Anezka Kazikova

Anezka Kazikova
Tomas Bata University in Zlín · Department of Informatics and Artificial Intelligence

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About

14
Publications
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Citations
Introduction
Anezka Kazikova currently works at the Department of Informatics and Artificial Intelligence, Tomas Bata University in Zlín. Anezka does research in Theory of Computation, Artificial Intelligence and Algorithms. Their most recent publication is 'Performance of the Bison Algorithm on Benchmark IEEE CEC 2017'.

Publications

Publications (14)
Article
Measuring the population diversity in metaheuristics has become a common practice for adaptive approaches, aiming mainly to address the issue of premature convergence. Understanding the processes leading to a diversity loss in a metaheuristic algorithm is crucial for designing successful adaptive approaches. In this study, we focus on the relation...
Article
Full-text available
Comparing various metaheuristics based on an equal number of objective function evaluations has become standard practice. Many contemporary publications use a specific number of objective function evaluations by the benchmarking sets definitions. Furthermore, many publications deal with the recurrent theme of late stagnation, which may lead to the...
Article
Although metaheuristic optimization has become a common practice, new bio-inspired algorithms often suffer from a priori ill reputation. One of the reasons is a common bad practice in metaheuristic proposals. It is essential to pay attention to the quality of conducted experiments, especially when comparing several algorithms among themselves. The...
Chapter
Meta-heuristic algorithms are reliable tools for modern optimization. Yet their amount is so immense that it is hard to pick just one to solve a specific problem. Therefore many researchers hold on known, approved algorithms. But is it always beneficial? In this paper, we use the meta-heuristics for the design of cascade PID controllers and compare...
Chapter
Many state-of-the-art optimization algorithms stand against the threat of premature convergence. While some metaheuristics try to avoid it by increasing the diversity in various ways, the Bison Algorithm faces this problem by guaranteeing stable exploitation – exploration ratio throughout the whole optimization process. Still, it is important to en...
Chapter
This research represents a detailed insight into the modern and popular hybridization of unconventional quasiperiodic/chaotic sequences and evolutionary computation. It is aimed at the influence of different randomization schemes on the population diversity, thus on the performance, of two selected adaptive Differential Evolution (DE) variants. Exp...
Chapter
This paper studies the performance of a newly developed optimization algorithm inspired by the behavior of bison herds: the Bison Algorithm. The algorithm divides its population into two groups. The exploiting group simulates the swarming behavior of bison herds endangered by predators. The exploring group systematically runs through the search spa...
Chapter
This paper proposes a new swarm optimization algorithm inspired by bison behavior. The algorithm mimics two survival mechanisms of the bison herds: swarming into the circle of the strongest individuals and exploring the search space via organized run throughout the optimization process. The proposed algorithm is compared to the Particle Swarm Optim...
Article
This paper proposes a modification of the Bison Algorithm’s running technique, which allows the running group to exploit the areas of discovered promising solutions. It also provides a closer examination of the successful running behavior and its impact on the overall optimization process. The new algorithm is then compared to other optimization al...