Evolutionary optimisation algorithms
The formal framework that has been used to develop the search engine can also be used to develop evolutionary optimisation algorithms,
- multi-obective and many-obective problems with continus, discrete ou mixed objectives,
- with constraints.
Such a genetic algorithm has been developed for the 0–1 multi-objective knapsack problem. It has been validated and published at the GECCO 2021 conference by Jean Ruppert (Mathematics and Computing S.à.r.l.) and Marharyta Aleksandrova and Thomas Engel (University of Luxembourg). One of the main findings ist that according to the hypervolume indicator performance is superior to the standard multi-objective algorithms (NSGA-ii NSGA-iii). This publication is available online,
In November 2022 the same authors published an in-depth analysis of the above algorithm in the journal Algorithms.
- k-Pareto Optimality-Based Sorting with Maximization of Choice and Its Application to Genetic Optimization,
- Ruppert J, Aleksandrova M, Engel T. k-Pareto Optimality-Based Algorithms. 2022; 15(11):420. https://doi.org/10.3390/a15110420.