Design Patterns for Searching in C# by Fred Mellender

By Fred Mellender

Meant for the skilled C# programmer, this e-book provides an easy object-oriented interface to a few of the vintage combinatorial and graph looking algorithms. No past event with those is important. one of the subject matters mentioned are:
Depth-first Search
Breadth-first Search
Alpha/Beta Pruning
Dynamic Programming
Simulated Annealing

Many solved problems/applications are awarded, including:
Traveling Salesman
2-Person video game Tree

The resource code and set of rules library can be found for download.

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It is a queen on row 3 (because the “parent chain” above it represents a queen on row 2, and then a queen on row 1). The graph indicates that the partial solution 1-2-5 cannot be extended, so we ask node 5 for its “nextSibling”, which is node 6, and we visit that next. The partial solution 1-2-6 cannot be extended and node 6 has no nextSibling, so we backtrack and ask node 2 for its nextSibling, which is node 7. At this point we have partial solution 1-7. 3 Depth First Search 33 Design Patterns for Searching in C# Depth First Search Classes Let’s implement 8-queens with some C# classes.

The first line causes our parse to stop the firstChild chain if we run out of goals and have input left, or vice versa. To make a new child node (in clone), we copy the current node (clone does a deep copy of the goalStack and the toParse). toParse = new List(toParse); return clone; } After making the clone, we make a new ruleHead by popping the goalStack. Then we search for a rule that will resolve that. If we find a matching rule, we note the ruleNumber (when we backtrack and seek an alternative rule, we use this to keep track of rules already tried.

Summary of the Depth First Search pattern 1. Think of your application as a graph. Distinguish root nodes as the initial step in a solution to the problem. View a path from a root node to some other node as a partial solution (that may or may not lead to a complete solution). Conceptually, the graph contains all possible solutions and non-solutions. A complete solution will be represented as a path from some root node to some terminal node. Application context will determine the meaning of the nodes along the way.

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