Genetic algorithms with shrinking population size

A Genetic Algorithm (GA) is an evolutionary computation technique inspired by the principle of biological evolution via natural selection. It employs the fundamental components of evolution, such as selection, mating, and mutation, which continue from generation to generation, creating better solutions as time progresses. Although it is mostly used as an optimization tool, GA enjoys a wide spectrum of applications in diverse fields such as engineering, medicine, and ecology, among others. In this study, we propose three different population size reduction methods for a typical GA optimization, aiming to increase efficiency. Additionally, we compare the accuracy and precision of these methods using Monte Carlo simulations.     

Adsorption and diffusion of argon in disordered nanoporous carbons

Application-specific optimization of disordered nanoporous carbons remains a formidable challenge due to the difficulty in accurately characterizing their microstructures with current empirical methods. Using molecular simulation techniques, we investigated the adsorptive and diffusive behavior of argon in three models of disordered nanoporous carbons. We found that the structural and morphological differences between these models gave rise to distinct phenomenological properties. The adsorptive behavior of argon in both the low and high pressure regimes was enhanced dramatically in the models with more crystalline microstructures. As for dynamic properties, we found that the adsorbent’s structure and energetic topology significantly alters the rates of diffusion as well as the characteristics of the underlying diffusion mechanisms.     

Using an iterative linear solver in an interior-point method for generating support vector machines

This paper concerns the generation of support vector machine classifiers for solving the pattern recognition problem in machine learning. A method is proposed based on interior-point methods for convex quadratic programming. This interior-point method uses a linear preconditioned conjugate gradient method with a novel preconditioner to compute each iteration from the previous. An implementation is developed by adapting the object-oriented package OOQP to the problem structure. Numerical results are provided, and computational experience is discussed.     

A recyclable chiral Ru catalyst for enantioselective olefin metathesis. Efficient catalytic asymmetric ring-opening/cross metathesis in air

The synthesis and structure of a new chiral bidentate imidazolinylidene ligand and a derived chiral Ru-based carbene are disclosed. The Ru complex is stereogenic at the metal center; it can be prepared in >98% diastereoselectivity and purified by silica gel chromatography with undistilled solvents. The air-stable Ru complex efficiently catalyzes ring-closing and ring-opening metathesis and is recyclable. The chiral complex is highly effective (0.5-10 mol % loading) in promoting enantioselective ring-opening/cross metathesis reactions (up to >98% ee). These enantioselective transformations can be effected in air, with unpurified solvent and with substrates that would only polymerize with Mo-based catalysts.