. Results obtained using Backtracking Search Method (BTSM) is compared with Adjacent Pairwise Interchange Method (APIM) and global search for a test system. Key-Words:- Distribution system restoration, cold load pickup, step by step restoration. 1

There has been substantial recent interest in two new families of search techniques. One family consists of nonsystematic methods such as gsat; the other contains systematic approaches that use a polynomial amount of justification information to prune the search space. This paper introduces a new

. These networks have been widely used for both prediction and classification in many different areas. Although the most popular method for training these networks is backpropagation, other optimization methods such as tabu search have been applied to solve this problem. This chapter describes two training

A new suboptimal search strategy for feature selection is presented. It represents a more sophisticated version of "classical" floating search algorithms (Pudil et al., 1994), attempts to remove some of their potential deficiencies and facilitates finding a solution even closer

The value of depth-first search or "backtracking" as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and ar algorithm for finding the biconnected components

of computational experience for a variety of applications. Part I1 of this study (to appear in a subsequent issue) examines more advanced considerations, applying the basic ideas to special settings and outlining a dynamic move structure to insure finiteness. Part I1 also describes tabu search methods for solving

A new and conceptually simple data structure, called a suffix array, for on-line string searches is intro-duced in this paper. Constructing and querying suffix arrays is reduced to a sort and search paradigm that employs novel algorithms. The main advantage of suffix arrays over suffix trees

A new access meth d, called M-tree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion

the sequences and e ciently answer similarity queries. We provide experimental results which show that our method is superior to search based on sequential scanning. Our experiments show that a few coefficients (1-3) are adequate to provide good performance. The performance gain of our method increases

Search operations in databases require special support at the physical level. This is true for conventional databases as well as spatial databases, where typical search operations include the point query (find all objects that contain a given search point) and the region query (find all objects