7 - August 12, 2006 n Baden-Baden, Germany SPECIALIZED DAY TRADING - A NEW VIEW ON AN OLD GAME Vladmr Šmovć 1, and Vladmr Šmovć 2, PhD 1 Faculty of Electrcal Engneerng and Computng, Unska 3, 10000
More informationThe OC Curve of Attribute Acceptance Plans
The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4
More informationThe Greedy Method. Introduction. 0/1 Knapsack Problem
The Greedy Method Introducton We have completed data structures. We now are gong to look at algorthm desgn methods. Often we are lookng at optmzaton problems whose performance s exponental. For an optmzaton
More informationDynamic Pricing for Smart Grid with Reinforcement Learning
Dynamc Prcng for Smart Grd wth Renforcement Learnng Byung-Gook Km, Yu Zhang, Mhaela van der Schaar, and Jang-Won Lee Samsung Electroncs, Suwon, Korea Department of Electrcal Engneerng, UCLA, Los Angeles,
More information