The following function is applied onto a graph and it specifically implements the method of breadth-first search. To visit all nodes connected to node k of a graph, we put the k in a FIFO queue and then go into a loop, during which we get the next node from the queue and, if we have not already visited it, we visit it and push into the queue all nodes belonging to the adjacency list of this node, continuing this process until we empty the queue.

## Archive for March, 2011

The following program creates the syntax tree of a mathematical expression in prefix notation. For simplicity, we assume that the terms of the expression are individual characters (digits). Each time the recursive function parse() is called, a new node is created, whose value is the next character of the expression. If the value is a term (digit), we return the new node. However, if it is an operator, we set the left and right pointers to point the tree made (recursively) for the two operands.

The iterative (non-recursive) function preorderTreeTraverse() can perform preorder traversing of a binary tree with the help of a stack that can hold pointers to nodes of the tree.

Also, the iterative (non-recursive) function layerTreeTraverse() can perform level-order traversing of a binary tree with the help of a queue that can hold pointers to nodes of the tree.

To calculate a prefix expression, we either convert a number from ASCII to decimal (in the loop ‘while’ at the end of the program) or implement the operation indicated by the first character of the expressions to the two terms, with a recursive calculation. This function is recursive, but it uses a global array containing the expression and an index number for the current character of the expression. The index number goes beyond each sub-expression calculated.

The elegant recursive solution to a problem is most of the times invaluable. Although the iterative solution of that problem is likely to have a better space and time complexity, it is often preferred to use the recursive version for clarity and simplicity. It is remarkable how easily a problem can be solved by use of a recursive manner. In this article we will try to record a collection of useful recursive functions:

The following program converts an expression from infix to postfix notation. The conversion is carried out with the help of a stack. For example, to turn the expression (A + B) into the postfix form A B +, we ignore the left parenthesis, convert the A to postfix form, we store the + operator on the stack, we convert B to postfix form and then, when find the right parenthesis, we pop the + operator from the top of the stack. In particular, the following implementation works only for the addition and multiplication of integers.

The following program reads any postfix expression that includes integer multiplication and addition, evaluates the expression and displays the final result on the screen. The program stores the intermediate results in a stack of integers.

The terms (operands) are pushed onto the stack. The operators are applied to the two entries at the top of the stack (the two entries are popped from the stack); the result is pushed back into the stack. Because the order in which the two pop() functions are performed in the expression of this code is not specified in C++, the code for some non-commutative operators, such as the ones for subtraction and division, would be somewhat more complicated.

The following program demonstrates a significant processing operation on arrays of strings. More precisely, we present the re-sorting of a set of strings in order to classify them. So, we read the strings and place them in a temporary storage area (buffer) large enough to be able to store all, keeping in an array a pointer to each string. Then we re-sort the pointers (only pointers and not strings to increase the performance) to place the pointer to the “smallest” string at the first position of the array, the pointer to the next “largest” string at the second position of the array, and so on.

The following program reads a set of edges that define a graph and creates a representation of this graph with an adjacency list. The adjacency list of a graph is an array of lists, one for each vertex, where the j-th list contains a linked list of the nodes which are connected with the j-th vertex.

The following program reads a set of edges that define an undirected graph and creates a representation of this graph with an adjacency matrix, giving a[i][j] and a[j][i] the value of 1 if there is an edge from i to j or from j to i in the graph, or the value of 0 if it does not exist. Also, we assume that the number of vertices V is a constant known at compilation time. Otherwise, there should be dynamic memory allocation for the array that represents the adjacency matrix.

The following program finds all the occurrences of a word in a text string. We set the text string as an array of characters of fixed size (although we could, instead, use the operator ‘new’) and read it from the standard input using the function cin.get(). The memory allocation for the word entered in the command line and passed on as argument is done by the system before this program is called and we find the pointer to the string in argv[1]. For every starting position i in the array a, tries to associate the substring starting from this position with the p, checking character by character for equality. Each time we reach the end of p successfully, we display the starting position (i) of the word in the text on the screen.

**Taxonomy**

The Ant Colony System algorithm is an example of an Ant Colony Optimization method from the field of Swarm Intelligence, Metaheuristics and Computational Intelligence. Ant Colony System is an extension to the Ant System algorithm and is related to other Ant Colony Optimization methods such as Elite Ant System, and Rank-based Ant System.

**Taxonomy**

The Cultural Algorithm is an extension to the field of Evolutionary Computation and may be considered a Meta-Evolutionary Algorithm. It more broadly belongs to the field of Computational Intelligence and Metaheuristics. It is related to other high-order extensions of Evolutionary Computation such as the Memetic Algorithm.

**Taxonomy**

Harmony Search belongs to the fields of Computational Intelligence and Metaheuristics.

**Taxonomy**

Simulated Annealing is a global optimization algorithm that belongs to the field of Stochastic Optimization and Metaheuristics. Simulated Annealing is an adaptation of the Metropolis-Hastings Monte Carlo algorithm and is used in function optimization. Like the Genetic Algorithm, it provides a basis for a large variety of extensions and specialization’s of the general method not limited to Parallel Simulated Annealing, Fast Simulated Annealing, and Adaptive Simulated Annealing.

**Taxonomy**

Memetic Algorithms have elements of Metaheuristics and Computational Intelligence. Although they have principles of Evolutionary Algorithms, they may not strictly be considered an Evolutionary Technique. Memetic Algorithms have functional similarities to Baldwinian Evolutionary Algorithms, Lamarckian Evolutionary Algorithms, Hybrid Evolutionary Algorithms, and Cultural Algorithms. Using ideas of memes and Memetic Algorithms in optimization may be referred to as Memetic Computing.

Probably sometime you will need to write a program in assembly language. For this reason, in this article we will develop a simple and typical executable “Hello World” program in assembly language to familiarize yourself with the process.

Black Box testing and general testing is a key step in the iterative development methodologies (e.g. UP, XP, Agile) as it performs both the validation and verification of units in a system.

Within the framework of the course “**Computer Networks III – Theory**” (Department of Informatics and Communications, T.E.I. of Central Macedonia) we were asked to write a presentation related to the content of the course. The topic of my presentation was Genetic Routing.