Chen Y., Abraham A. Tree-Structure Based Hybrid Computational Intelligence. Theoretical Foundations and Applications

Springer, 2010, -211 p.Computational intelligence is a well-established paradigm, where new theories with a sound biological understanding have been evolving. The current experimental systems have many of the characteristics of biological computers (brains in other words) and are beginning to be built to perform a variety of tasks that are difficult or impossible to do with conventional computers. In a nutshell, which becomes quite apparent in the light of the current research pursuits, the area is heterogeneous as being dwelled on such technologies as neurocomputing, fuzzy inference systems, artificial life, probabilistic reasoning, evolutionary computation, swarm intelligence and intelligent agents and so on.
Research in computational intelligence is directed toward building thinking machines and improving our understanding of intelligence. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. Even though we are a long way from achieving this, some success has been achieved in mimicking specific areas of human mental activity.
Recent research in computational intelligence together with other branches of engineering and computer science has resulted in the development of several useful intelligent paradigms. The integration of different learning and adaptation techniques, to overcome individual limitations and achieve synergetic effects through hybridization or fusion of some of these techniques, has in recent years contributed to a large number of new hybrid intelligent system designs.
Learning methods and approximation algorithms are fundamental tools that deal with computationally hard problems, in which the input is gradually disclosed over time. Both kinds of problems have a large number of applications arising from a variety of fields, such as function approximation and classification, algorithmic game theory, coloring and partitioning, geometric problems, mechanism design, network design, scheduling, packing and covering and real-world applications such as medicine, computational finance, and so on.
In this book, we illustrate Hybrid Computational Intelligence (HCI) framework and it applications for various problem solving tasks. Based on tree-structure based encoding and the specific function operators, the models can be flexibly constructed and evolved by using simple computational intelligence techniques. The main idea behind this model is the flexible neural tree, which is very adaptive, accurate and efficient. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. The flexible neural tree could be evolved by using tree-structure based evolutionary algorithms with specific instructions. The fine tuning of the parameters encoded in the structure could be accomplished by using parameter optimization algorithms. The flexible neural tree method interleaves both optimizations. Starting with random structures and corresponding parameters, it first tries to improve the structure and then as soon as an improved structure is found, it fine tunes its parameters. It then goes back to improving the structure again and, provided it finds a better structure, it again fine tunes the rules’ parameters. This loop continues until a satisfactory solution is found or a time limit is reached.Foundations of Computational Intelligence.
Flexible Neural Tree: Foundations and Applications.
Hierarchical Neural Networks.
Hierarchical Fuzzy Systems.
Reverse Engineering of Dynamic Systems.
Concluding Remarks and Further Research.