Description

Leader of the group Evolutionary Intelligence: Peter Bosman.

Evolutionary Intelligence can be described as biologically inspired, self-learning systems that can be used to solve complex real-world problems, i.e., problems that are hard or impossible to solve optimally with exact algorithms. The algorithms that we study are inspired by natural and biological systems which we know are capable of doing extraordinary things. Key examples are natural evolution and biological brains, the artificial analogy of which are Evolutionary Algorithms (EAs) and Neural Networks (NNs). With EI we particularly focus on novel synergies between these two types of algorithms.

An example of an EI-application in medicine is medical image analysis. We are working on EI that can automatically recognize the prostate on an MRI scan. The system learns this by analyzing multiple scans on which doctors have outlined this organ. This can already be done using existing Neural Networks. But by combining Evolutionary Algorithms and Neural Networks we can explicitly learn and reproduce a discrete number of variations – not all physicians outline the prostate in the exact same way.

Besides this application, there are many other examples of applications, such as using EI to automatically design explainable predictive systems that can link certain characteristics of a patient to the outcome of a treatment. In addition, our group continues previously award-winning work in collaboration with Amsterdam UMC and Leiden UMC on applying EI technology to brachytherapy treatment planning. Brachytherapy is an important type of radiotherapy for the treatment of cancer whereby radioactive sources are placed in or near the tumor.
However, EI has a much broader application. This has led to interest in our research from a variety of sectors, including healthcare, logistics, agriculture and industrial design.

Watch our group video to get a glimpse of our activities.

 

 

 

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