105 citations · 399 across the 23 of their papers we have counts for
15 papers · 1 filter
Liquid State Genetic Programming
Mihai Oltean
A new Genetic Programming variant called Liquid State Genetic Programming (LSGP) is proposed in this paper. LSGP is a hybrid method combining a dynamic memory for storing the input…
Multi Expression Programming for solving classification problems
Mihai Oltean
Multi Expression Programming (MEP) is a Genetic Programming variant which encodes multiple solutions in a single chromosome. This paper introduces and deeply describes several stra…
Improving the Search by Encoding Multiple Solutions in a Chromosome
Mihai Oltean
We investigate the possibility of encoding multiple solutions of a problem in a single chromosome. The best solution encoded in an individual will represent (will provide the fitne…
Evolving Evolutionary Algorithms with Patterns
Mihai Oltean
A new model for evolving Evolutionary Algorithms (EAs) is proposed in this paper. The model is based on the Multi Expression Programming (MEP) technique. Each MEP chromosome encode…
Solving classification problems using Traceless Genetic Programming
Mihai Oltean
Traceless Genetic Programming (TGP) is a new Genetic Programming (GP) that may be used for solving difficult real-world problems. The main difference between TGP and other GP techn…
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…
Solving even-parity problems using traceless genetic programming
Mihai Oltean
A genetic programming (GP) variant called traceless genetic programming (TGP) is proposed in this paper. TGP is a hybrid method combining a technique for building individuals and a…
New Evolutionary Computation Models and their Applications to Machine Learning
Mihai Oltean
Automatic Programming is one of the most important areas of computer science research today. Hardware speed and capability have increased exponentially, but the software is years b…
Multi Expression Programming -- an in-depth description
Mihai Oltean
Multi Expression Programming (MEP) is a Genetic Programming variant that uses a linear representation of chromosomes. MEP individuals are strings of genes encoding complex computer…
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…
Searching for a practical evidence of the No Free Lunch theorems
Mihai Oltean
According to the No Free Lunch (NFL) theorems all black-box algorithms perform equally well when compared over the entire set of optimization problems. An important problem related…
Evolving reversible circuits for the even-parity problem
Mihai Oltean
Reversible computing basically means computation with less or not at all electrical power. Since the standard binary gates are not usually reversible we use the Fredkin gate in ord…
Evolving Evolutionary Algorithms using Linear Genetic Programming
Mihai Oltean
A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA…
Evolving winning strategies for Nim-like games
Mihai Oltean
An evolutionary approach for computing the winning strategy for Nim-like games is proposed in this paper. The winning strategy is computed by using the Multi Expression Programming…
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…