Abstract
The objective of this chapter is to compare, with respect to the performance two versions of EDA type algorithm (AETC – EDA) whose probabilistic distribution structure is learned at each step using the Extended Tree Adaptive Learning (ETreeAL) Algorithm. One of the versions has a Metropolis step in the inner loop, and the other do not. The samples are generated evaluating the objective function, and running Boltzmann selection roulette through all the cliques of the learned model. In the outer loop the temperature parameter is updated. The efficiency is tested using 4 benchmark functions known by its difficulty for evolutionary algorithms. The experiments were performed with 50 and 100 variables. As results of the experiments, the algorithms obtain the optimum in practically in all the cases, both algorithms use the learned cliques at each step of the inner cycles to obtain the best solution at hand. The algorithm with Metropolis step uses less evaluations in all cases, except for the Fc 2 function.
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Ponce-de-Leon-Senti, E.E., Diaz-Diaz, E. (2014). Optimization by Structure Learning during Algorithm Execution Using an Adaptive Extended Tree Cliqued – EDA (AETC – EDA). In: Schuetze, O., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation III. Studies in Computational Intelligence, vol 500. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01460-9_2
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DOI: https://doi.org/10.1007/978-3-319-01460-9_2
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