DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas.
Its design departs from most other existing frameworks in that it seeks to make algorithms …

This paper proposes a probabilistic sensor model for the optimization of sensor placement.
Traditional schemes rely on simple sensor behaviour and environmental factors. The …

We are proposing to use an ensemble of diverse specialists, where speciality is defined
according to the confusion matrix. Indeed, we observed that for adversarial instances originating …

In this paper, we bridge the gap between hyperparameter optimization and ensemble learning
by performing Bayesian optimization of an ensemble with regards to its hyperparameters. …

Traditional approaches for finding well-performing parameterizations of complex imaging
systems, such as super-resolution microscopes rely on an extensive exploration phase over …

In this paper, we propose a novel approach for the multi-objective optimization of classifier
ensembles in the ROC space. We first evolve a pool of simple classifiers with NSGA-II using …

We propose an automatic method to infer high dynamic range illumination from a single,
limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous …

Evolution provides a creative fount of complex and subtle adaptations that often surprise the
scientists who discover them. However, the creativity of evolution is not limited to the natural …

DEAP (Distributed Evolutionary Algorithms in Python) is a novel volutionary computation
framework for rapid prototyping and testing of ideas. Its design departs from most other existing …

The use of electric vehicles (EVs) load management is relevant to support electricity demand
softening, making the grid more economic, efficient, and reliable. However, the absence of …