1. Introduction. - 1. 1. Satisfying hidden strong constraints. - 1. 2. Function optimization. - 1. 3. High-dimensional binary vector spaces. - 1. 4. Dissertation overview. - 1. 5. Summary. - 2. The model. - 2. 1. Design goal: Learning while searching. - 2. 2. Design goal: Sustained exploration. - 2. 3. Connectionist computation. - 2. 4. Stochastic iterated genetic hillclimbing. - 2. 5. Summary. - 3. Empirical demonstrations. - 3. 1. Methodology. - 3. 2. Seven algorithms. - 3. 3. Six functions. - 4. Analytic properties. - 4. 1. Problem definition. - 4. 2. Energy functions. - 4. 3. Basic properties of the learning algorithm. - 4. 4. Convergence. - 4. 5. Divergence. - 5. Graph partitioning. - 5. 1. Methodology. - 5. 2. Adding a linear component. - 5. 3. Experiments on random graphs. - 5. 4. Experiments on multilevel graphs. - 6. Related work. - 6. 1. The problem space formulation. - 6. 2. Search and learning. - 6. 3. Connectionist modelling. - 7. Limitations and variations. - 7. 1. Current limitations. - 7. 2. Possible variations. - 8. Discussion and conclusions. - 8. 1. Stability and change. - 8. 2. Architectural goals. - 8. 3. Discussion. - 8. 4. Conclusions. - References. Language: English