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February 20, 2002 Issue of Code Journal
(Back to Code Journal Archive)

Code Journal is a free, biweekly newsletter on programming and computer science provided jointly by and AI Horizon. There is also an archive of all past issues in both HTML and text formats.

This is the February 20th Issue. (If you want a pure text version, go to the text archive of Code Journal.)

CODE JOURNAL: Your Guide to Programming

February 20, 2002

In This Edition:
- Welcome to the Code Journal
- Genetic Algorithms
- Unraveling a Pattern of Strings
- Programming Challenge

Welcome to the Code Journal, a joint venture between and AI Horizon that aims to provide insightful articles on both C++ and algorithmic programming. Code Journal is helpware: in return for reading it, you are asked to help someone else out with their own programming problems. Good luck, and quick compiling.

C/C++ Programming by Alex Allain
Genetic Algorithms

This article will discuss the basic concept of Genetic Algorithms. Genetic algorithms are useful for solving problems having solutions representable as strings (hence the name Genetic Algorithm - the programming model is based on DNA). In terms of practical value, Genetic Algorithms are useful for solving problems in which the solutions are difficult to find by following a set algorithm. It functions as a sort of systematized brute force approach. Problems Genetic Algorithms are valuable for solving include scheduling problems, game AI, and other problems in which stable solutions are difficult to find.

A simple example is finding a five digit number that acts as the best solution to an expression; for example, if you wish to find the number that makes the expression x^2+2x-11 equal to 0, you could of course use brute force to solve the equation, but a genetic algorithm can also be used, and if you have a very complex expression, it may be of great value to use a genetic algorithm, especially when one considers the time saved over brute force. In a sense, all Genetic Algorithm problems boil down to solving complex expressions or sets of expressions, as all problems are representable in that fashion.

Genetic algorithms work from the same basis as evolutionary theory. A genetic algorithm has several components: a pool of solutions, a method of evaluating the effectiveness of each solution, a breeding function that combines the best solutions into new solutions, and a mutation function. The pool of solutions do not compete for resources; rather, each solution is tested by an evaluation function (called the "fitness" function), which then gives it a ranking based on its effectiveness at solving the problem compared to the other solutions.

The best solution strings are the ones that are ranked highest (that are the most "fit"); the breeding function takes two of the better performing solutions and combines them together into a new solution. The breeding function should repeat the process of randomly selecting two solutions and breeding them; the better performing functions should be given the higher percentage chance of being selected.

The breeding function generally works by taking slices of each solution and splicing them together into a new one. Solutions are often represented as strings, so generally, a breeding function will take fragments of random lengths from each string and concatenate them together to form a new string. Each fragment should be placed into the location in the new string that corresponds to its location in the old string. For example, if a string fragment is from positions 5 to 8 in the first string being bred, it should be placed into positions 5 through 8 in the new child solution.

After the strings have been bred, and the set of potential solutions has been refilled, it is important to have the mutation function. The mutation function is important because it introduces an element of randomness that allows variation in the solution sets, which otherwise would stagnate and have no advantage over a hand-crafted solution. Mutations may diminish the strength of some solutions, but in general it will increase the overall value of the solution set; by including a very small mutation rate, you introduce new traits that might never have otherwise existed within the pool.

Alexander Allain is the webmaster of
Contact him at
Algorithms and Programming by Eric Suh
Unraveling Patterns of Strings

Imagine that you are given a large string of text, and you wish to find a certain string that it contains. The text you are searching through is called the master string and the string you are searching for is called the pattern string. Now, there is a simple algorithm, for this search, and then there is an efficient one. The simple one, of course, follows like this:
   Master String:    GGRLGGT...
   Pattern String:   GGT
You see that the first two letters match in this position, but the third R T | character does not. Shift the pattern one character to the right.
   Master String:    GGRLGGT...
   Pattern String:    GGT
Now, the second character in the pattern doesn't match. Shift again.

And so on. The problem is that in this search, you are making a lot of unnecessary comparisons. In the first step, for instance, we see that since 'R' doesn't appear at all in the pattern, the next two string comparisons are a waste.

What we could do is take the pattern and shift it until the 'R' is behind it. That would save a few comparisons. The new algorithm could get away with only 3 shifts of the pattern to find the match. This algorithm is called the Boyer-Moore String Search Algorithm.

The algorithm, first of all, works back to front when comparing the pattern and the master string. The pattern is still moved from front to back, but the comparisons are made from the last character of the pattern. So, in the first step of the above example, the algorithm would compare 'T' in the pattern with 'R' in the master string.

If there is a mismatch, such as in the first step of example, then the algorithm goes through the pattern and finds in the remaining part of the pattern the rightmost occurrence of the mismatched character of the master string.

Now, this would be slow if you did this every loop, so the algorithm is usually precomputed. That means that you go through the pattern and record some often-used data into tables. Specifically, you need to know how much to shift for any character that you might encounter when there isn't a match. You precompute for each character how far you would shift the pattern if that character were to appear in the master string while we were comparing the last character in the pattern. Then, when we search, we can offset the table value with how far we've matched the pattern and the master string.

The table for the pattern of the example might look like this:
            G : 1
            T : 0
Any character that doesn't appear in the table would signify a shift of the length of the pattern. So, in the first step of the example, the pattern would be shifted 3 (since 'R' doesn't appear in the example string at all).

Let's take a more complicated pattern:
The searching table would look like this: (For convenience sake, the algorithm takes the second occurrence from the end of the last letter.)
            A: 2
            E: 6
            G: 3
            T: 1
            W: 7
The algorithm also computes substrings. For instance, say that we matched the last two characters of this new pattern. The character bank doesn't help very much, since it only tells us to shift it once. What we would like is if the algorithm saw that there was a pattern in the string, and that the "TA" substring occurs again at the beginning of the string.

Well, the algorithm does, and we compute substrings before the search. There are only as many substrings as there are characters in the pattern, because we are only looking for substrings that include the last character of the pattern (since we're searching from the last character to the left).

There are only seven values in the substring table, one for each character. The value at each character is where the next occurrence is of the part of the pattern up to that character. So, the value at the second to last 'A' of the substring table would contain the shift value for the substring "TA".

The substring table would look like this:
            T: 8
            A: 8
            W: 8
            E: 8
            G: 8
            A: 8
            G: 8
            A: 8
            T: 2
            A: 1
Thus, the algorithm takes whatever is larger, the character shift value or the substring value, and shifts the pattern by that much. So, the pattern skips some of the letters when searching, which greatly increases the speed of the search. This search is one of the best search algorithms for natural text. It should be interesting to program this.

Eric Suh is the webmaster of AI Horizon, a site devoted to Artificial Intelligence and Computer Science programming.
Contact him at

Code Challenge
Every issue, we will issue a programming challenge and ask people to submit their solutions within two weeks. A few of the best solutions will be published the next issue, along with a new challenge.

No one submitted any answers to last week's code challenge, so we will offer it again this week, except with a hint.

This Week's Challenge

One famous chess problem is the Knight's Tour, in which a knight, placed on a starting square, is then moved to every other square on the chessboard. The Knight's Tour is a fairly typical chess problem. Your challenge is not only to write a program to calculate the Knight's Tour from any square on the board, but also to allow the user to add up to five pieces to the board that the moving knight must avoid capturing and must also avoid being moved onto a square where it could be captured. As an interesting note, in The Psychology of Chess, the authors talk about a test for chess talent that works under similar conditions and is based on the speed of response. The test accurately predicated a future grandmaster.

HINT: Use recursion.

Send your solutions to as source code files, and you may find it published. Please include either your name or an identifying username so that we may attribute the solution to you in the next newsletter. If you wish, you may ask us to withhold your name.

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