Jirí Kubalík

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Genetic Algorithms (GAs) are adaptive search methods, which try to incorporate the principle of surviving known from nature. They proved to be an efficient instrument for solving many hard problems in different areas, in which the majority of other techniques failed as being weak or not applicable. On the other hand, GAs fight with a number of problems, as(More)
Large software companies have to plan their project portfolio to maximize potential portfolio return and strategic alignment, while balancing various preferences, and considering limited resources. Project portfolio managers need methods and tools to find a good solution for complex project portfolios and multiobjective target criteria efficiently. However,(More)
This paper presents an application of a prototype optimization with evolved improvement steps algorithm (POEMS) to the well-known problem of optimal sorting network design. The POEMS is an iterative algorithm that seeks the best variation of the current solution in each iteration. The variations, also called hypermutations, are evolved by means of an(More)
This paper presents an analysis of selected social networking portals for science, research and innovation as the first step in development of a portal for social network of IT professionals in the Czech Republic. First, actual portal implementations of four portals dedicated to scientific communities of researchers are analyzed, compared from the(More)
This paper proposes an evolutionary-based iterative local search hyper-heuristic approach called Iterated Search Driven by Evolutionary Algorithm Hyper-Heuristic (ISEA). Two versions of this algorithm, ISEAchesc and ISEA-adaptive, that differ in the re-initialization scheme are presented. The performance of the two algorithms was experimentally evaluated on(More)
Recently, a new iterative optimization framework utilizing an evolutionary algorithm called ”Prototype Optimization with Evolved iMprovement Steps” (POEMS) was introduced, which showed good performance on hard optimization problems large instances of TSP and real-valued optimization problems. Especially, on discrete optimization problems such as the TSP the(More)