In recent years, soft computing methods have generated a large research interest. The synthesis of the fuzzy logic and the evolutionary algorithms is one of these methods. A particular evolutionary algorithm (EA) is differential evolution (DE). As for any EA, DE algorithm also requires parameters tuning to achieve desirable performance. In this paper tuning the perturbation factor vector of DE ...

This paper presents an investigation into the comparative performance of intelligent system identification and control algorithms within the framework of an active vibration control (AVC) system. Evolutionary Genetic algorithms (GAs) and Adaptive Neuro-Fuzzy Inference system (ANFIS) algorithms are used to develop mechanisms of an AVC system, where the controller is designed on the basis of opti...

Protein comparative modelling (CM) is a predictive technique to build an atomic model for a polypeptide chain, based on the experimentally determined structures of related proteins (templates). It is widely used in Structural Biology, with applications ranging from mutation analysis, protein and drug design to function prediction and analysis, particularly when there are no experimental structu...

Interactive Evolutionary Algorithms (IEAs) are a powerful explorative search technique that utilizes human input to make subjective decisions on potential problem solutions. But humans are much slower than computers and get bored and tired easily, limiting the usefulness of IEAs. Here two variations of a user-modeling approach are compared to determine if this approach can accelerate IEA search...

Protein comparative modelling (CM) is a predictive technique to build an atomic model for a polypeptide chain, based on the experimentally determined structures of related pro­ teins (templates). It is widely used in Structural Biology, with applications ranging from mutation analysis, protein and drug design to function prediction and analysis, particu­ larly when there are no experimental str...

The paper presents a review of current evolutionary algorithms for feature ranking in data mining tasks involving automated learning. This issue is highly important as real-world problems commonly suffer from the curse of dimensionality. By weighting the significance of each attribute from a data set, the less influential indicators can be disposed of before learning actually takes place, makin...

A key part of any evolutionary algorithm is fitness evaluation. When fitness evaluations are corrupted by noise, as happens in many real-world problems as a consequence of various types of uncertainty, a strategy is needed in order to cope with this. Resampling is one of the most common strategies, whereby each solution is evaluated many times in order to reduce the variance of the fitness esti...

As multiobjective optimization problems have many solutions, evolutionary algorithms have been widely used for complex multiobjective problems instead of simulated annealing. However, simulated annealing also has favorable characteristics in the multimodal search. We developed several simulated annealing schemes for the multiobjective optimization based on this fact. Simulated annealing and evo...