We empirically investigate the use of dual duplication/truncation operators both as mutation operators and as generic local search operators, in combination with genetic search, in a tree adjoining grammar guided genetic programming system (TAG3P). The results show that, on the problems tried, duplication/truncation works well as mutation operator but not reliably when the complexity of the pro...

In this paper we present the outcome of two recent sets of experiments to evaluate the effectiveness of a new adjunct genetic operator GeneRepair. This operator was developed to correct invlaid tours which may be generated following crossover or mutation of our particular implementation of the genetic algorithm. Following implementation and testing of our genetic algotihm with GeneRepair we fou...

In present study, a three-step multi-objective optimization algorithm of cyclone separators is catered for the design objectives. First, the pressure drop (Dp) and collection efficiency (h) in a set of cyclone separators are numerically evaluated. Secondly, two meta models based on the evolved Group Method of Data Handling (GMDH) type neural networks are regarded to model the Dp and h as the re...

In order to improve the problem of low oxygen supply efficiency during aerobic composting and prolong maturity, a genetic algorithm was used optimize initial weights thresholds standard BP neural network obtain optimal parameters, then clonal selection mutation operator in duplicate operator. A CGA-BP based on an aeration prediction model constructed, predicted by ferment compost accelerate pro...

Mutation analysis measures test suite adequacy, the degree to which a test suite detects seeded faults (mutants): one test suite is better than another if it detects more mutants. The effectiveness of mutation analysis rests on the assumption that mutants are coupled with real faults—that is, mutant detection is strongly correlated with real fault detection. The work that validated this assumpt...

Clustering is a division of data into groups of similar objects. Each group, called cluster, consists of objects that are similar between themselves and dissimilar to objects of other groups. The clustering algorithm attempts to find natural groups of components, based on some similarity. Traditional clustering algorithms will search only a small sub-set of all possible clustering and consequen...