The evolution of the movement of virtual creatures is an interesting application of evolutionary algorithms in the field of Artificial Life. The goal of this thesis was to explore the possibilities of its use in computer games. As part of this thesis I proposed a game with the theme of evolution of the movement of virtual creatures, I created a system for their evolution based on information from existing works and expanded it with the features needed for the proposed game. In the course of the work, it turned out that the evolution is too computationally demanding to use in the game. Therefore, I created a simplified game prototype and in my work I focused more on experimenting with various settings of the algorithms for the evolution of virtual creatures.

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This thesis examines adversarial examples in machine learning, specifically in the im- age classification domain. State-of-the-art deep learning models are able to recognize patterns better than humans. However, we can significantly reduce the model's accu- racy by adding imperceptible, yet intentionally harmful noise. This work investigates various methods of creating adversarial images as well as techniques that aim to defend deep learning models against these malicious inputs. We choose one of the contemporary defenses and design an attack that utilizes evolutionary algorithms to deceive it. Our experiments show an interesting difference between adversarial images created by evolu- tion and images created with the knowledge of gradients. Last but not least, we test the transferability of our created samples between various deep learning models. 1

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This research study focuses on solving the deployment planning problem for UAV-BSs using Multi-Objective Evolutionary Algorithms (MOEAs). The main research objectives encompass gridbased modelling of the target area, investigating evolution parameters, and evaluating algorithm performance in diverse deployment scenarios. Cost, coverage, and interference are considered as objectives along with specific constraints to generate optimal deployment plans. The solution incorporates objective decision support for selecting the best solution among the Pareto front. The research also accounts for parameter initialization and UAV network heterogeneity. Through comprehensive evaluations, the proposed solution demonstrates computational efficiency and the ability to generate satisfactory deployment plans. The study recommends using NonDominated Sorting Genetic Algorithm-II (NSGA-II) for optimal performance. The research also incorporates a fitness approximation technique to reduce computational time while maintaining solution quality. The findings provide valuable insights and recommendations for efficient and balanced deployment planning. However, the research acknowledges limitations and suggests future enhancements. Overall, this research contributes to the field by establishing a foundation for robust and practical deployment plans, guiding future advancements. Future research should focus on addressing identified limitations to enhance applicability and effectiveness in real-world deployment scenarios.

Daily fantasy basketball presents interesting problems to researchers due to the extensive amounts of data that needs to be explored when trying to predict player performance. A large amount of this data can be noisy due to the variance within the sport of basketball. Because of this, a high degree of skill is required to consistently win in daily fantasy basketball contests. On any given day, users are challenged to predict how players will perform and create a lineup of the eight best players under fixed salary and positional requirements. In this thesis, we present a tool to assist daily fantasy basketball players with these tasks. We explore the use of several machine learning techniques to predict player performance and develop multiple approaches to approximate optimal lineups. We then compare each different heuristic and lineup creation combination, and show that our best combinations perform much better than random lineups. Although creating provably optimal lineups is computationally infeasible, by focusing on players in the Pareto front between performance and cost we can reduce the search space and compute near optimal lineups. Additionally, our greedy and evolutionary lineup search methods offer similar performance at a much smaller computational cost. Our analysis indicates that due to how player salaries are structured, it is generally preferred to construct a lineup consisting of a few stars and filling out the rest of the roster with average to mediocre players than to construct a lineup where all players are expected to perform about the same. Through these findings we hope that our research can serve as a future baseline towards developing an automated or semi-automated tool to optimize daily fantasy basketball.

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