Using a multi-objective artificial immune system approach for biodiversity conservation

A key issue in policies for sustainable development is related to biodiversity conservation, which aim is to minimize the costs of conservation while ensuring the maximal biodiversity representation, a NP-hard problem. We propose the use of a constrained multi-objective artificial immune system algorithm (MAIS), based on principles of systematic conservation planning (SCP), incorporating allelic and habitat information to deal with the biodiversity conservation problem. As a case study, we used an Eugenia dysenterica data set. We were able to identify the best set of populations that should be protected to preserve the species diversity. The proposed approach can be used to help construct in situ conservation schemes with maximal genetic richness, and also be extended to ex situ conservation. This is the first time that an artificial immune system algorithm is applied to the SCP problem using genetic and habitat information as well.