- author = "Domagoj Jakobovic",
- title = "Scheduling based on adaptive rules",
- title_cr = "Rasporedivanje zasnovano na prilagodljivim pravilima",
- school = "Department of Electronics, Microelectronics, Computer and Intelligent Systems, University of Zagreb",
- year = "2005",
- address = "Croatia",
- month = "7 " # dec,
- keywords = "genetic algorithms, genetic programming, Computing, Data processing, scheduling heuristics, priority scheduling, adaptive genetic operators",
-
URL = "
https://dr.nsk.hr/en/islandora/object/fer%3A5430",
-
URL = "https://urn.nsk.hr/urn:nbn:hr:168:435819",
-
URL = "
https://www.zemris.fer.hr/~yeti/doktorat/doktorat.pdf",
- size = "143 pages",
- abstract = "In this work the problem of devising an appropriate scheduling policy for different environments is addressed. The methodology which uses genetic programming to evolve scheduling heuristics is described. The scheduling heuristics are developed in the form of scheduling rules which define dynamic priorities for the elements in the system. Scheduling rules for different environments are devised using genetic programming: one machine, parallel proportional machines, unrelated machines and job shop environment. Scheduling algorithms are defined with two components: one component represents meta-algorithm which operates in scheduling environment, and the other represents an appropriate scheduling policy which derives job or machine priorities. The scheduling policy is evolved with genetic programming. For each scheduling environment a set of learning and a set of evaluation scheduling instances is defined. Devised algorithms are compared with existing algorithms in each environment. The evolved algorithms exhibit similar or better efficiency in all cases, and a significant improvement is achieved in scheduling environments where there are no fitting algorithms. Additionally, a method for evaluation of terminals in genetic programming solution and adaptive probabilities for genetic operators crossover and mutation are devised. The adaptive methods increase the probability of finding a good solution and may speed up the evolution process.",
- abstract = "U radu se promatra problem definiranja prikladnih postupaka rasporedivanja za razlicita okruzenja s obzirom na uvjete rasporedivanja i zadane kriterije. Predlaze se metodologija izvodenja algoritama rasporedivanja uz pomoc genetskog programiranja. Algoritmi rasporedivanja poprimaju oblik pravila u kojima se elementima u sustavu dodjeljuje prioritet na temelju kojega se aktivnosti pridruzuju sredstvima. Koristeci genetsko programiranje, izvode se pravila za razlicita okruzenja rasporedivanja: rasporedivanje na jednom stroju, na paralelnim jednolikim strojevima, nesrodnim strojevima te u okruzenju proizvoljne obrade. Za pojedino okruzenje postupak rasporedivanja definiran je u dva dijela: jedan dio predstavlja meta-algoritam koji koristi prioritete elemenata u sustavu kako bi pridruzivao aktivnosti sredstvima, a drugi dio predstavlja funkciju koja odreduje prioritete elemenata. Prioritetna funkcija dobiva se primjenom genetskog programiranja. Za svako okruzenje definirani su skupovi ispitnih primjera za ucenje i ocjenu, a predlozeni algoritmi usporedeni su sa postojecim algoritmima rasporedivanja. Algoritmi rasporedivanja izvedeni uz pomoc genetskog programiranja pokazuju slicnu ili bolju ucinkovitost u usporedbi s postojecim algoritmima, a znacajnu prednost ostvaruju u okolinama rasporedivanja za koje ne postoje prikladni postupci rasporedivanja. U radu je takoder opisan postupak vrednovanja podatkovnih elemenata rješenja genetskog programiranja te postupak prilagodbe primjene genetskih operatora krizanja i mutacije. Predlozeni postupci prilagodbe olakšavaju pronalazenje kvalitetnog rješenja i povecavaju uspješnost evolucijskog procesa.",
-
notes = "Language croatian
Universal decimal classification (UDC) 004 URN:NBN urn:nbn:hr:168:435819
Mentor Leo Budin",
