Finding High-Dimensional D-OptimalDesigns for Logistic Models via Differential Evolution

D-optimal designs are frequently used in controlled experiments to obtain the most accurateestimate of model parameters at minimal cost. Finding them can be a challenging task, especially whenthere are many factors in a nonlinear model. As the number of factors becomes large and interact withone another, there are many more variables to optimize and the D-optimal design problem becomes highdimensionaland non-separable. Consequently, premature convergence issues arise. Candidate solutions gettrapped in local optima and the classical gradient-based optimization approaches to search for the D-optimaldesigns rarely succeed. We propose a specially designed version of differential evolution (DE) which is arepresentative gradient-free optimization approach to solve such high-dimensional optimization problems.The proposed specially designed DE uses a new novelty-based mutation strategy to explore the variousregions in the search space. The exploration of the regions will be carried out differently from the previouslyexplored regions and the diversity of the population can be preserved. The proposed novelty-based mutationstrategy is collaborated with two common DE mutation strategies to balance exploration and exploitationat the early or medium stage of the evolution. Additionally, we adapt the control parameters of DE as theevolution proceeds. Using logistic models with several factors on various design spaces as examples, oursimulation results show our algorithm can find D-optimal designs efficiently and the algorithm outperformsits competitors. As an application, we apply our algorithm and re-design a 10-factor car refueling experimentwith discrete and continuous factors and selected pairwise interactions. Our proposed algorithm was able toconsistently outperform the other algorithms and find a more efficient D-optimal design for the problem

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

DIFFERENTIAL EVOLUTION-BASED METHODS FOR NUMERICAL OPTIMIZATION

Ph.DDOCTOR OF PHILOSOPH

2013 Conference Abstracts: Annual Undergraduate Research Conference at the Interface of Biology and Mathematics

URC Schedule and Abstract Book for the Fifth Annual Undergraduate Research Conference at the Interface of Biology and Mathematics Date: November 16-17, 2013Plenary Speaker: Mariel Vazquez, Associate Professor of Mathematics at San Francisco State UniversityFeatured Speaker: Andrew Liebhold, Research Entomologist for the USDA Forest Servic

Sexual Recombination and the Development of Complex Phenotypes

Adaptive laboratory evolution facilitates the development and study of complex phenotypes. In an evolving population, individuals with mutations conveying a fitness benefit are selected for, and become enriched, in the environment. However, the rate of adaptation can be limited by the frequency of beneficial mutations; and competition amongst co-occurring beneficial mutations can lead to a loss of information. In this work, we describe the use of horizontal gene transfer (HGT) in conjunction with modulating mutation rate to more rapidly develop complex phenotypes in E. coli. We first characterize a previously developed “genderless” strain of E. coli proficient in continuous HGT during liquid culture. We next examine a few steps that can be taken to broaden and enhance the characteristics of this strain. We then introduced an inducible mutator system to the genderless strain in order allow modulation of mutation rate to enhance the supply of mutations during ALE. The strain was evolved in several well-characterized experimental environments to determine the influences of HGT and mutation rate on the rate of adaptation. The results indicate HGT and increasing mutation rate can act together to speed adaptive laboratory evolution, in many adaptive landscapes (environment). We then leveraged the HGT to more rapidly combine different complex phenotypes, to help expedite strain development of more industrially focused phenotypes. Finally, less developed works, which focus on applying different aspects of ALE toward strain development, are briefly discussed

hi_class: Horndeski in the Cosmic Linear Anisotropy Solving System

We present the public version of hi_class (www.hiclass-code.net), an extension of the Boltzmann code CLASS to a broad ensemble of modifications to general relativity. In particular, hi_class can calculate predictions for models based on Horndeski's theory, which is the most general scalar-tensor theory described by second-order equations of motion and encompasses any perfect-fluid dark energy, quintessence, Brans-Dicke, $f(R)$ and covariant Galileon models. hi_class has been thoroughly tested and can be readily used to understand the impact of alternative theories of gravity on linear structure formation as well as for cosmological parameter extraction.Comment: 17 pages + appendices, 4 figures, code available on https://github.com/miguelzuma/hi_class_publi

Feature selection using enhanced particle swarm optimisation for classification models.

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets