Difference between revisions of "Evoptool: Evolutionary Optimization Tool"

Revision as of 15:34, 26 April 2009

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.