Paralelní evoluční algoritmus EDA využívající teorii kopulí
In my thesis I~deal with the design, implementation and testing of the advanced parallel Estimation of Distribution Algorithm (EDA) utilizing copula theory to create a~probabilistic model. A~new population is created by the process of sampling the joint distribution function, which models the current distribution of the subpopulation of promising individuals. The usage of copulas increases the efficiency of the learning process and sampling the probabilistic model. It can be separated into mutually independent marginal distributions and the copula, which represents the correlations between the variables of the solved problem. This concept initiated the usage of the parallel island architecture, in which the migration of probabilistic models belonging to individual islands' subpopulations was used instead of the migration of individuals. The statistical tests used in the comparison of the proposed algorithm (mCEDA = migrating Copula-based Estimation of Distribution Algorithm) and the algorithms of other authors confirmed the effectiveness of the proposed concept.
EDA, Estimation of Distribution Algorithms, Optimization, Copula Theory, Multivariate Copula Sampling, Parallelisation, Parallel EDA, Island-based Model, Migration of Probabilistic models