Climbing the Steiner Tree--Sources of Active Information in a Genetic Algorithm for Solving the Euclidean Steiner Tree Problem

Abstract

Genetic algorithms are widely cited as demonstrating the power of natural selection to produce biological complexity. In particular, the success of such search algorithms is said to show that intelligent design has no scientific value. Despite their merits, genetic algorithms establish nothing of the sort. Such algorithms succeed not through any intrinsic prop- erty of the search algorithm, but rather through incorporating sources of information derived from the programmer’s prior knowledge. A genetic algorithm used to defend the efficacy of natural selection is Thomas’s Steiner tree algorithm. This paper tracks the various sources of information incorporated into Thomas’s algorithm. Rather than creating informa- tion from scratch, the algorithm incorporates resident information by restricting the set of solutions considered, introducing selection skew to increase the power of selection, and adopting a structure that facilitates fortuitous crossover. Thomas’s algorithm, far from exhibiting the power of natural selection, merely demonstrates that an intelligent agent, in this case a human programmer, possesses the ability to incorporate into such algorithms the information necessary for successful search.