Modern search methods for optimization consider hybrid search metaheuristics those employing general optimizers working together with a problem-specific local search procedure. The hybridism comes from the balancing of global and local search procedures. A challenge in such algorithms is to discover efficient strategies to cover all the search space, applying local search only in actually promi...

Many engineering optimization problems involve a special kind of discrete variable that can be represented by a number, but this representation has no significance. Such variables arise when a decision involves some situation like a choice from an unordered list of categories. This has two implications: The standard approach of solving problems with continuous relaxations of discrete variables ...

Recently various studies on recommendation systems based on the expert knowledge are actively conducted in the fusion research of IT field. Present systems recommend same items to everyone based on the knowledge of common experts group. While this paper suggests to show expert recommendations and researcher ideas based on the big-data analysis of existing research findings and designs the syste...

Due to the wide availability of social media and the wide range of real-life and human-centered applications, social networks have become an attractive research area. However, the temporal aspect of relations between entities in a social network has been widely ignored. We argue that the temporal aspect of social networks is the key to understand interactions and other phenomena happening in th...

We present two new classes of pattern search algorithms for unconstrained minimization: the rank ordered and the positive basis pattern search methods. These algorithms can nearly halve the worst case cost of an iteration compared to the classical pattern search algorithms. The rank ordered pattern search methods are based on a heuristic for approximating the direction of steepest descent, whil...

describes the starting state probability. The HMM is a generative model which means that we can understand the model by how it generates data. In this case we would sample a hidden state from the distribution p(h1), for example state 3, and then draw a sample from the distribution p(v1|h = 3) by drawing from the distribution represented by B:,3. Then we may draw h2 from A:,3 and continue in thi...

In this paper, we study the near-neighbor problem based on pattern similarity, a new type of similarity which conventional distance metrics such as Lp norm cannot model effectively. The problem, however, is important to many applications. For example, in DNA microarray analysis, the expression levels of two closely related genes may rise and fall under different external conditions or at differ...

Indexed pattern search in text has been studied for many decades. For small alphabets, the FM-Index provides unmatched performance, in terms of both space required and search speed. For large alphabets – for example, when the tokens are words – the situation is more complex, and FM-Index representations are compact, but potentially slow. In this paper we apply recent innovations from the field ...

An increasing number of RDF datasets are available on the Web. Querying RDF data requires the knowledge of a query language such as SPARQL; it also requires some information describing the content of these datasets. The goal of our work is to facilitate the interrogation of RDF datasets, and we present an approach for enabling users to search in RDF data using keywords. We propose the notion of...

We present a new problem solving architecture based upon extremely fast search and pattern recognition. This architecture, which we have named SUPREM, has been implemented in the chess machine/program Hitech, and has proven to be very successful. We describe the implementation in Hitech and the reasons for its success, and compare the SUPREM architecture to other well known problem solving arch...