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— In this paper, the performance assessment of the hybrid Archive-based Micro Genetic Algorithm (AMGA) on a set of bound-constrained synthetic test problems is reported. The hybrid AMGA proposed in this paper is a combination of a classical gradient based single-objective optimization algorithm and an evolutionary multi-objective optimization algorithm. The(More)
Slow Feature Analysis (SFA) has been established as a robust and versatile technique from the neurosciences to learn slowly varying functions from quickly changing signals. Recently, the method has been also applied to classification tasks. Here we apply SFA for the first time to a time series classification problem originating from gesture recognition. The(More)
Kernel-based methods like Support Vector Machines (SVM) have been established as powerful techniques in machine learning. The idea of SVM is to perform a mapping φ from the input space to a higher-dimensional feature space using a kernel function k, so that a linear learning algorithm can be employed. However, the burden of choosing the appropriate kernel(More)
In this paper, we propose a new evolutionary algorithm for multi-objective optimization. The proposed algorithm benefits from the existing literature and borrows several concepts from existing multi-objective optimization algorithms. The proposed algorithm employs a new kind of selection procedure which benefits from the search history of the algorithm and(More)
In the recent past, hybrid metaheuristics became famous as successful optimization methods. The motivation for the hybridization is a notion of combining the best of two worlds: evolutionary black box optimization and local search. Successful hybridizations in large combinatorial solution spaces motivate to transfer the idea of combining the two worlds to(More)
Nowadays, constraints play an important role in industry, because most industrial optimization tasks underly several restrictions. Finding good solutions for a particular problem with respect to all constraint functions can be expensive, especially when the dimensionality of the search space is large and many constraint functions are involved. Unfortunately(More)
Learning complex game functions is still a difficult task. We apply temporal difference learning (TDL), a well-known variant of the reinforcement learning approach, in combination with n-tuple networks to the game Connect-4. Our agent is trained just by self-play. It is able, for the first time, to consistently beat the optimal-playing Minimax agent (in(More)
Self-adaptive crossover is a step towards exploiting the structure of problems automatically by evolution. We present a self-adaptive extension of the partially mapped crossover (PMX) operator that controls the crossover points. Because the link between strategy parameters and fitness is weak for self-adaptive crossover, superior results were hard to gather(More)
Real-world data mining applications often confront us with complex and noisy data, which makes it necessary to optimize the data mining models thoroughly to achieve high-quality results. We describe in this contribution an approach to tune the parameters of the model and the feature selection conjointly. The aim is to use one framework to solve a variety of(More)
The complex, often redundant and noisy data in real-world data mining (DM) applications frequently lead to inferior results when out-of-the-box DM models are applied. A tuning of parameters is essential to achieve high-quality results. In this work we aim at tuning parameters of the preprocessing and the modeling phase conjointly. The framework TDM (Tuned(More)