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Evolutionary algorithms and HP Model for protein structure prediction

Protein structures prediction (PSP) is a computationally complex problem. Simplified models of the protein molecule (such as the HP Model) and the use of evolutionary algorithms (EAs) are among the most investigated techniques for PSP. However, the evaluation of a structure represented by the HP model considers only the number of hydrophobic contacts, which doesn’t

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Investigating Smart Sampling as a population initialization method for Differential Evolution in continuous problems

Recently, researches have shown that the performance of metaheuristics can be affected by population initialization. Opposition-based Differential Evolution (ODE), Quasi-Oppositional Differential Evolution (QODE), and Uniform-Quasi-Opposition Differential Evolution (UQODE) are three state-of-the-art methods that improve the performance of the Differential Evolution algorithm based on population initialization and different search strategies. In a different approach to achieve

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Evaluating differential evolution with penalty function to solve constrained engineering problems

Over the years, several metaheuristics have been developed to solve hard constrained and unconstrained optimization problems. In general, a metaheuristic is proposed and following researches are made to improve the original algorithm. In this paper, we evaluate a not so new metaheuristic called differential evolution (DE) to solve constrained engineering design problems and compare the

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Phylogenetic Differential Evolution

This paper presents a new technique for optimizing binary problems with building blocks. The authors have developed a different approach to existing Estimation of Distribution Algorithms (EDAs). Our technique, called Phylogenetic Differential Evolution (PhyDE), combines the Phylogenetic Algorithm and the Differential Evolution Algorithm. The first one is employed to identify the building blocks and to

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Efficient Identification of Duplicate Bibliographical References

In this work we present an approach to extract and to structure bibliographical references from BibTex files, allowing the identification of the duplicate ones, which can appear slightly different in different files. To deal with this problem, existing systems use classifiers, clustering or others algorithms, allied with an Edit Distance metric, to distinguish between duplicate

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