Let's begin with the picture on the front cover. You may have observed
that the portra让of Alan Turing is constructed from a number of pictures
("tiles") of great computer scientists and mathematicians.
Suppose you were asked in an interview to design a program that
takes an 垃nage and a collection of s x s-sized tiles and produce a mosaic
from the tiles that resembles the image. A good way to begin may be to
partition the image into s x s-sized squares, compute the average color
of each such image square, and then find the tile that is closest to it in
the color space. Here distance in...
This book aims at attracting the interest of researchers and practitioners around the applicability of meta-heuristic algorithms to practical scenarios arising from different knowledge disciplines. Emphasis is placed on evolutionary algorithms and swarm intelligence as computational means to efficiently balance the tradeoff between optimality of the produced solutions and the complexity derived from their estimation.
This book presents state of the art contributes to Simulated Annealing (SA) that is a well-known probabilistic meta-heuristic. It is used to solve discrete and continuous optimization problems. The significant advantage of SA over other solution methods has made it a practical solution method for solving complex optimization problems. Book is consisted of 13 chapters, classified in single and multiple objectives applications and it provides the reader with the knowledge of SA and several applications.
Meta-theoretical results on the decidability, generatire capacity, and recognition complexity o~ several syntactic theories are surveyed These include context-free , lexical func-computer o r a parallel array of neurons. These results over whole classes of machines are very difficult to obtain, and none el any significance exist for parsiD.g problems. Restricting ourselves to a specific machine model and an algorithm M for j', we can ask about the cost. (e.g time or space) e(z) of executing M on a specific input z. ...
We present a novel approach for discovering word categories, sets of words sharing a signiﬁcant aspect of their meaning. We utilize meta-patterns of highfrequency words and content words in order to discover pattern candidates. Symmetric patterns are then identiﬁed using graph-based measures, and word categories are created based on graph clique sets. Our method is the ﬁrst pattern-based method that requires no corpus annotation or manually provided seed patterns or words.