A5090445830- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
- Chaos control and synchronization
- Neural Networks and Applications
- Complex Systems and Time Series Analysis
- Complex Network Analysis Techniques
- Chaos-based Image/Signal Encryption
- Advanced Control Systems Optimization
- Advanced Multi-Objective Optimization Algorithms
- Cellular Automata and Applications
- Opinion Dynamics and Social Influence
- Modeling, Simulation, and Optimization
- Nonlinear Dynamics and Pattern Formation
- Scheduling and Optimization Algorithms
- Evolutionary Game Theory and Cooperation
- Advanced Malware Detection Techniques
- Advanced Algorithms and Applications
- Artificial Immune Systems Applications
- Fuzzy Logic and Control Systems
- Network Security and Intrusion Detection
- Advanced Computational Techniques and Applications
- Mathematical Dynamics and Fractals
- Gene Regulatory Network Analysis
- Fault Detection and Control Systems
- Advanced Data Processing Techniques
VSB - Technical University of Ostrava
2016-2025
Physical Technical Testing Institute
2019-2024
Ton Duc Thang University
2014-2022
University of Ostrava
2012-2022
Laboratoire d'Informatique de Paris-Nord
2018
Tomas Bata University in Zlín
2007-2016
Informa (Italy)
2008
Brno University of Technology
2004
Artificial intelligence techniques have grown rapidly in recent years, and their applications practice can be seen many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based provide better cyber defense tools help adversaries improve methods of attack. However, malicious actors are aware new prospects too will probably attempt use them for nefarious purposes. This survey paper aims at providing an overview how artificial used context both offense defense.
The problem of class imbalance has always been considered as a significant challenge to traditional machine learning and the emerging deep research communities. A classification can be imbalanced if training set does not contain an equal number labeled examples from all classes. classifier trained on such is likely favor those classes containing larger than others. Unfortunately, that small labelled instances usually correspond rare events. Thus, poor accuracy these may lead severe...
Determination of the global optimum complex non-convex optimization problems real-world applications has remained a challenging task. Many researchers have been developing various types effective direct search-based methods to tackle these problems. In this paper, we introduce new variant recently developed Spherical Search (SS) algorithm, which contains powerful and self-adaptation structure enhance performance. To analyze performance, proposed algorithm is tested on 57 test collected from...
This paper introduces the notion of chaos synthesis by means evolutionary algorithms and develops a new method for chaotic systems synthesis. is similar to genetic programming grammatical evolution being applied along with three algorithms: differential evolution, self-organizing migration algorithm. The aim this investigation synthesize "simple" based on some elements contained in prechosen existing system properly defined cost function. consists eleven case studies: aforementioned...
Most of the real-world black-box optimization problems are associated with multiple non-linear as well non-convex constraints, making them difficult to solve. In this work, we introduce a variant Covariance Matrix Adaptation Evolution Strategy (CMA-ES) linear timing complexity adopt constraints Constrained Optimization Problems (COPs). CMA-ES is already well-known powerful algorithm for solving continuous, non-convex, and by fitting second-order model underlying objective function (similar...
This paper presents a new algorithm for optimizing parameters in swarm using reinforcement learning. The algorithm, called iSOMA-RL, is based on the iSOMA population-based optimization that mimics competition-cooperation behavior of creatures to find optimal solution. By learning, iSOMA-RL can dynamically and continuously optimize parameters, which play crucial role determining performance but are often difficult determine. learning technique used state-of-the-art Proximal Policy...