A synthetic biology approach for the design of genetic algorithms with bacterial agents

TL;DR

This paper introduces BAGA, a genetic algorithm inspired by synthetic biology, where all evolutionary steps are conducted by programmable synthetic bacteria, demonstrating potential for solving optimization problems.

Contribution

The paper presents a novel approach of designing evolutionary algorithms using synthetic bacteria, integrating synthetic biology principles into algorithmic processes.

Findings

BAGA successfully solves simple optimization problems.

Synthetic biology principles can inspire new evolutionary algorithm designs.

Simulation results support the feasibility of bacteria-based evolutionary computation.

Abstract

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.