We introduce the concept of fruitful regions in a dynamic routing context: regions that have a high potential of generating loads to be transported. The objective is to maximise the number of loads transported, while keeping to capacity and time constraints. Loads arrive while the problem is being solved, which makes it a real-time routing problem. The solver is a self-adaptive evolutionary algorithm that ensures feasible solutions at all times. We investigate under what conditions the exploration of fruitful regions improves the effectiveness of the evolutionary algorithm.

A limited number of hardcopies is available for those who are interested, drop me an e-mail. Contents (chapter level):

• 1. Introduction
• 2. Evolutionary Computation
• Part I: Constraint Satisfaction
• 3. Constraint Satisfaction problems
• 4. Solving Constraint Satisfaction Problems
• 5. Empirical Research on Constraint Satisfaction
• 6. Measuring the Resampling Ratio
• 7. Constraint Satisfaction: Conclusions
• Part II: Data Mining
• 8. Introduction
• 9. Classification
• 10. Symbolic Regression
• 11. Data Mining Conclusions
• 12. Dynamic Behaviour
• 13. Bridging the Gap
• A. RandomCSP Library
• B. Library for Evolutionary Algorithm Programming
• C. Case Study: Scheduling a Telescope

The optimization of dynamic environments has proved to be a difficult area for Evolutionary Algorithms. As standard haploid populations find it difficult to track a moving target, diffKerent schemes have been suggested to improve the situation. We study a novel approach by making use of a meta learner which tries to predict the next state of the environment, i.e. the next value of the goal the individuals have to achieve, by making use of the accumulated knowledge from past performance.

We present a general system that evolves art on the Internet. The system runs on a server which enables it to collect information about its usage world wide; its core uses operators and representations from genetic programming. The output consists of images that are decoded from tree structures. We show how this general system can be used to evolve two types of art: A Mondriaan like art and a type known as mandala. Both types are implemented with the mind of an engineer.

We describe an empirical investigation within an artificial world, aegis, where a population of animals and plants is evolving. We compare different system setups in search of an `ideal' world that allows a constantly high number of inhabitants for a long period of time. We observe that high responsiveness at individual level (speed of movement) or population level (high fertility) are `ideal'. Furthermore, we investigate the emergence of the so-called mental features of animals determining their social, consumptional and aggressive behaviour. The tests show that being socially oriented is generally advantageous, while agressive behaviour only emerges under specific circumstances.