Messy Genetic Algorithm Based Multi-Objective Optimization 1 Messy Genetic Algorithm Based Multi-Objective Optimization: A Comparative Statistical Analysis

Many real-world scientific and engineering applications involve finding solutions to “hard” Multiobjective Optimization Problems (MOPs). Genetic Algorithms (GAs) can be extended to find acceptable MOP Pareto solutions. The intent of this discussion is to illustrate that modifications made to the Multi-Objective messy GA (MOMGA) have further improved the efficiency of the algorithm. The MOMGA is a Multiobjective Evolutionary Algorithm (MOEA) extension of the existing single-objective building block (BB) based messy Genetic Algorithm (mGA). The modified MOMGA uses a probabilistic BB approach to initializing the population referred to as Probabilistically Complete Initialization (PCI). This does have the effect of improving the efficiency of the MOMGA through the reduction of the computational bottleneck encountered with the mGA. This paper presents statistical results obtained from the modified MOMGA compared to the results of the original MOMGA as well as those obtained by other MOEAs as employed for a generic test suite.