10.6084/M9.FIGSHARE.12156582
A. Gargantilla Becerra
Rafael Lahoz-Beltra
A novel bio-inspired method of finding the fitness function of an evolutionary algorithm: Microbial Screening Methods In Silico (MSMIS)
One of the most delicate stages of an evolutionary algorithm is the evaluation of the goodness of the solutions by some procedure providing a fitness value. However, although there are general rules, it is not always easy to find an appropriate <br><div>evaluation function for a given problem. In the biological realm, today there is a variety of experimental methods under the name of microbial screening to identify and select bacteria from their traits, as well as to obtain their fitness. </div><br>In this repository we collect a group of scripts showing how given an optimization problem a colony of synthetic bacteria or bacterial agents by building an evaluation function is able to evaluate the fitness of candidate solutions. The evaluation <br>function is obtained simulating in silico a bacterial colony conducting the laboratory methods used in microbiology, biotechnology and synthetic biology to measure microbial fitness. Once the evaluation function is built, it is included in <br>the code of the genetic algorithm as part of the fitness routine. The practical use of this approach is illustrated in two classic optimization problems: 0/1 knapsack problem and 2-SAT Boolean problem. In silico routines have been programmed in Gro, a cell programming language oriented to synthetic biology, and can easily be customized to many other optimization problems. <br>
80108 Neural, Evolutionary and Fuzzy Computation
80110 Simulation and Modelling
60113 Synthetic Biology
figshare
2020
2020-04-20
2020-04-20
Software
348435 Bytes
MIT