MOGASI: A multi-objective genetic algorithm for efficiently handling constraints and diversified decision variables

Genetic algorithms are versatile tools that are able to tackle a wide range of real-world problems. In this paper, we propose a general-purpose genetic algorithm for black box multi-objective optimization well suited for different types of variables (continuous, discrete, and combinatorial) and constraints (linear and non- linear, equalities and inequalities) without requiring any customization, such as problem-dependent operators. The basic idea is to extract as much information as possible from the characteristics of the decision variables in order to activate the most appropriate routines automatically. We apply this strategy to a real world problem: The layout optimization of a Wireless Sensor Network (WSN). This problem can be equivalently formulated in two different ways, both presenting some critical points for an effective application of standard genetic algorithms. We show how our algorithm can learn from its structure and solve the problem more efficiently than other classical genetic approaches.