We show that there are unimodal fitness functions and genetic algorithm (GA) parameter settings where the GA, when initialized with a random population, will not move close to the fitness peak in a practically useful time period. When the GA is initialized with a population close to the fitness peak, the GA will be able to stay close to the fitness peak. Roughly speaking, the parameter settings involve strong recombination, weak selection, and require mutation. This "bistability" phenomenon has been previously investigated with needle-in-the-haystack fitness functions, but this fitness, when used with a GA with random initialization, requires a population size exponential in the string length for the GA to have nontrivial behavior. We introduce sloping-plateau fitness functions which show the bistability phenomenon and should scale to arbitrary string lengths. We introduce and use an unitation infinite population model to investigate the bistability phenomenon. For the fitnesses and GAs considered in the paper, we show that the use of crossover moves the GA to its fixed point faster in comparison to the same GA without crossover.

Adaptive web sites have been proposed to enhance ease of navigation and information retrieval. A variety of approaches are described in the literature, but consideration of interface presentation issues and realistic user studies are generally lacking. We report here a large-scale study of sites with dynamic information collections and user interests, where adaptation is based on an Ant Colony Optimization technique. We find that most users were able to locate information effectively without needing to perform explicit searches. The behavior of users who did search was similar to that on Internet search engines. Simulations based on site and user models give insight into the adaptive behavior and correspond to observations.

Using a dynamic systems model for the Simple Genetic Algorithm due to Vose[1], we analyze the fixed point behavior of the model without crossover applied to functions of unitation. Unitation functions are simplified fitness functions that reduce the search space into a smaller number of equivalence classes. This reduction allows easier computation of fixed points. We also create a dynamic systems model from a simple nondecreasing EA like the (1+1) EA and variants, then analyze this models on unitation classes.

RightNow Web is an integrated software package for web-based customer service that has, at its core, a database of answers to frequently asked questions (FAQs). One major design goal is to facilitate end-user interaction with this dynamic document collection, i.e. make it as easy and efficient as possible for users to browse the collection and locate desired information. To this end, we perform several types of analysis on the session tracking database that records user navigation histories. First, using both explicit and implicit measures of user satisfaction, we infer a "solved count" representing the average utility of an FAQ. Second, using the user navigation patterns we construct a link matrix representing connections between FAQs. The technique of building up the link matrix and using it to advise users on related information amounts to a form of the "swarm intelligence" method of finding optimal paths. Both solved count and the link matrix are continuously updated as users interact with the site; furthermore, they are periodically "aged" to emphasize recent activity. The synergistic combination of these techniques allows users to learn from the database in a more effective manner, as evidenced by usage statistics.