Difference between revisions of "Evoptool: Evolutionary Optimization Tool"
(→People involved) |
(→Project head(s)) |
||
| Line 18: | Line 18: | ||
===== Project head(s) ===== | ===== Project head(s) ===== | ||
M. Matteucci - [[User:MatteoMatteucci]] | M. Matteucci - [[User:MatteoMatteucci]] | ||
| + | |||
L. Malagò - [[User:LuigiMalago]] | L. Malagò - [[User:LuigiMalago]] | ||
Revision as of 14:20, 4 April 2009
Contents
Part 1: project profile
Project name
Optool
Combining Estimation of Distribution Algorithms and other Evolutionary techniques for combinatorial optimization
Project short description
The project will focus on the study, implementation, comparison and analysis of different algorithms for combinatorial optimization using techniques and algorithms proposed in Evolutionary Computation. In particular we are interested in the study of Estimation of Distribution Algorithms, a recent meta-heuristic, often presented as an evolution of Genetic Algorithms, where classical crossover and mutation operators, used in genetic algorithms, are replaced with operators that come from statistics, such as sampling and estimation. The focus will be on the implementation of new hybrid algorithms able to combine estimation of distribution algorithms with different approaches available in the evolutionary computation literature, such as genetic algorithms and evolutionary strategies, together with other local search techniques.
Dates
Start date: 2009/04/01
End date: till end
People involved
Project head(s)
M. Matteucci - User:MatteoMatteucci
L. Malagò - User:LuigiMalago
Students currently working on the project
G. Valentini - User:GabrieleValentini
Part 2: project description
The project will focus on the study, implementation, comparison and analysis of different algorithms for combinatorial optimization using techniques and algorithms proposed in Evolutionary Computation. In particular we are interested in the study of Estimation of Distribution Algorithms [1,2,3,4], a recent meta-heuristic, often presented as an evolution of Genetic Algorithms, where classical crossover and mutation operators, used in genetic algorithms, are replaced with operators that come from statistics, such as sampling and estimation. The focus will be on the implementation of new hybrid algorithms able to combine estimation of distribution algorithms with different approaches available in the evolutionary computation literature, such as genetic algorithms and evolutionary strategies, together with other local search techniques.
