Multi-objective fast messy genetic algorithm solving deception problems

Deception problems are among the hardest problems to solve using ordinary genetic algorithms. Recent studies show that Bayesian optimization can help in solving these problems. This work compares the results acquired from the multiobjective fast messy genetic algorithm (MOMGA-II), multiobjective Bayesian optimization algorithm (mBOA), and the nondominated sorting genetic algorithm-II (NSGA-II) when applied to three different deception problems. The three deceptive problems studies are: interleaved minimal deceptive problem, interleaved 5-bit trap function, and the interleaved 6-bit bipolar function. The unmodified MOMGA-II, by design, explicitly learns building block linkages which is required if an algorithm is to solve these hard deception problems. Preliminary results using the MOMGA-II are favorable.