Modular Differential Evolution

Genetic and Evolutionary Computation Conference (GECCO), pp. 864–872

Abstract

New contributions in the field of iterative optimisation heuristics are often made in an iterative manner. Novel algorithmic ideas are not proposed in isolation, but usually as extensions of a preexisting algorithm. Although these contributions are often compared to the base algorithm, it is challenging to make fair comparisons between larger sets of algorithm variants. This happens because even small changes in the experimental setup, parameter settings, or implementation details can cause results to become incomparable. Modular algorithms offer a way to overcome these challenges. By implementing the algorithmic modifications into a common framework, many algorithm variants can be compared, while ensuring that implementation details match in all versions.

Authors 4

1. Diederick Vermetten Aachen

   Leiden University

   Affiliation as printed

   LIACS, Leiden University, Leiden, Netherlands

2. Fabio Caraffini

   Swansea University

   Affiliation as printed

   Swansea University, Swansea, United Kingdom

3. Anna V. Kononova Aachen

   Leiden University

   Affiliation as printed

   LIACS, Leiden University, Leiden, Netherlands

4. Thomas Bäck Aachen

   Leiden University

   Affiliation as printed

   LIACS, Leiden University, Leiden, Netherlands