EVOLUTIONARY ALGORITHMS TO SIMULATE REAL CONDITIONS IN ARTIFICIAL INTELLIGENCE AS BASIS FOR MATHEMATICAL FUZZY CLUSTERING
Ness, S. C. C.1
Evocell Institute, Austria
In present-day physics we may assumes space as a perfect continuum describable by
discrete mathematics or a set of discrete elements described by a programmed probabilistic process
or find alternative models that grasp real conditions better as they more closely simulate real
behaviour. Clustering logic based on evolutionary algorithms is able to give meaning to the
unlimited amounts of data that enterprises generate and that contain valuable hidden knowledge.
Evolutionary algorithms are useful to make sense of this hidden knowledge, as they are very close
to nature and the mind. However, most known applications of evolutionary algorithms cluster data
points to to one group, thereby leaving key aspects to understand the data out and thus hardening
simulations of biological processes. Fuzzy clustering methods divide data points into groups based
on item similarity and detects patterns between items in a set, whereby data points can belong to
more than one group. Evolutionary algorithm fuzzy clustering inspired multivariate mechanism
allows for changes at each iteration of the algorithm and improves performance from one feature
to another and from one cluster to another. It is applicable to real life objects that are neither
circular nor elliptical and thereby allows for clusters of any predefined shape.
In this paper we explain the philosophical concept of evolutionary algorithms for production of
fuzzy clustering methods that produce good quality of clustering in the fields of virtual reality,
augmented reality and gaming applications and in industrial manufacturing, robotic assistants,
product development, law and forensics as well as parameterless body model extraction from
CCTV camera images.
Artficial Evolution, Artificial Intelligence, Biology, Big Data, Cellular Automata,
Data Interpretation and Analytics, Deep Learning, Features Selection, Genetic Algorithms,
Generative Models, Machine Learning, Pattern Recognition, Robotic Process Automation,
Simulation, Smart Systems, Virtual Machines, Visualization.