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Distributed Artificial Life Simulator
Weiss, Martin ; Kočí, Radek (referee) ; Martinek, David (advisor)
Along with the artificial intelligence modelling and related studies, distributed computing ranks amongs the most discussed scientific topics nowadays and one can be certain that it will not fade away anytime soon. This work is also concerned about the above mentioned topics, more precisely its its main goal is to design and implement a distributed artificial intelligence simulator. The mentioned application was realized during as an firm part of this work and some benchmarks were run to determine and observe some properties inherent to distributed systems. The actual realization takes into account different approaches to distributing simulation resources. The final product did not prove as that convenient for simulation of rather simple AI models, which it if fact, even slowed down at times. In the future the project should be aimed towards more computationally challenging problems, that can truly use the capacity and advantages of such a computational system.
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Learnable Evolution Model for Optimization (LEM)
Weiss, Martin ; Vašíček, Zdeněk (referee) ; Schwarz, Josef (advisor)
Numerical optimization of multimodal or otherwise nontrivial functions has stayed around the peak of the interest of many researchers for a long time. One of the promising methods that appeared is the hybrid approach of the Learnable Evolution Model that combines the well-established ways of artificial intelligence and machine learning with recently popular and efective methods of evolutionary programming. In this work, the method itself was reviewed with respect to what has been already implemented and tested and several possible new implementations of the method were proposed and some of them consequently implemented. The resulting program was then tested against a set of chosen nontrivial real-valued functions and its results were compared to those achieved with EDA algorithms.
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Distributed Artificial Life Simulator
Weiss, Martin ; Kočí, Radek (referee) ; Martinek, David (advisor)
Along with the artificial intelligence modelling and related studies, distributed computing ranks amongs the most discussed scientific topics nowadays and one can be certain that it will not fade away anytime soon. This work is also concerned about the above mentioned topics, more precisely its its main goal is to design and implement a distributed artificial intelligence simulator. The mentioned application was realized during as an firm part of this work and some benchmarks were run to determine and observe some properties inherent to distributed systems. The actual realization takes into account different approaches to distributing simulation resources. The final product did not prove as that convenient for simulation of rather simple AI models, which it if fact, even slowed down at times. In the future the project should be aimed towards more computationally challenging problems, that can truly use the capacity and advantages of such a computational system.
|
|
|
Learnable Evolution Model for Optimization (LEM)
Weiss, Martin ; Vašíček, Zdeněk (referee) ; Schwarz, Josef (advisor)
Numerical optimization of multimodal or otherwise nontrivial functions has stayed around the peak of the interest of many researchers for a long time. One of the promising methods that appeared is the hybrid approach of the Learnable Evolution Model that combines the well-established ways of artificial intelligence and machine learning with recently popular and efective methods of evolutionary programming. In this work, the method itself was reviewed with respect to what has been already implemented and tested and several possible new implementations of the method were proposed and some of them consequently implemented. The resulting program was then tested against a set of chosen nontrivial real-valued functions and its results were compared to those achieved with EDA algorithms.
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