Book Details:-

# Hardcover: 1184 pages
# Publisher: The MIT Press; 2nd edition (September 1, 2001)
# Language: English
# ISBN-10: 0262032937
# ISBN-13: 978-0262032933
# Product Dimensions: 9.1 x 8.1 x 2 inches

Table of Contents

* 0. Introduction, history, and course goals
* Recursion
o α. Solving recurrences
o 1. Recursion
o 2. Divide and conquer
o 3. Fast Fourier transforms
o 4. Dynamic programming
* A. Greedy algorithms
* Randomization
o 5. Nuts and bolts (randomized quicksort)
o 6. Treaps and skip lists
o 7. Uniform and universal hashing
* Amortized analysis
o 8. Aggregation, taxation, potential
o 9. Scapegoat trees and splay trees
o 10. Maintaining disjoint sets ("union-find")

One of the
main problems in chip design is the huge number of possible
combinations of individual chip elements, leading to a combinatorial
explosion as chips become more complex. New key results in theoretical
computer science and in the design of data structures and efficient
algorithms can be applied fruitfully here. The application of ordered
binary decision diagrams (OBDDs) has led to dramatic performance
improvements in many computer-aided design projects. This textbook
provides an introduction to the foundations of this interdisciplinary

For the past several years mathematics majors in the computing track at the University of Pennsylvania have taken a course in continuous algorithms (numerical analysis) in the junior year, and in discrete algorithms in the senior year. This book has grown out of the senior course as I have been teaching it recently.

he design of algorithms is studied, according to methodology and
application. Methodologies
include: divide and conquer, dynamic programming, and greedy
strategies. Applications involve: sorting, ordering and
searching, graph algorithms, geometric algorithms, mathematical
(number theory, algebra and linear algebra) algorithms, and string
matching algorithms. Analysis of algorithms is studied - worst case,
average case, and amortized - with an emphasis on the close connection
between the time complexity
of an algorithm and the underlying data structures.

A Genetic Algorithm Tutorial
by Darrell Whitley
Computer Science Department
Colorado State University.

This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, including the schema theorem as well as recently developed exact models of the canonical genetic algorithm.