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472 Part Three Key System Applications for the Digital Age

same solution again with the same input data, or always guarantee the best
solution. They are very sensitive and may not perform well if their training
covers too little or too much data. In most current applications, neural net-
works are best used as aids to human decision makers instead of substitutes
for them.

Genetic Algorithms
Genetic algorithms are useful for finding the optimal solution for a specific
problem by examining a very large number of possible solutions for that prob-
lem. They are based on techniques inspired by evolutionary biology, such as
inheritance, mutation, selection, and crossover (recombination).
A genetic algorithm works by representing information as a string of 0s and
1s. The genetic algorithm searches a population of randomly generated strings
of binary digits to identify the right string representing the best possible solu-
tion for the problem. As solutions alter and combine, the worst ones are dis-
carded and the better ones survive to go on to produce even better solutions.
In Figure 11.10, each string corresponds to one of the variables in the prob-
lem. One applies a test for fitness, ranking the strings in the population accord-
ing to their level of desirability as possible solutions. After the initial population
is evaluated for fitness, the algorithm then produces the next generation of
strings, consisting of strings that survived the fitness test plus offspring strings
produced from mating pairs of strings, and tests their fitness. The process
continues until a solution is reached.
Genetic algorithms are used to solve problems that are very dynamic and
complex, involving hundreds or thousands of variables or formulas. The
problem must be one where the range of possible solutions can be repre-
sented genetically and criteria can be established for evaluating fitness.
Genetic algorithms expedite the solution because they are able to evaluate
many solution alternatives quickly to find the best one. For example, General

FIGURE 11.10 THE COMPONENTS OF A GENETIC ALGORITHM

This example illustrates an initial population of “chromosomes,” each representing a different solution. The genetic algorithm uses an iterative
process to refine the initial solutions so that the better ones, those with the higher fitness, are more likely to emerge as the best solution.

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