FastPGA: A Dynamic Population Sizing Approach for Solving Expensive Multiobjective Optimization Problems. We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm (FastPGA). FastPGA uses a new fitness assignment and ranking strategy for the simultaneous optimization of multiple objectives where each solution evaluation is computationally- and/or financially-expensive. This is often the case when there are time or resource constraints involved in finding a solution. A population regulation operator is introduced to dynamically adapt the population size as needed up to a user-specified maximum population size. Computational results for a number of well-known test problems indicate that FastPGA is a promising approach. FastPGA outperforms the improved nondominated sorting genetic algorithm (NSGA-II) within a relatively small number of solution evaluations.

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  1. Karasakal, Esra; Silav, Ahmet: A multi-objective genetic algorithm for a bi-objective facility location problem with partial coverage (2016)
  2. Gong, Wenyin; Cai, Zhihua: An improved multiobjective differential evolution based on Pareto-adaptive (\epsilon) (Porson)-dominance and orthogonal design (2009)
  3. Eskandari, Hamidreza; Geiger, Christopher D.; Lamont, Gary B.: FastPGA: A dynamic population sizing approach for solving expensive multiobjective optimization problems (2007) ioport