The goal of minimal attribute reduction is to find the minimal subset R of the condition attribute set C such that R has the same classification quality as C. This problem is well known to be NP-hard. When only one minimal attribute reduction is required, it was transformed into a nonlinearly constrained combinatorial optimization problem over a Boolean space and some heuristic search approache...

in this paper, we present a genetic algorithm (ga) for optimization of a multi-mode resource constrained time cost trade off (mrctct) problem. the proposed ga, each activity has several operational modes and each mode identifies a possible executive time and cost of the activity. beyond earlier studies on time-cost trade-off problem, in mrctct problem, resource requirements of each execution mo...

Because the existing attribute reduction algorithms based on rough set theory and genetic algorithm have the main problems: the complexity in calculating fitness function and slow speed in convergence. An attribute reduction algorithm based on rough set theory and an improved genetic algorithm is proposed in this paper. In order to simplify the calculation of fitness function under the conditio...

A reduct is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of a given information table. A general definition of an attribute reduct is presented. Specifically, we discuss the following issues: First, there are a variety of properties that can be observed in an information table. Second, the preservation of a certain property b...

Attribute reduction is one of the most important topics in rough set theory. Heuristic attribute reduction algorithms have been presented to solve the attribute reduction problem. It is generally known that fitness functions play a key role in developing heuristic attribute reduction algorithms. The monotonicity of fitness functions can guarantee the validity of heuristic attribute reduction al...

Using evolutionary algorithms, a search is performed based on a population where each population member consists of a vector of attribute values and a fitness value. A simulation of a system is run, given a particular set of the member attribute values, producing a fitness value. Fitness measures how well the system achieves its mission objectives. If the fitness has a random component, several...

When primitive data representation yields attribute interactions, learning requires feature construction. MFE2/GA, a GA-based feature construction has been shown to learn more accurately than others when there exist several complex attribute interactions. A new fitness function, based on the principle of Minimum Description Length (MDL), is proposed and implemented as part of the MFE3/GA system...

We propose the use of evolutionary algorithms (EAs) (Holland, 1992) to deal with the attribute selection task of referring expression generation. Evolutionary algorithms operate over a population of individuals (possible solutions for a problem) that evolve according to selection rules and genetic operators. The fitness function is a metric that evaluates each of the possible solutions, ensurin...

in this paper, a new alternative method for order reduction of high order systems is presented based on optimization of multi objective fitness function by using harmony search algorithm. at first, step response of full order system is obtained as a vector, then, a suitable fixed structure considered for model order reduction which order of original system is bigger than fixed structure model. ...