@phdthesis{Ghasemzadeh2005,
  author    = {Ghasemzadeh, Mohammad},
  title     = {A new algorithm for the quantified satisfiability problem, based on zero-suppressed binary decision diagrams and memoization},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-6378},
  school      = {Universit{\"a}t Potsdam},
  year      = {2005},
  abstract  = {Quantified Boolean formulas (QBFs) play an important role in theoretical computer science. QBF extends propositional logic in such a way that many advanced forms of reasoning can be easily formulated and evaluated. In this dissertation we present our ZQSAT, which is an algorithm for evaluating quantified Boolean formulas. ZQSAT is based on ZBDD: Zero-Suppressed Binary Decision Diagram , which is a variant of BDD, and an adopted version of the DPLL algorithm. It has been implemented in C using the CUDD: Colorado University Decision Diagram package. The capability of ZBDDs in storing sets of subsets efficiently enabled us to store the clauses of a QBF very compactly and let us to embed the notion of memoization to the DPLL algorithm. These points led us to implement the search algorithm in such a way that we could store and reuse the results of all previously solved subformulas with a little overheads. ZQSAT can solve some sets of standard QBF benchmark problems (known to be hard for DPLL based algorithms) faster than the best existing solvers. In addition to prenex-CNF, ZQSAT accepts prenex-NNF formulas. We show and prove how this capability can be exponentially beneficial.},
  subject      = {Bin{\"a}res Entscheidungsdiagramm},
  language  = {en}
}
@misc{AlibabaieGhasemzadehMeinel2017,
  author    = {Alibabaie, Najmeh and Ghasemzadeh, Mohammad and Meinel, Christoph},
  title     = {A variant of genetic algorithm for non-homogeneous population},
  series = {International Conference Applied Mathematics, Computational Science and Systems Engineering 2016},
  volume    = {9},
  journal   = {International Conference Applied Mathematics, Computational Science and Systems Engineering 2016},
  publisher = {EDP Sciences},
  address   = {Les Ulis},
  issn      = {2271-2097},
  doi       = {10.1051/itmconf/20170902001},
  pages     = {8},
  year      = {2017},
  abstract  = {Selection of initial points, the number of clusters and finding proper clusters centers are still the main challenge in clustering processes. In this paper, we suggest genetic algorithm based method which searches several solution spaces simultaneously. The solution spaces are population groups consisting of elements with similar structure. Elements in a group have the same size, while elements in different groups are of different sizes. The proposed algorithm processes the population in groups of chromosomes with one gene, two genes to k genes. These genes hold corresponding information about the cluster centers. In the proposed method, the crossover and mutation operators can accept parents with different sizes; this can lead to versatility in population and information transfer among sub-populations. We implemented the proposed method and evaluated its performance against some random datasets and the Ruspini dataset as well. The experimental results show that the proposed method could effectively determine the appropriate number of clusters and recognize their centers. Overall this research implies that using heterogeneous population in the genetic algorithm can lead to better results.},
  language  = {en}
}