About Rigorous Research
The Rigorous Research team was established in March 2015 in the Algorithms research group of the Department of Computer Science at the University of Sheffield. It is led by Dr. Pietro S. Oliveto. Rigorous Research focuses on understanding how computers may imitate processes occurring successfully in nature to automatically solve complex computational problems.
We invite both practitioners and theoreticians to contribute to the set of benchmarks currently being set up by working group 3 (WG3) of the COST Action. To contribute, join WG3 by e-mailing Pietro S. Oliveto.
Join Rigorous Research
We provide the necessary support for postdoctoral Marie Curie Fellowship applications. Qualified individuals with appropriate research plans can send an email to Pietro Oliveto.
If you would like to visit us as a guest researcher, contact Pietro Oliveto.
A tutorial on the computational complexity analysis of genetic programming was presented by Pietro S. Oliveto and Andrei Lissovoi.
We organised the ImAppNIO working group 3 (WG3) Black Box Discrete Optimization Benchmarking Workshop at PPSN.
An introductory tutorial to the runtime analysis of evolutionary algorithms was presented by Pietro S. Oliveto and Per Kristian Lehre.
We organised the ImAppNIO working group 3 (WG3) Black Box Discrete Optimization Benchmarking Workshop at GECCO.
The Gentle Introduction to the Time Complexity Analysis of Evolutionary Algorithms tutorial was presented by Pietro S. Oliveto.
We presented the On the Time and Space Complexity of Genetic Programming for Evolving Boolean Conjunctions paper by A. Lissovoi and P.S. Oliveto.
Current Research Topics
On the Time Complexity of Algorithm Selection Hyper-Heuristics for Multimodal Optimisation. [pdf]
On the benefits and risks of using fitness sharing for multimodal optimisation [open access]
Fast Artificial Immune Systems
D. Corus, P. S. Oliveto, D. Yazdani.
Artificial Immune Systems Can Find Arbitrarily Good Approximations for the NP-Hard Partition Problem
On the Runtime Analysis of Selection Hyper-Heuristics with Adaptive Learning Periods [pdf]