Genetic Algorithms and Swarm Intelligence
Like memory-based reasoning and neural networks, genetic algorithms and swarm intelligence are problem-solving techniques inspired by biological processes. Evolution and natural selection have, over the course of millions of years, resulted in adaptable, specialized species that are highly suited to their environments. Evolution optimizes the fitness of individuals over succeeding generations by propagating the genetic material in the fittest individuals of one generation to the next generation. Among the most successful products of natural evolution are social insects such as ants, bees, and termites. These small, seemingly simple creatures cooperate to solve complex problems, such as finding the most efficient route to a source of food, that seem well beyond the ability of the individual members of the hive or colony. This chapter looks at how to apply insights gained from the study of evolution and social insects to data mining.
Most of the techniques described in this book are widely used in the business world and are easily available as part of commercial data mining packages. The inclusion of the techniques described in this chapter cannot really be justified on those grounds, but the authors feel they are too exciting to ignore, even though it means using some examples that are a bit more academic than the case studies used to illustrate other techniques.
The chapter begins with a brief look at a variety of problem-solving ...