Author:
Michal R. Przybylek
Affiliation:
University of Warsaw, Poland
Keyword(s):
Evolutionary algorithms, Process mining, Language recognition, Minimum description length.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolution Strategies
;
Evolutionary Computing
;
Evolutionary Multiobjective Optimization
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
This paper introduces a new kind of evolutionary method, called “skeletal algorithm”, and shows its sample
application to process mining. The basic idea behind the skeletal algorithm is to express a problem in
terms of congruences on a structure, build an initial set of congruences, and improve it by taking limited
unions/intersections, until a suitable condition is reached. Skeletal algorithms naturally arise in the context of
data/process minig, where the skeleton is the “free” structure on initial data and a congruence corresponds to
similarities in data. In such a context, skeletal algorithms come equipped with fitness functions measuring the
complexity of a model. We examine two fitness functions for our sample problem — one based on Minimum
Description Length Principle, and the other based on Bayesian Interpretation.