Extended Multi-objective fast messy Genetic Algorithm Solving Deception Problems

Deception problems are among the hardest problems to solve using ordinary genetic algorithms. Designed to simulate a high degree of epistasis, these deception problems imitate extremely difficult real world problems. [1]. Studies show that Bayesian optimization and explicit building block manipulation algorithms, like the fast messy genetic algorithm (fmGA), can help in solving these problems. This paper compares the results acquired from an

extended

multiobjective fast messy genetic algorithm (MOMGA-IIa), ordinary multiobjective fast messy genetic algorithm (MOMGA-II), multiobjective Bayesian optimization algorithm (mBOA), and the non-dominated sorting genetic algorithm-II (NSGA-II) when applied to three different deception problems. The

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MOMGA-II is enhanced with a new technique exploiting the fmGA’s basis function to improve partitioned searching in both the genotype and phenotype domain. The three deceptive problems studied are: interleaved minimal deceptive problem, interleaved 5-bit trap function, and interleaved 6-bit bipolar function. The

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MOMGA-II, by design, explicitly learns building block linkages, a requirement if an algorithm is to solve these hard deception problems. Results using the MOMGA-IIa are excellent when compared to the non-explicit building block algorithm results of both the mBOA and NSGA-II.