65 citations · 87 across the 5 of their papers we have counts for
5 papers
Using Traceless Genetic Programming for Solving Multiobjective Optimization Problems
Mihai Oltean, Crina Grosan
Traceless Genetic Programming (TGP) is a Genetic Programming (GP) variant that is used in cases where the focus is rather the output of the program than the program itself. The mai…
Evolving Evolutionary Algorithms using Multi Expression Programming
Mihai Oltean, Crina Groşan
Finding the optimal parameter setting (i.e. the optimal population size, the optimal mutation probability, the optimal evolutionary model etc) for an Evolutionary Algorithm (EA) is…
Evolving Digital Circuits for the Knapsack Problem
Mihai Oltean, Crina Groşan, Mihaela Oltean
Multi Expression Programming (MEP) is a Genetic Programming variant that uses linear chromosomes for solution encoding. A unique feature of MEP is its ability of encoding multiple…
Classifying action correctness in physical rehabilitation exercises
Alina Miron, Crina Grosan
The work in this paper focuses on the role of machine learning in assessing the correctness of a human motion or action. This task proves to be more challenging than the gesture an…
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery
Ivan Olier, Noureddin Sadawi, G. Richard Bickerton +4
We investigate the learning of quantitative structure activity relationships (QSARs) as a case-study of meta-learning. This application area is of the highest societal importance,…