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By M. Tim Jones

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Software/ common. General Search Paradigms Before we discuss some of the uninformed search methods, let’s look at two simple general uninformed search methods. ’ In this method, we generate a potential solution and then check it against the solution. If we’ve found the solution, we’re done, otherwise, we repeat by trying another potential solution. This is called ‘Generate and Test’ because we generate a potential solution, and then test it. Without a proper solution, we try again. Note here that we don’t keep track of what we’ve tried before; we just plow ahead with potential solutions, which is a true blind search.

These methods are explored here with discussion of their characteristics and complexities. Big-O notation will be used to compare the algorithms. This notation defines the asymptotic upper bound of the algorithm given the depth (d) of the tree and the branching factor, or the average number of branches (b) from each node. There are a number of common complexities that exist for search algorithms. 1. 1: Common orders of search functions. ) Linear (consistent with the number of nodes) Logarithmic Quadratic Geometric Combinatorial Big-O notation provides a worst-case measure of the complexity of a search algorithm and is a common comparison tool for algorithms.

Timmis, Jonathan Artificial Immune Systems: A New Computational Intelligence Approach Springer, 2002. Holland, John Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, 1975. McCarthy, John “Recursive Functions of Symbolic Expressions and their Computation by Machine (Part I),” Communications of the ACM, April 1960. H. “Rule-based Exper Systems: The Mycin Experiments of the Stanford Heuristic Programming Project,” Addison-Wesley, 1984. Winograd, Terry “Procedures as a Representation for Data in a Computer Program for Understanding Natural Language,” MIT AI Technical Report 235, February 1971.

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