Monday 6 March
|16:30 - 17:25||Lecture 5||
This lecture will present the fundamentals of the Evolutionary Computation and of the main types of evolutionary algorithms. A survey of the applications of these algorithms in HEP data analysis will also be presented, illustrating the early development phase of an emerging technique in HEP data analysis.
A new evolutionary algorithm, Gene Expression Programming, and its first application to HEP data analysis will also be presented.
The lecture targets both physicists and computer scientists interested in algorithms for data analysis.
A minimal general background in particle physics data analysis techniques is sufficient for understanding the topic. No a priory knowledge on Evolutionary Computation is required.
- Natural evolution
- Simulation of the natural evolution on a computer
- Specific terminology
Structure of an evolutionary algorithm
- Problem representation (encoding solutions)
- Fitness functions
- Genetic operators
- Termination conditions
Types of evolutionary algorithms: Genetic Algorithms, Genetic Programming
- Problem representation for each type of algorithm
- Genetic variation in each type of algorithm
- Comparison of the different types of algorithms
- Applications in HEP data analysis
New development in Evolutionary Computation: Gene Expression Programming
- Problem representation
- Genetic variation
- First application of Gene Expression Programming to HEP data analysis
Recommendations on when to use Evolutionary Algorithms