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Evolutionary Optimization for Intelligent Systems Design J . UCS Special Issue

Evolutionary Optimization is becoming omnipresent technique in almost every process of intelligent system design. Just to name few, engineering, control, economics and forecasting are some of the scientific fields that take advantage of an evolutionary computational process that aid in engineering systems with intelligent behavior. This special issue of Journal of Universal Computer Science is devoted to reporting innovative and significant progress in intelligent systems design through an evolutionary computational process. It includes eight contributed papers, whose main contributions are described in the sequel. In the first contribution, which is entitled " Automatic Construction of Fuzzy Rule Bases: a further Investigation into two Alternative Inductive Approaches " , Marcos Nicoletti discuss the results of two different hybrid methods for the automatic generation of fuzzy rules from numerical data. One of the methods proposes the creation of fuzzy rule bases using genetic algorithms in association with a heuristic for preselecting candidate rules based on the degree of coverage. The other method induces a Bayesian classifier using a dataset previously granulated by fuzzy partitions and then translates it into a fuzzy rule base. A comparative analysis between both approaches focusing on their main characteristics, strengths/weaknesses and easiness of use is carried out. The reliability of both methods is also compared by analyzing their results in a few knowledge domains. In the second contribution, which is entitled " Parallel Strategies for Stochastic propose the discussion of parallelization of Stochastic Evolution meta-heuristic, for a distributed parallel environment. VLSI cell placement is used as an optimization problem. A