Search Results for author:
Found 6 papers, 3 papers with code
Population network structure impacts genetic algorithm optimisation performance
These results may constitute a starting point for network tuning and network control: seeing the network structure of the population as a parameter that can be tuned to improve the performance of evolutionary algorithms, and offer more realistic modelling of social learning.
Emergence of more contagious COVID-19 variants from the coevolution of viruses and policy interventions
Simulation runs reproduce the emergence of more contagious variants, and identifies the evolution of policy responses as a determinant cause of this phenomenon.
Evolutionary Strategies with Analogy Partitions in p-guessing Games
We characterize strategic behavior in mixed regimes with unstable values of p. Varying the number of iterations given to the genetic algorithm to learn about the game replicates the behavior of agents with different levels of reasoning of the level k approach.
A Genetic Algorithm approach to Asymmetrical Blotto Games with Heterogeneous Valuations
We show that our genetic algorithm converges to the analytical Nash equilibrium of the symmetric Blotto game.
Modelling SARS-CoV-2 coevolution with genetic algorithms
At the end of 2020, policy responses to the SARS-CoV-2 outbreak have been shaken by the emergence of virus variants, impacting public health and policy measures worldwide.
Qualities, challenges and future of genetic algorithms: a literature review
This compiling review aims at informing practitioners and newcomers in the field alike in their genetic algorithm research, and at outlining promising avenues for future research.
