This paper describes the idea of MOEA/D and proposes a strategy for allocating the computational resource to different subproblems in MOEA/D. The new version of MOEA/D has been tested on all the CEC09 unconstrained MOP test instances.

The Proteins API provides searching and programmatic access to protein and associated genomics data such as curated protein sequence positional annotations from UniProtKB, as well as mapped variation and proteomics data from large scale data sources (LSS). Using the coordinates service, researchers are able to retrieve the genomic sequence coordinates for proteins in UniProtKB. This, the LSS genomics and proteomics data for UniProt proteins is programmatically only available through this service. A Swagger UI has been implemented to provide documentation, an interface for users, with little or no programming experience, to ‘talk’ to the services to quickly and easily formulate queries with the services and obtain dynamically generated source code for popular programming languages, such as Java, Perl, Python and Ruby. Search results are returned as standard JSON, XML or GFF data objects. The Proteins API is a scalable, reliable, fast, easy to use RESTful services that provides a broad protein information resource for users to ask questions based upon their field of expertise and allowing them to gain an integrated overview of protein annotations available to aid their knowledge gain on proteins in biological processes. The Proteins API is available at (http://www.ebi.ac.uk/proteins/api/doc).

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Abstract. MOEA/D is a novel and successful Multi-Objective Evolutionary Algorithms(MOEA) which utilizes the idea of problem decomposition to tackle the complexity from multiple objectives. It shows better performance than most nowadays mainstream MOEA methods in various test problems, especially on the quality of solution's distribution in the Pareto set. This paper aims to bring the strength of metamodel into MOEA/D to help the solving of expensive black-box multiobjective problems. Gaussian Random Field Metamodel(GRFM) is chosen as the approximation method. The performance is analyzed and compared on several test problems, which shows a promising perspective on this method.

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Gaussian process model is an effective and efficient method for approximating a continuous function. However, its computational cost increases exponentially with the size of training data set. A very popular way to alleviate this shortcoming is to cluster the whole training data set into a number of small clusters and then a local model is built for each cluster. However, widely used crisp clustering might not be accurate in the boundary areas among different clusters. This paper proposes a fuzzy clustering based method for improving approximation quality. Several clusters with overlaps are firstly obtained by Fuzzy C-Means clustering and then local models are built for these clusters. It has been demonstrated that this method can be used with evolutionary algorithms for dealing expensive optimization problems.

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