Co-evolutionary techniques for evolutionary algorithms allow for the application of such algorithms to problems for which it is difficult or even impossible to formulate explicit fitness function. These techniques also maintain population diversity, allows for speciation and help overcoming limited adaptive capabilities of evolutionary algorithms. In this paper the idea of co-evolutionary multi...

Neutrosophic set (NS) is a generalization of fuzzy set (FS) that is designed for some practical situations in which each element has different truth membership function, indeterminacy membership function and falsity membership function. In this paper, we study the multi-attribute group decision making (MAGDM) problems under neutrosophic environment with the incompletely known or completely unkn...

Co-evolutionary techniques for evolutionary algorithms are aimed at overcoming their limited adaptive capabilities and allow for the application of such algorithms to problems for which it is difficult or even impossible to formulate explicit fitness function. In this paper the idea of co-evolutionary multi-agent system with host-parasite mechanism for multi-objective optimization is introduced...

For the assembly of multi micro objects in micromanipulation, the first task is to identify multi micro parts. We present an improved support vector machine algorithm, which employs invariant moments based edge extraction to obtain feature attribute and then presents a heuristic attribute reduction algorithm based on rough set’s discernibility matrix to obtain attribute reduction, with using su...

Abstract.With respect to interval-valued hesitant fuzzy multi-attribute decision making, this study first presents a new ranking method for interval-valued hesitant fuzzy elements. In order to obtain the comprehensive values of alternatives, two induced generalized interval-valued hesitant fuzzy hybrid operators based on the Shapley function are defined, which globally consider the importance o...

Real world datasets may be continuous. Many data analysis algorithms work efficiently on discrete data while some other algorithms work only on discrete data. Thus the continuous datasets are discretized as a pre-process step to knowledge acquisition. Attribute discretization is the process of reducing the domain of a continuous attribute with irreducible and optimal set of cuts, while preservi...

Evolutionary algorithms (EA) (Rechenberg, 1965) belong to a family of stochastic search algorithms inspired by natural evolution. In the last years, EA were used successfully to produce efficient solutions for a great number of hard optimization problems (Beasley, 1997). These algorithms operate on a population of potential solutions and apply a survival principle according to a fitness measure...