| This dissertation research emphasizes explicit Building Block (BB) based MOEA performance and detailed symbolic representations. An explicit BB-based MOEA for solving constrained and real-world MOPS is developed, the Multiobjective Messy Genetic Algorithm II (MOMGA-II) to validate symbolic BB concepts. The MOMGA-II provides insight into solving difficult MOPS that is generally not realized through the use of implicit BB-based MOEA approaches. This insight is necessary to increase the effectiveness of all MOEA approaches.; Parallel MOEA (pMOEA) concepts are presented to potentially increase MOEA computational efficiency and effectiveness. Communications in a pMOEA implementation is extremely important, hence innovative migration and replacement schemes are detailed and tested. These parallel concepts support the development of the first explicit BB-based pMOEA, the pMOMGA-II. MOEA theory is also advanced through the derivation of the first MOEA population sizing theory. The sizing theory presented derives a conservative estimate of the MOEA population size necessary to achieve good results with a specified level of confidence. Validated results illustrate insight into building block phenomena, good efficiency, excellent effectiveness, and motivation for future research in the area of explicit BB-based MOEAs. |
