Multi-population Evolutionary Computation Based on Lethal Chromosome and Its Application in Path Planning

Evolutionary computation has been widely applied in many fields. However, there are several disadvantages when evolutionary computation is applied to the field of path planning: Firstly, the fitness function struggles to discern discontinuous paths. Secondly, in cases of small population sizes, there is a notable tendency for homogenization within the population. Lastly, the reliance on hyperparameters becomes excessive in determining the optimal path. The paper tackles the aforementioned issues by introducing a multi-population genetic algorithm based on lethal chromosomes. Firstly, discontinuous paths are identified as lethal chromosomes and eliminated via a deletion operator. Secondly, the utilization of multi-population addresses the homogenization phenomenon within the population. Finally, adding a fitness evaluation process to the mutation operator promotes positive mutation and reduces reliance on hyperparameters. The paper presents experimental evidence showcasing the algorithm’s effectiveness in obstacle avoidance, path smoothness, and path length optimization. This has some instructive significance for mitigating discontinuous paths in path planning challenges using genetic algorithms.