A synthetic biology approach for the design of genetic algorithms with bacterial agents
This work proposes a novel approach for designing evolutionary algorithms, potentially benefiting researchers in synthetic biology and optimization, but it appears incremental as it builds on existing inspiration from bacteria.
The paper tackled the design of evolutionary algorithms using synthetic bacteria, introducing BAGA, a genetic algorithm where all steps are conducted by synthetic bacteria, and demonstrated its utility by solving optimization problems like function optimization and the knapsack problem in simulations.
Bacteria have been a source of inspiration for the design of evolutionary algorithms. At the beginning of the 20th century synthetic biology was born, a discipline whose goal is the design of biological systems that do not exist in nature, for example, programmable synthetic bacteria. In this paper, we introduce as a novelty the designing of evolutionary algorithms where all the steps are conducted by synthetic bacteria. To this end, we designed a genetic algorithm, which we have named BAGA, illustrating its utility solving simple instances of optimization problems such as function optimization, 0/1 knapsack problem, Hamiltonian path problem. The results obtained open the possibility of conceiving evolutionary algorithms inspired by principles, mechanisms and genetic circuits from synthetic biology. In summary, we can conclude that synthetic biology is a source of inspiration either for the design of evolutionary algorithms or for some of their steps, as shown by the results obtained in our simulation experiments.