This paper presents a two-level protein folding optimization on a three-dimensional AB off-lattice model. The first level is responsible for forming conformations with a good hydrophobic core or a set of compact hydrophobic amino acid positions. These conformations are forwarded to the second level, where an accurate search is performed with the aim of locating conformations with the best energy value. The optimization process switches between these two levels until the stopping condition is satisfied. An auxiliary fitness function was designed for the first level, while the original fitness function is used in the second level. The auxiliary fitness function includes expression about the quality of the hydrophobic core. This expression is crucial for leading the search process to the promising solutions that have a good hydrophobic core and, consequently, improves the efficiency of the whole optimization process. Our differential evolution algorithm was used for demonstrating the efficiency of the two-level optimization. It was analyzed on well-known amino acid sequences that are used frequently in the literature. The obtained experimental results show that the employed two-level optimization improves the efficiency of our algorithm significantly, and that the proposed algorithm is superior to other state-of-the-art algorithms.
Although powerful optimization algorithms have been introduced for PFO, researchers are also focused on the time-consuming optimization problems. For solving such a problem for PFO, the authors in @cite_12 introduced a new version of DE which uses the computationally cheap surrogate models and gene expression programming. The purpose of the incorporated gene expression programming is to generate a diversified set of configurations, while the purpose of the surrogate model is to help DE to find the best set of configurations. Additionally, a covariance matrix adaptation evolution strategy was also adopted, to explore the search space more efficiently. This algorithm is called SGDE, and it outperforms all state-of-the-art algorithms according to the number of function evaluations. Its efficiency was also demonstrated in terms of runtime on the adopted all-atom model which represents time-consuming PFO.
@cite_12
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Abstract Protein structure prediction (PSP) plays an important role in the field of computational molecular biology. Although powerful optimization algorithms have been proven effective to tackle the PSP, researchers are faced with the challenge of time consuming simulations. This paper introduces a new modification of differential evolution (DE) which makes use of the computationally cheap surrogate models and gene expression programming (GEP) in order to address the aforementioned issue. The incorporated GEP is used to generate a diversified set of configurations, while radial basis function (RBF) surrogate model helps DE to find the best set of configurations. In addition to this, covariance matrix adaptation evolution strategy (CMAES) is also adopted to explore the search space more efficiently. The introduced algorithm, called SGDE, is tested on real-world proteins from the Protein data bank (PDB) using both a simplified and an all-atom model. The experiments show that SGDE performs better than the state-of-the-art algorithms on the PSP problems in both terms of the convergence rate and accuracy. In the case of run time complexity, SGDE significantly outperforms the other competitive algorithms for the adopted all-atom model. Abstract The purpose of this paper is to present a new and an alternative differential evolution (ADE) algorithm for solving unconstrained global optimization problems. In the new algorithm, a new directed mutation rule is introduced based on the weighted difference vector between the best and the worst individuals of a particular generation. The mutation rule is combined with the basic mutation strategy through a linear decreasing probability rule. This modification is shown to enhance the local search ability of the basic DE and to increase the convergence rate. Two new scaling factors are introduced as uniform random variables to improve the diversity of the population and to bias the search direction. Additionally, a dynamic non-linear increased crossover probability scheme is utilized to balance the global exploration and local exploitation. Furthermore, a random mutation scheme and a modified Breeder Genetic Algorithm (BGA) mutation scheme are merged to avoid stagnation and or premature convergence. Numerical experiments and comparisons on a set of well-known high dimensional benchmark functions indicate that the improved algorithm outperforms and is superior to other existing algorithms in terms of final solution quality, success rate, convergence rate, and robustness. Abstract An efficient hybrid genetic algorithm (HGA) approach for solving the economic dispatch problem (EDP) with valve-point effect is presented in this paper. The proposed method combines the GA algorithm with the differential evolution (DE) and sequential quadratic programming (SQP) technique to improve the performance of the algorithm. GA is the main optimizer, while the DE and SQP are used to fine tune in the solution of the GA run. To improve the performance of the SQP, the cost function of EDP is approximated by using a smooth and differentiable function based on the maximum entropy principle. An initial population obtained by using uniform design exerts optimal performance of the proposed hybrid algorithm. The combined algorithm is validated for two test systems consisting of 13 and 40 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. The proposed combined method outperforms other algorithms reported in literatures (EP, EP–SQP, PSO, PSO–SQP) for EDP considering valve-point effects. State-of-the-art item recommendation algorithms, which apply Factorization Machines (FM) as a scoring function and pairwise ranking loss as a trainer (PRFM for short), have been recently investigated for the implicit feedback based context-aware recommendation problem (IFCAR). However, good recommenders particularly emphasize on the accuracy near the top of the ranked list, and typical pairwise loss functions might not match well with such a requirement. In this paper, we demonstrate, both theoretically and empirically, PRFM models usually lead to non-optimal item recommendation results due to such a mismatch. Inspired by the success of LambdaRank, we introduce Lambda Factorization Machines (LambdaFM), which is particularly intended for optimizing ranking performance for IFCAR. We also point out that the original lambda function suffers from the issue of expensive computational complexity in such settings due to a large amount of unobserved feedback. Hence, instead of directly adopting the original lambda strategy, we create three effective lambda surrogates by conducting a theoretical analysis for lambda from the top-N optimization perspective. Further, we prove that the proposed lambda surrogates are generic and applicable to a large set of pairwise ranking loss functions. Experimental results demonstrate LambdaFM significantly outperforms state-of-the-art algorithms on three real-world datasets in terms of four standard ranking measures.