Rebeka Čorić

 

PhD student
Department of Mathematics
Josip Juraj Strossmayer University of Osijek
Trg Ljudevita Gaja 6
Osijek, HR-31000, Croatia
phone: +385-31-224-805
fax: +385-31-224-801
email:  rcoric @ mathos.hr
office:  7 (ground floor)

 


Research Interests

Fitness landscape analysis
Genetic algorithms
Genetic programming

 

Degrees

MSc in Mathematics, Department of Mathematics, University of Osijek,Croatia, 2014.
BSc in Mathematics, Department of Mathematics, University of Osijek, Croatia, 2011.

 

Publications

 
Journal Publications

  1. M. Đumić, D. Šišejković, R. Čorić, D. Jakobović, Evolving priority rules for resource constrained project scheduling problem with genetic programming, Future Generation Computer Systems 86 (2018), 211-221
    The main task of scheduling is the allocation of limited resources to activities over time periods to optimize one or several criteria. The scheduling algorithms are devised mainly by the experts in the appropriate fields and evaluated over synthetic benchmarks or real-life problem instances. Since many variants of the same scheduling problem may appear in practice, and there are many scheduling algorithms to choose from, the task of designing or selecting an appropriate scheduling algorithm is far from trivial. Recently, hyper-heuristic approaches have been proven useful in many scheduling domains, where machine learning is applied to develop a customized scheduling method. This paper is concerned with the resource constrained project scheduling problem (RCPSP) and the development of scheduling heuristics based on Genetic programming (GP). The results show that this approach is a viable option when there is a need for a customized scheduling method in a dynamic environment, allowing the automated development of a suitable scheduling heuristic.
  2. N. Čerkez, R. Čorić, M. Đumić, D. Matijević, Finding an optimal seating arrangement for employees traveling to an event, Croatian Operational Research Review 6/2 (2015), 419-427
    The paper deals with modelling a specific problem called the Optimal Seating Arrangement (OSA) as an Integer Linear Program and demonstrated that the problem can be efficiently solved by combining branch-and-bound and cutting plane methods. OSA refers to a specific scenario that could possibly happen in a corporative environment, i.e. when a company endeavors to minimize travel costs when employees travel to an organized event. Each employee is free to choose the time to travel to and from an event and it depends on personal reasons. The paper differentiates between using different travel possibilities in the OSA problem, such as using company assigned or a company owned vehicles, private vehicles or using public transport, if needed. Also, a user-friendly web application was made and is available to the public for testing purposes.


Refereed Proceedings

  1. R. Čorić, M. Đumić, S. Jelić, A Genetic Algorithm for Group Steiner Tree Problem, 41st International Convention - MIPRO 2018, Opatija, Hrvatska, 2018, 1113-1118
    In Group Steiner Tree Problem (GST) we are given a weighted undirected graph and family of subsets of vertices which are called groups. Our objective is to find a minimum-weight subgraph which contains at least one vertex from each group (groups do not have to be disjoint). GST is NP-hard combinatorial optimization problem that arises from many complex real-life problems such as finding substrate-reaction pathways in protein networks, progressive keyword search in relational databases, team formation in social networks, etc. Heuristic methods are extremely important for finding the good-enough solutions in short time. In this paper we present genetic algorithm for solving GST. We also give results of computational experiments with comparisons to optimal solutions.
  2. R. Čorić, M. Đumić, D. Jakobović, Complexity Comparison of Integer Programming and Genetic Algorithms for Resource Constrained Scheduling Problems , 40th International ICT Convention - MIPRO 2017, Opatija, 2017, 1394-1400
    Resource constrained project scheduling problem (RCPSP) is one of the most intractable combinatorial optimization problems. RCPSP belongs to the class of NP hard problems. Integer Programming (IP) is one of the exact solving methods that can be used for solving RCPSP. IP formulation uses binary decision variables for generating a feasible solution and with different boundaries eliminates some of solutions to reduce the solution space size. All exact methods, including IP, search through entire solution space so they are impractical for very large problem instances. Due to the fact that exact methods are not applicable to all problem instances, many heuristic approaches are developed, such as genetic algorithms. In this paper we compare the time complexity of IP formulations and genetic algorithms when solving the RCPSP. In this paper we use two different solution representations for genetic algorithms, permutation vector and vector of floating point numbers. Two formulations of IP and and their time and convergence results are compared for the aforementioned approaches.
  3. R. Čorić, S. Picek, D. Jakobović, C.A. Coello Coello, On the mutual information as a fitness landscape measure, GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference Companion, Berlin, Germany, 2017, 165-166
    Fitness landscape analysis plays an important role in both theoretical and practical perspectives when using evolutionary algorithms. In this paper, we develop a new measure based on the mutual information paradigm and we show how it can help to deduce further information about the fitness landscape. In order to validate it as a valuable source of information when conducting fitness landscape analysis, we investigate its properties on a well-known benchmark suite. Moreover, we investigate the usefulness of the obtained information when choosing crossover operators. Finally, we show that when using our new measure, a number of classifiers can be constructed that offer an improved accuracy.



Projects


Professional Activities

Conferences
  • 15th International Conference on Operational Research KOI 2014, Osijek, Croatia, September 24-26, 2014.
  • 40th International ICT Convention - MIPRO 2017, Opatija, Croatia, May 22-26, 2017.
  • 41st International ICT Convention - MIPRO 2018, Opatija, Croatia, May 21-25, 2018.
Schools
  • 7th PhD Summer School in Discrete Mathematics, Rogla, Slovenia, July 23-29, 2017.
  • COST Training School: bridging the gap between theory and practice and making nature-inspired search and optimisation heuristics more applicable, Paris, France, October 18-24, 2017.
  • International Workshop on Optimal Control of Dynamical Systems and  Applications, Osijek, Croatia, June 20-22, 2018.
 
Service Activities
  • Festival znanosti:

2011. radionica - Primjena Sunčeve svjetlosti pri određenim izračunavanjima

2012. radionica - 10 u svijetu računala

2013. radionica - Zamisli jedan broj

 

  • Zimska škola matematike:

2011. predavanje - Bertrandov paradoks

 

  • Zimska škola informatike:

2017. radionica - Multi-threading i multi-processing u Pythonu

 

  • Geometrijska škola Stanka Bilinskog, Našice

2017. radionica za osnovnu i srednju školu - Geometrijska vjerojatnost

 


Teaching

Nastavne aktivnosti u zimskom semestru akademske 2018./2019.

Nastavne aktivnosti u ljetnom semestru akademske 2017./2018.

Prethodna nastava:

Konzultacije (Office Hours):   Ponedjeljkom od 10:30 do 11:00 i srijedom od 11:30 do 12:00 ili po dogovoru.

 


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