A Gaussian Artificial Immune System for Multi-Objective optimization in continuous domains

This paper proposes a Multi-Objective Gaussian Artificial Immune System (MOGAIS) to deal effectively with building blocks (high-quality partial solutions coded in the solution vector) in multi-objective continuous optimization problems. By replacing the mutation and cloning operators with a probabilistic model, more specifically a Gaussian network representing the joint distribution of promising solutions, MOGAIS takes into account the relationships among the variables of the problem, avoiding the disruption of already obtained high-quality partial solutions. The algorithm was applied to three benchmarks and the results were compared with those produced by state-of-the-art algorithms.