Optimization is an integral part to science and engineering. Most real-world applications involve complex optimization processes, which are difficult to solve without advanced computational tools. With the increasing challenges of fulfilling optimization goals of current applications there is a strong drive to advance the development of efficient optimizers. The challenges introduced by emerging problems include:
• objective functions which are prohibitively expensive to evaluate, so typically so only a small number of objective function evaluations can be made during the entire search,
• objective functions which are highly multimodal or discontinuous, and
• non-stationary problems which may change in time (dynamic).
This volume presents a collection of recent studies covering the spectrum of computational intelligence applications with emphasis on their application to challenging real-world problems. Topics covered include: Intelligent agent-based algorithms, Hybrid intelligent systems, Cognitive and evolutionary robotics, Knowledge-Based Engineering, fuzzy sets and systems, Bioinformatics and Bioengineering, Computational finance and Computational economics, Data mining, Machine learning, and Expert systems. "Computational Intelligence in Optimization" is a comprehensive reference for researchers, practitioners and advanced-level students interested in both the theory and practice of using computational intelligence in real-world applications.