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K.T.C. Boudier
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Master thesis
(2025)
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K.T.C. Boudier, Peter A.N. Bosman, Thalea Schlender, Tanja Alderliesten, M.M. de Weerdt, D.M.J. Tax
Survival trees are a statistical modeling technique used to predict the time until an event occurs. They are widely valued for their interpretability, as they allow practitioners to understand how different variables influence outcomes. However, traditional survival trees struggle to capture nonlinear relationships and rely on greedy splitting strategies, which limit their performance in complex settings.
This thesis proposes a novel approach that addresses these limitations by generating globally optimized survival trees using Genetic Programming Gene-pool Optimal Mixing Evolutionary Algorithm (GP-GOMEA). By integrating a state-of-the-art evolutionary algorithm into the tree construction process, the resulting survival trees optimize both the structure and the decision nodes at a global level.
The method was evaluated on a synthetic dataset designed to require nonlinear decision boundaries—the XOR problem. Our approach consistently outperformed traditional survival trees, achieving optimal or near-optimal results in the noise-free setting. Moreover, the results show that GP-GOMEA survival trees can maintain a high performance even with a smaller population size and limited data, demonstrating the method’s suitability for problems involving nonlinear interactions.
These findings suggest that GP-GOMEA survival tree is a promising direction for advancing survival tree methodology. Future work should include evaluating the method on real-world survival datasets and further tuning key hyperparameters, such as the number of decision nodes. ...
This thesis proposes a novel approach that addresses these limitations by generating globally optimized survival trees using Genetic Programming Gene-pool Optimal Mixing Evolutionary Algorithm (GP-GOMEA). By integrating a state-of-the-art evolutionary algorithm into the tree construction process, the resulting survival trees optimize both the structure and the decision nodes at a global level.
The method was evaluated on a synthetic dataset designed to require nonlinear decision boundaries—the XOR problem. Our approach consistently outperformed traditional survival trees, achieving optimal or near-optimal results in the noise-free setting. Moreover, the results show that GP-GOMEA survival trees can maintain a high performance even with a smaller population size and limited data, demonstrating the method’s suitability for problems involving nonlinear interactions.
These findings suggest that GP-GOMEA survival tree is a promising direction for advancing survival tree methodology. Future work should include evaluating the method on real-world survival datasets and further tuning key hyperparameters, such as the number of decision nodes. ...
Survival trees are a statistical modeling technique used to predict the time until an event occurs. They are widely valued for their interpretability, as they allow practitioners to understand how different variables influence outcomes. However, traditional survival trees struggle to capture nonlinear relationships and rely on greedy splitting strategies, which limit their performance in complex settings.
This thesis proposes a novel approach that addresses these limitations by generating globally optimized survival trees using Genetic Programming Gene-pool Optimal Mixing Evolutionary Algorithm (GP-GOMEA). By integrating a state-of-the-art evolutionary algorithm into the tree construction process, the resulting survival trees optimize both the structure and the decision nodes at a global level.
The method was evaluated on a synthetic dataset designed to require nonlinear decision boundaries—the XOR problem. Our approach consistently outperformed traditional survival trees, achieving optimal or near-optimal results in the noise-free setting. Moreover, the results show that GP-GOMEA survival trees can maintain a high performance even with a smaller population size and limited data, demonstrating the method’s suitability for problems involving nonlinear interactions.
These findings suggest that GP-GOMEA survival tree is a promising direction for advancing survival tree methodology. Future work should include evaluating the method on real-world survival datasets and further tuning key hyperparameters, such as the number of decision nodes.
This thesis proposes a novel approach that addresses these limitations by generating globally optimized survival trees using Genetic Programming Gene-pool Optimal Mixing Evolutionary Algorithm (GP-GOMEA). By integrating a state-of-the-art evolutionary algorithm into the tree construction process, the resulting survival trees optimize both the structure and the decision nodes at a global level.
The method was evaluated on a synthetic dataset designed to require nonlinear decision boundaries—the XOR problem. Our approach consistently outperformed traditional survival trees, achieving optimal or near-optimal results in the noise-free setting. Moreover, the results show that GP-GOMEA survival trees can maintain a high performance even with a smaller population size and limited data, demonstrating the method’s suitability for problems involving nonlinear interactions.
These findings suggest that GP-GOMEA survival tree is a promising direction for advancing survival tree methodology. Future work should include evaluating the method on real-world survival datasets and further tuning key hyperparameters, such as the number of decision nodes.
Natural Language Processing and Reinforcement Learning to Generate Morally
What is the optimal weight w to win the games while playing morally?
In our everyday life, people interact more and more with agents. However these agents often lack a moral sense and prioritize the accomplishment of the given task. In consequence, agents may unknowingly act immorally. Little research or progress has been done to endow agents with human morality and an internal sense of right and wrong. As of today, agents have a primitive representation of morality often represented as 1 value. In contrast, humans have multiple reasons to judge an action as moral. In hope of creating agents that are imbued with a more complex and human moral, we build upon the Jiminy Cricket environment. This preexisting environment has multiple games with diverse scenarios and the objective is to do the most moral action to maximize the reward
...
In our everyday life, people interact more and more with agents. However these agents often lack a moral sense and prioritize the accomplishment of the given task. In consequence, agents may unknowingly act immorally. Little research or progress has been done to endow agents with human morality and an internal sense of right and wrong. As of today, agents have a primitive representation of morality often represented as 1 value. In contrast, humans have multiple reasons to judge an action as moral. In hope of creating agents that are imbued with a more complex and human moral, we build upon the Jiminy Cricket environment. This preexisting environment has multiple games with diverse scenarios and the objective is to do the most moral action to maximize the reward