In this paper, we investigate the use of a self-adaptive Pareto evolutionary multi-objective optimization (EMO) approach for evolving the controllers of virtual embodied organisms. The objective of this paper is to demonstrate the trade-off between quality of solutions and computational cost. We show empirically that evolving controllers using the proposed algorithm incurs significantly less computational cost when compared to a self-adaptive weighted sum EMO algorithm, a self-adaptive single-objective evolutionary algorithm (EA) and a hand-tuned Pareto EMO algorithm. The main contribution of the self-adaptive Pareto EMO approach is its ability to produce sufficiently good controllers with different locomotion capabilities in a single run, thereby reducing the evolutionary computational cost and allowing the designer to explore the space of good solutions simultaneously. Our results also show that self-adaptation was found to be highly beneficial in reducing redundancy when compared against the other algorithms. Moreover, it was also shown that genetic diversity was being maintained naturally by virtue of the system's inherent multi-objectivity.
Statistical classification remains the most useful statistical tool for forensic chemists to assess the relationships between samples. Many clustering techniques such as principal component analysis and hierarchical cluster analysis have been employed to analyze chemical data for pattern recognition. Due to the feeble foundation of this statistics knowledge among novice drug chemists, a tetrahedron method was designed to simulate how advanced chemometrics operates. In this paper, the development of the graphical tetrahedron and computational matrices derived from the possible tetrahedrons are discussed. The tetrahedron method was applied to four selected parameters obtained from nine illicit heroin samples. Pattern analysis and mathematical computation of the differences in areas for assessing the dissimilarity between the nine tetrahedrons were found to be user-convenient and straightforward for novice cluster analysts.
We have calculated the vibrational frequencies of clusters of atoms from the first principles by using the density-functional theory in the local density approximation (LDA). We are also able to calculate the electronic binding energy for all of the clusters of atoms from the optimized structure. We have made clusters of BanOm (n, m=1-6) and have determined the bond lengths, vibrational frequencies as well as intensities in each case. We find that the peroxide cluster BaO2 occurs with the O-O vibrational frequency of 836.3 cm(-1). We also find that a glass network occurs in the material which explains the vibrational frequency of 67 cm(-1). The calculated values agree with those measured from the Raman spectra of barium peroxide and Ba-B-oxide glass. We have calculated the vibrational frequencies of BaO4, GeO4 and SiO4 each in tetrahedral configuration and find that the vibrational frequencies in these systems depend on the inverse square root of the atomic mass.