Using Genetic Algorithm to Improve Information Retrieval Systems

Transcription

1 Worl Acaemy of Scence, Engneerng an Technology Inernaonal Journal of Compuer an Informaon Engneerng Usng Genec Algorhm o Improve Informaon Rereval Sysems Ahme A. A. Rawan, Bahga A. Abel Laef, Abel Mge A. Al, an Osman A. Saek Inernaonal Scence Inex, Compuer an Informaon Engneerng wase.org/publcaon/881 Absrac Ths suy nvesgaes he use of genec algorhms n nformaon rereval. The meho s shown o be applcable o hree well-known ocumens collecons, where more relevan ocumens are presene o users n he genec mofcaon. In hs paper we presen a new fness funcon for approxmae nformaon rereval whch s very fas an very flexble, han cosne smlary fness funcon. Keywors Cosne smlary, Fness funcon, Genec Algorhm, Informaon Rereval, Query learnng. I. INTRODUCTION ENETIC Algorhm ( GA ) s a probablsc algorhm Gsmulang he mechansm of naural selecon of lvng organsms an s ofen use o solve problems havng expensve soluons. In GA, he search space s compose of canae soluons o he problem, each represene by a srng s erme as a chromosome. Each chromosome has an objecve funcon value, calle fness. A se of chromosomes ogeher wh her assocae fness s calle he populaon. Ths populaon, a a gven eraon of he genec algorhm, s calle a generaon. Hollan, De Jong an Golberg were poneere of GA n he conex of connuous non-lnear opmzaon [1], [] an [3]. Genec algorhms (GAs) are no new o nformaon rereval [4], [5]. Goron suggese represenng a posng as a chromosome an usng genec algorhms o selec goo nexes [6]. Yang e al. suggese usng GAs wh user feeback o choose weghs for search erms n a query [7]. Morgan an Klgour suggese an nermeary beween he user an IR sysem employng GAs o choose search erms from a hesaurus an conary [8]. Boughanem e al. [9], Horng an Yeh [10], an Vrajoru [11], examne GAs for nformaon rereval an hey suggese new crossover an muaon operaors. Vrajoru examne he effec of populaon sze on learnng ably, conclung ha a large populaon sze s mporan [1]. A. A. A. Rawan s Prof. an he hea of Compuer Scence Deparmen, Mna Unversy (Corresponng auhor o prove moble: , e- mal: aaarawaneg@yahoo.co.uk, IEEE member for 5 years). B. A. Abel Laef s asssan prof., Deparmen of Compuer Scence, Mna Unversy (e-mal: r_bahga005@yahoo.com). A. A. Al s asssan prof., Deparmen of Compuer Scence, Mna Unversy (e-mal: abelmge@yahoo.com). O. A. Saek s a Demonsraor, Deparmen of Compuer Scence, Mna Unversy (e-mal: oas_as@yahoo.com). Despe he successes, lle use has been mae of genec algorhms for A-Hoc queres. Harman observe fferen IR sysems reurnng subsanally fferen resuls, ye mananng approxmaely equal performance [13]. Bulng on he, Barell e al. suggeson, n whch we combne he oupu of fferen rankng funcons o mprove performance [14]. Pahak e al. use a genec algorhm o choose weghs for such a combnaon [15]. In hs paper we nrouce a new fness funcon an compare s resuls wh GA base on Cosne fness funcon an Classcal IR n query learnng problems. Our fness funcon has been apple on hree well-known es collecons (CISI, CACM an NPL), o gan an exhausve vew of mprovemen nformaon rereval sysems usng genec echnques. II. ANTECEDENTS A. Informaon Rereval Informaon Rereval Sysem (IRS), ha s, a sysem use o sore ems of nformaon ha nee o be processe, searche an rereve corresponng o a user s query. Mos IRSs use keywors o rereve ocumens. The sysems frs exrac keywors from ocumens an hen assgn weghs o he keywors by usng fferen approaches. Such a sysem has wo major problems. One s how o exrac keywors precsely an he oher s how o ece he wegh of each keywor. Ths research presens an applcaon of GA as relevan feeback meho amng o aap keywors weghs. An IRS s bascally consue by hree man componens, whose composon s nrouce as follows [16], [17]. - The ocumenary aabase. Ths componen sores he ocumens an he represenaons of her nformaon conens. I s assocae wh he nexer moule, whch auomacally generaes a represenaon for each ocumen by exracng he ocumen conens. Texual ocumen represenaon s ypcally base on nex erms (ha can be eher sngle erms or sequences), whch are he conen enfers of he ocumens. - The query subsysem. I allows he users o formulae her nformaon nees an presens he relevan ocumens rereve by he sysem o hem. To o ha, nclues a query language ha collecs he rules o generae legmae queres an proceures o selec he relevan ocumens. Inernaonal Scholarly an Scenfc Research & Innovaon (5)

2 Worl Acaemy of Scence, Engneerng an Technology Inernaonal Journal of Compuer an Informaon Engneerng Inernaonal Scence Inex, Compuer an Informaon Engneerng wase.org/publcaon/881 - The machng mechansm. I evaluaes he egree o he ocumen, whch represenaons sasfy he requremens expresse n he query, he Rereval Saus Value (RSV) an rereves hose ocumens ha are juge o be relevan o. B. Informaon Rereval Moels Several rereval moels have been sue an evelope n he IR area; we analyze some of hese moels, whch are: Boolean moel. In he Boolean rereval moel, he nexer moule performs a bnary nexng n he sense ha a erm n a ocumen represenaon s eher sgnfcan (appears a leas once n ) or no. User queres n hs moel are expresse usng a query language ha s base on hese erms an allows combnaons of smple user requremens wh he logcal operaors AND, OR an NOT. The resul obane from he processng of a query s a se of ocumens ha oally mach wh,.e., only wo possbles are consere for each ocumen: o be or no o be relevan for he user s nees, represene by he user query [17], [18]. Vecor space moel. In hs moel, a ocumen s vewe as a vecor n n-mensonal ocumen space (where n s he number of sngushng erms use o escrbe conens of he ocumens n a collecon) an each erm represens one menson n he ocumen space. A query s also reae n he same way an consruce from he erms an weghs prove n he user reques. Documen rereval s base on he measuremen of he smlary beween he query an he ocumens. Ths means ha ocumens wh a hgher smlary o he query are juge o be more relevan o an shoul be rereve by he IRS n a hgher poson n he ls of rereve ocumens. In Ths meho, he rereve ocumens can be orerly presene o he user wh respec o her relevance o he query [17]. Probablsc moel. Ths moel res o use he probably heory o bul he search funcon an s operaon moe. The nformaon use o compose he search funcon s obane from he srbuon of he nex erms hroughou he collecon of ocumens or a subse of. Ths nformaon s use o se he values of some parameers of he search funcon, whch s compose of a se of weghs assocae o he nex erms [19], [0]. C. Evaluaon of Informaon Rereval Sysems There are several ways o measure he qualy of an IRS, such as he sysem effcency an effecveness, an several subjecve aspecs relae o he user sasfacon. Traonally, he rereval effecveness (usually base on he ocumen relevance wh respec o he user s nees) s he mos consere. There are fferen crera o measure hs aspec, wh he precson an he recall beng he mos use. Precson ( P ) s he rae beween he relevan ocumens rereve by he IRS n response o a query an he oal number of ocumens rereve, whls Recall ( R ) s he rae beween he number of relevan ocumens rereve an he oal number of relevan ocumens o he query exsng n he aabase [18]. The mahemacal expresson of each of hem s showe as follows: wh { 0, 1} user an { 0, 1} r beng he relevance of ocumen for he f beng he rereval of ocumen n he r f r f P =, R = (1) f r processng of he curren query. Noce ha boh measures are efne n [0,1], wh beng he opmal value. The evaluaon funcon heren s he non-nerpolae average precson [1], []. Whch s smlar o average precson bu wh he cuoff pons equvalen o he ranng ocumens. In hs measure funcon, he ocumens are smply ranke. Le 1,,..., D enoe he sore ocumens by ecreasng orer of he values of he smlary measure funcon, where D represens he number of ranng ocumens. The funcon r ( ) gves he relevance of a ocumen. I reurns 1 f s relevan, an 0 oherwse. The non-nerpolae average precson s efne as follows: AvgP = 1 D D = 1 r ( ) j = 1 when r( ) reurns 1, f s relevan an 0 oherwse where D represen he number of ocumens [1]. III. SOME APPLICATIONS OF GAS IN INFORMATION RETRIEVAL There has been an ncreasng neres n he applcaon of GA ools o IR n he las few years. Concreely, he machne learnng paragm [3], whose am s he esgn of sysem able o auomacally acqure knowlege by hemselves, seems o be neresng n hs opc [4]. GAs are no specfcally learnng algorhms, bu also offerng a powerful an oman nepenen search ably ha can be use n many learnng asks, snce learnng an self-organzaon can be consere as opmzaon problems n many cases. Due o hs reason, he applcaons of GAs o IR have ncrease n he las ecae. Among ohers, nex subsecons show some of fferen proposals mae n hese areas n he las few years. A. Auomac Documen Inexng The applcaons n hs area o aap he escrpons of he ocumens n he ocumenary base wh he am of faclang ocumen rereval n he face of relevan queres. Goron proposes a GA o erve he ocumen escrpons. He chooses a bnary cong scheme where each escrpon s D 1 j () Inernaonal Scholarly an Scenfc Research & Innovaon (5)

3 Worl Acaemy of Scence, Engneerng an Technology Inernaonal Journal of Compuer an Informaon Engneerng Inernaonal Scence Inex, Compuer an Informaon Engneerng wase.org/publcaon/881 a fxe lengh an a bnary vecor [8]. The genec populaon s compose of fferen escrpons for he same ocumen. The fness funcon s base on calculang he smlary beween he curren ocumen escrpon an each of he queres (for whch he ocumen s relevan or non-relevan) by means of he Jaccar s nex an hen compung he average aapaon values of he escrpon for he se of relevan an non-relevan queres. In Goron work, GA consere s que unusual as here s no muaon operaor an he crossover probably s equal o 1. Wh regar o he selecon scheme, he number of copes of each chromosome n he new populaon s calculae an vng s aapaon value by he populaon average. Fan e al. propose an algorhm for nexng funcon learnng base on GA, whose am o oban an nexng funcon for he key erm weghng of a ocumenary collecon o mprove he IR process [5]. B. Cluserng of Documens an Terms In hs area, wo approaches have been apple for obanng user-orene ocumen clusers. Roberson an Wlle look for groups of erms appearng wh smlar frequences n he ocumens of a collecon [6]. The auhors conser a GA groupng he erms whou mananng her nal orer. The man feaures of he GA are: - Represenaon scheme. Two fferen cong schemes are consere: separaor meho an vson-assgnmen meho. - Inal populaon. The frs generaon of he chromosomes epens on he chosen cong an he res of nvuals are ranomly generae. - Operaors. Each operaor has an applcaon probably assocae an s selece spnnng he roulee. Dfferen crossover an muaon operaors are use. - Fness funcon. There are wo proposals: o o A measure of he relave enropy an Pra s measure. C. Machng Funcon Learnng The am of machng funcon learnng s o use a GA o generae a smlary measure for a vecor space IRS o mprove s rereval effcency for a specfc user. Ths consues a new relevance feeback phlosophy snce machng funcons are aape nsea of queres. Two fferen varans have been propose n he specalze leraure: - Lnear combnaon of exsng smlary funcons. In Pahak e al. propose a new weghe machng funcon, whch s he lnear combnaon of fferen exsng smlary funcons [15]. The weghng parameers are esmae by a GA base on relevance feeback from users. They use real cong, a classcal generaonal scheme, wo-pon crossover an Gaussan nose muaon. The algorhm s ese on he Cranfel collecon. - Auomac smlary measure learnng. A GA o auomacally learn a machng funcon wh relevance feeback s nrouce n [7], [8]. The smlary funcons are represene as rees an a classcal generaonal scheme, he usual GA crossover are consere. D. Query Learnng Ths s he mos exene group of applcaons of GAs n IR. Every proposal n hs group use GAs eher lke a relevance feeback echnque or lke an Inucve Query By Example (IQBE) algorhm. The bass of relevance feeback les n he fac ha eher users normally formulae queres compose of erms, whch o no mach he erms (whch use o nex he relevan ocumens o her nees) or hey o no prove he approprae weghs for he query erms. The operaon moe s nvolvng an mofyng he prevous query (ang an removng erms or changng he weghs of he exsng query erms) wh akng no accoun he relevance jugmens of he ocumens rereve by, consues a goo way o solve he laer wo problems an o mprove he precson, an especally he recall, of he prevous query [18]. IQBE was propose n as a process n whch searchers prove sample ocumens (examples) an he algorhms nuce (or learn) he key conceps n orer o fn oher relevan ocumens [4]. Ths meho s a process for asssng he users n he query formulaon process performe by machne learnng mehos. I works by akng a se of relevan (an oponally, non-relevan ocumens) prove by a user an applyng an off-lne learnng process o auomacally generae a query escrbng he user s nees. Smh an Smh propose a GA for learnng queres for Boolean IRSs [9]. Alhough hey nrouce as a relevance feeback algorhm, he expermenaon s acually closer o he IQBE framework. The algorhm componens are escrbe as follows: - The Boolean queres are encoe n expresson rees, whose ermnal noes are query erms an whose nner noes are he Boolean operaors AND, OR an NOT. - Each generaon s base on selecng wo parens, wh he bes fe havng a larger chance o be chosen, an generang wo offsprng from hem. Boh offsprng are ae o he curren populaon whch ncremens s sze n hs way. - The usual GA crossover s consere [30]. No muaon operaor s apple. - The nal populaon s generae by ranomly selecng he erms nclue n he se of relevan ocumens prove by he user, havng hose presen Inernaonal Scholarly an Scenfc Research & Innovaon (5)

4 Worl Acaemy of Scence, Engneerng an Technology Inernaonal Journal of Compuer an Informaon Engneerng Inernaonal Scence Inex, Compuer an Informaon Engneerng wase.org/publcaon/881 n more ocumens a hgher probably of beng selece. - The fness funcon gves a compose rereval evaluaon encompassng he wo man rereval parameers (precson an recall). Yang an Korfaghe [31] propose a smlar GA o ha of Roberson an Wlle s [6]. They use a real cong wh he wo-pon crossover an ranom muaon operaors (beses, crossover an muaon probables are change hroughou he GA run). The selecon s base on a classc generaonal scheme where he chromosomes wh a fness value below he average of he populaon are elmnae, an he reproucon s performe by Baker s mechansm. IV. SYSTEM FRAMEWORK A. Bulng IR Sysem The propose sysem s base on Vecor Space Moel (VSM) n whch boh ocumens an queres are represene as vecors. Frsly, o eermne ocumens erms, we use he followng proceure: - Exracon of all he wors from each ocumen. - Elmnaon of he sop-wors from a sop-wor ls generae wh he frequency conary of Kucera an Francs [3]. - Semmng he remanng wors usng he porer semmer ha s he mos commonly use semmer n Englsh [16], [33]. Afer usng hs proceure, he fnal number of erms was 6385 for he CISI collecon, 716 for CACM an 777 for NPL. Afer eermnng he erms ha escrbe all ocumens of he collecon, we assgne he weghs by usng he followng formula whch propose by Salon an Buckley [34]: a j = where D, fj log max f fj log max f aj s he wegh assgne o he erm f j s he number of j n ocumen mes ha erm j appears n ocumen D, n j s he number of ocumens nexe by he erm j an fnally, N s he oal number of ocumens n he aabase. Fnally, we normalze he vecors, vng hem by her Euclean norm. Ths s accorng o he suy of Noreaul e al., of he bes smlary measures whch makes angle comparsons beween vecors [35]. N n N n (3) We carry ou a smlar proceure wh he collecon of queres, hereby obanng he normalze query vecors. Then, we apply he followng seps: - For each collecon, each query s compare wh all he ocumens, usng he cosne smlary measure. Ths yels a ls gvng he smlares of each query wh all ocumens of he collecon. - Ths ls s ranke n ecreasng orer of smlary egree. - Make a ranng aa consss of he op 15 ocumen of he ls wh a corresponng query. - Auomacally, he keywors ( erms ) are rereve from he ranng aa an he erms whch are use o form a bnary query vecor. - Aap he query vecor usng he genec approach. B. The Genec Approach Once sgnfcan keywors are exrace from ranng aa (relevan an rrelevan ocumens) nclung weghs are assgne o he keywors. The bnary weghs of he keywors are forme as a query vecor. We have apple GA for wo fness funcon o ge an opmal or near opmal query vecor, also we have compare he resul of he wo GA approach wh he classcal IR Sysems whou usng GA. Ths wll be explane n he followng subsecons. 1) Represenaon of he chromosomes These chromosomes use a bnary represenaon, an are convere o a real represenaon by usng a ranom funcon. We wll have he same number of genes (componens) as he query an he feeback ocumens have erms wh non-zero weghs. The se of erms conane n hese ocumens an he query s calculae. The sze of he chromosomes wll be equal o he number of erms of ha se, we ge he query vecor as a bnary represenaon an applyng he ranom funcon o mofy he erms weghs o real represenaon. Our GA approach receves an nal populaon chromosomes corresponng o he op 15 ocumens rereve from classcal IR wh respec o ha query. ) Fness funcon Fness funcon s a performance measure or rewar funcon, whch evaluaes how each soluon, s goo. In our work, we use wo GAs wh wo fferen fness funcons: (a) he frs GA sysem (GA1) uses a measure of cosne smlary beween he query vecor an he chromosomes of he populaon as a fness funcon, wh he equaon: = 1 x x = 1 = 1 y y (4) Inernaonal Scholarly an Scenfc Research & Innovaon (5)

5 Worl Acaemy of Scence, Engneerng an Technology Inernaonal Journal of Compuer an Informaon Engneerng Inernaonal Scence Inex, Compuer an Informaon Engneerng wase.org/publcaon/881 where X s he real represenaon wegh of erm n he chromosome, Y s he real represenaon wegh of ha erm n he query vecor an s he oal number of erms n he query vecor as n a gven chromosome. The value of he cosne smlary les on he nerval [0,1] accorng o he smlary beween a chromosome an he query. (b) The secon GA uses a new fness funcon represens by: x y (5) = 1 whch s he fference beween erms weghs of a gven chromosome an he query vecor. 3) Selecon As he selecon mechansm, he GA uses smple ranom samplng [1], [3]. Ths consss of consrucng a roulee wh he same number of slos as here are nvuals n he populaon, an n whch he sze of each slo s recly relae o he nvual s fness value. Hence, he bes chromosomes wll on average acheve more copes, an he wors fewer copes. Also, we have use he elsm sraegy, as a complemen o he selecon mechansm []. Afer generang he new populaon, f he bes chromosome of he preceng generaon s by chance absen, he wors nvual of he new populaon s whrawn an replace by ha chromosome. 4) Operaors In our GA approaches, we use wo GA operaors o prouce offsprng chromosomes, whch are: Crossover s he genec operaor ha mxes wo chromosomes ogeher o form new offsprng. Crossover occurs only wh crossover probably Pc. Chromosomes are no subjece o crossover reman unmofe. The nuon behn crossover s exploraon of a new soluons an exploaon of ol soluons. GAs consruc a beer soluon by mxure goo characersc of chromosome ogeher. Hgher fness chromosome has an opporuny o be selece more han lower ones, so goo soluon always alve o he nex generaon. We use a sngle pon crossover, exchanges he weghs of sub-vecor beween wo chromosomes, whch are canae for hs process. Muaon s he secon operaor uses n our GA sysems. Muaon nvolves he mofcaon of he gene values of a soluon wh some probably Pm. In accorance wh changng some b values of chromosomes gve he fferen brees. Chromosome may be beer or poorer han ol chromosome. If hey are poorer han ol chromosome hey are elmnae n selecon sep. The objecve of muaon s resorng los an explorng varey of aa. V. EXPERIMENTAL RESULTS The es aabases for our GA approaches are hree wellknown es collecons, whch are: he CISI collecon (1460 ocumens on nformaon scence), he CACM collecon (304 ocumens on Communcaons), an fnally he NPL collecon (11,49 ocumens on elecronc engneerng). One of he prncpal reasons for choosng more han one es collecon s o emphasze an generalze our resuls n all alernave es ocumens collecons. We apply he Expermens on 100 queres an we choose hese queres accorng o each query o no rereve 15 relevan ocumens for our IR sysem. From our expermenal observaon, he bes values for hs es ocumens collecons a crossover probably Pc = 0.8 an muaon rae s Pm= 0.7 for he wo GAs (GA1 an GA). In he followng subsecons, he resuls for applyng GAs for 100 generaon for each GA are explane. A. The CISI Documens Collecon The resuls for he wo GAs (see Table I), by usng nonnerpolae average Recall Precson relaonshp. From hs able we noce ha GA gves a hgh mprovemen han GA1 wh % an boh hgher han classc IR sysem wh 13.6% an 11.9%, respecvely as average values. Also, he average number of erms of query vecor before applyng GAs s erms, hese erms are reuce afer applyng GA1 o erms, an reuce afer usng GA o 83.7 erms. TABLE I THE EXPERIMENTAL RESULTS OF CISI COLLECTION Average Recall Precson for 100 query n CISI Collecon Precson GA1 GA Recall Improvemen Improvemen Classc IR GA1 GA % % Average B. The NPL Documens Collecon The resuls for hs expermen (see Table II), by usng nonnerpolae average Recall Precson relaonshp. From hs able we fn ha he GA gves a hgh mprovemen han GA1 wh 7.5% an boh hgher han classc IR sysem wh 19.06% an 11.5%, respecvely as average values. Also, he average number of erms of query vecor before applyng GAs s erms; hese erms are reuce afer applyng GA1 o 16.8 erms, an reuce afer usng GA o 1.6 erms. Inernaonal Scholarly an Scenfc Research & Innovaon (5)

6 Worl Acaemy of Scence, Engneerng an Technology Inernaonal Journal of Compuer an Informaon Engneerng Inernaonal Scence Inex, Compuer an Informaon Engneerng wase.org/publcaon/881 TABLE II THE EXPERIEMENTAL RESULTS OF NPL COLLECTION Average Recall Precson for 100 query n NPL Collecon Precson GA1 Recall Improvemen Classc IR GA1 GA % C. The CACM Documens Collecon GA Improvemen % Average The resuls for hs expermen (see Table III), by usng non-nerpolae average Recall Precson relaonshp. TABLE III THE EXPERIEMENTAL RESULTS OF CACM COLLECTION Average Recall Precson for 100 query n CACM Collecon Precson GA1 Recall Improvemen Classc IR GA1 GA % GA Improvemen % Average From hs able we noce ha GA gves a hgh mprovemen han we ge GA1 wh 1.7% an boh hgher han classc IR sysem wh 6.8% an 5.13%, respecvely as average values. Also, he average number of erms of query vecor before applyng GAs s erms; hese erms are reuce afer applyng GA1 o erms, an reuce afer applyng GA o 31.5 erms. VI. CONCLUSION From pervous resuls, we noe ha our new fness funcon whch s represene by equaon (5) gves more sophscae resuls han a cosne fness funcon n our es collecons. Also, s easy o prove ha he new fness funcon has a complexy of orer (n ) whle he complexy of he secon fness funcon (cosne smlary) of orer (n 5 ) for each chromosome, where n s he number of erms n he search space for ha query. Also, from he prevous ables, we noe ha: our new fness funcon has a precson value beer han n cosne smlary fness funcon. The expermens evelope use hree of he relave ocumen collecons (CACM, CISI an NPL), an compare he resuls of wo varan algorhms (Classcal IR an GA1) wh our fness funcon (GA). The laer algorhm acheves he bes performance an obans beer precson han he oher wo approach. REFERENCES [1] J. H. Hollan, Aapaon n Naural an Arfcal Sysems, Unversy of Mchgan Press, Ann Arbor, [] K. A. DeJong, An Analyss of he Behavor of a Class of Genec Aapve Sysems, Ph.D. Thess, Unversy of Mchgan, [3] D. E. Golberg, Genec Algorhms n Search, Opmzaon, an Machne Learnng, Ason-Wesley, Reang, MA., [4] H. Chen, Machne learnng for nformaon rereval: neural neworks, symbolc learnng, an genec algorhms. Journal of he Amercan Socey for Informaon Scence, 46(3), 1995, pp [5] J. Savoy an D. Vrajoru, Evaluaon of learnng schemes use n nformaon rereval (CR-I-95-0). Unverse e Neuchael, Facule e ro e es Scences Economques, [6] M. Goron, Probablsc an genec algorhms n ocumen rereval. Communcaons of he ACM, 31(10), 1988, pp [7] J. Yang, R. Korfhage an E. Rasmussen. Query mprovemen n nformaon rereval usng genec algorhms a repor on he expermens of he TREC projec. In Proceengs of he 1s ex rereval conference (TREC-1), 199, pp [8] J. Morgan an A. Klgour. Personalsng on-lne nformaon rereval suppor wh a genec algorhm. In A. Moscarn, & P. Smh (Es.), PolyMoel 16: Applcaons of arfcal nellgence, 1996, pp [9] M. Boughanem, C. Chrsmen, an L. Tamne. On usng genec algorhms for mulmoal relevance opmzaon n nformaon rereval. Journal of he Amercan Socey for Informaon Scence an Technology, 53(11), 00, pp [10] J. T. Horng an C. C. Yeh. Applyng genec algorhms o query opmzaon n ocumen rereval. Informaon Processng & Managemen, 36(5), 000, pp [11] D. Vrajoru. Crossover mprovemen for he genec algorhm n nformaon rereval. Informaon Processng& Managemen, 34(4), 1998, pp [1] D. Vrajoru. Large populaon or many generaons for genec algorhms? Implcaons n nformaon rereval. In F. Cresan an G. Pas (Es.), Sof compung n nformaon rereval. Technques an applcaons, Physca-Verlag, 000, pp [13] D. Harman. Overvew of he frs TREC conference. In Proceengs of he 16h ACM SIGIR conference on nformaon rereval, 1993, pp [14] B. T. Barell, G. W. Corell an R. K. Belew. Auomac combnaon of mulple ranke rereval sysems. In Proceengs of he 17h ACM SIGIR conference on nformaon rereval, 1994, pp [15] P. Pahak, M. Goron an W. Fan. Effecve nformaon rereval usng genec algorhms base machng funcons aapon, n: Proc. 33r Hawa Inernaonal Conference on Scence (HICS), Hawa, USA, 000. [16] R. Baeza-Yaes an B. Rbero-Neo. Moern Informaon Rereval, Asson, [17] G. Salon an M.H. McGll. Inroucon o Moern Informaon Rereval, McGraw-Hll, [18] C.J. Van Rjsbergen. Informaon Rereval, secon e., Buerworh, [19] A. Booksen. Oulne of a general probablsc rereval moel, Journal of Documenaon 39 (), 1983, pp Inernaonal Scholarly an Scenfc Research & Innovaon (5)

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