no code implementations • 5 Dec 2023 • Mihai Oltean
A new Genetic Programming variant called Liquid State Genetic Programming (LSGP) is proposed in this paper.
1 code implementation • 9 Nov 2022 • Sanda Maria Avram, Mihai Oltean
Determining the author of a text is a difficult task.
no code implementations • 16 Mar 2022 • Mihai Oltean
Multi Expression Programming (MEP) is a Genetic Programming variant which encodes multiple solutions in a single chromosome.
1 code implementation • 13 Oct 2021 • Mihai Oltean
We investigate the possibility of encoding multiple solutions of a problem in a single chromosome.
1 code implementation • 10 Oct 2021 • Mihai Oltean
A new model for evolving Evolutionary Algorithms (EAs) is proposed in this paper.
1 code implementation • 7 Oct 2021 • Mihai Oltean
Traceless Genetic Programming (TGP) is a new Genetic Programming (GP) that may be used for solving difficult real-world problems.
1 code implementation • 7 Oct 2021 • 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.
1 code implementation • 4 Oct 2021 • Mihai Oltean
A genetic programming (GP) variant called traceless genetic programming (TGP) is proposed in this paper.
3 code implementations • 1 Oct 2021 • Mihai Oltean
It is costly to pay humans, it is hard to keep them satisfied for a long time, it takes a lot of time to teach and train them and the quality of their output is in most cases low (in software, mostly due to bugs).
1 code implementation • 29 Sep 2021 • Mihai Oltean
Multi Expression Programming (MEP) is a Genetic Programming variant that uses a linear representation of chromosomes.
1 code implementation • 22 Aug 2021 • 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 a difficult task.
no code implementations • 21 Aug 2021 • Mihai Oltean, Crina Groşan, Mihaela Oltean
Multi Expression Programming (MEP) is a Genetic Programming variant that uses linear chromosomes for solution encoding.
no code implementations • 21 Aug 2021 • Mihai Oltean
An evolutionary approach for computing the winning strategy for Nim-like games is proposed in this paper.
no code implementations • 21 Aug 2021 • Mihai Oltean
A new model for evolving Evolutionary Algorithms is proposed in this paper.
no code implementations • 21 Aug 2021 • Mihai Oltean
Of high interest is finding a function for which Random Search is better than another standard evolutionary algorithm.
no code implementations • 21 Aug 2021 • Mihai Oltean
Reversible computing basically means computation with less or not at all electrical power.
1 code implementation • 2 Dec 2017 • Horea Mureşan, Mihai Oltean
In this paper we introduce a new, high-quality, dataset of images containing fruits.
1 code implementation • 8 Sep 2015 • Mihai Oltean, D. Dumitrescu
The results emphasizes that evolved MEP heuristic is a powerful tool for solving difficult TSP instances.