Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution

It is not uncommon that meta-heuristic algorithms contain some intrinsic parameters, the optimal configuration of which is crucial for achieving their peak performance. However, evaluating the effectiveness of a configuration is expensive, as it involves many costly runs of the target algorithm. Perhaps surprisingly, it is possible to build a cheap-to-evaluate surrogate that models the algorithm's empirical performance as a function of its parameters. Such surrogates constitute an important building block for understanding algorithm performance, algorithm portfolio/selection, and the automatic algorithm configuration. In principle, many off-the-shelf machine learning techniques can be used to build surrogates. In this paper, we take the differential evolution (DE) as the baseline algorithm for proof-of-concept study. Regression models are trained to model the DE's empirical performance given a parameter configuration. In particular, we evaluate and compare four popular regression algorithms both in terms of how well they predict the empirical performance with respect to a particular parameter configuration, and also how well they approximate the parameter versus the empirical performance landscapes

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

Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)

Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i.e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into real and fake samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm

Got Breadfruit? Marshallese Foodways and Culture in Springdale, Arkansas

Understanding human food choices is essential in the examination of cultural knowledge and decision-making among members of any ethnic group. Ethnographic and cognitive anthropology methods, including a novel calculation of cognitive salience, were used in this study to explore the domain of traditional Marshallese foods in Springdale, Arkansas. Springdale is home to the highest population of Marshallese people outside of the Republic of the Marshall Islands (RMI); the population is expected to rise as people continue to migrate from the RMI because of global climate change and other factors such as family ties. Studies of traditional foodways are increasingly crucial in social science because they offer a relevant lens for examining beliefs, behaviors, and other biocultural elements binding people together. This study is the first to examine traditional Marshallese foods in the diasporic context. It is also significant from health and nutritional perspectives because Marshallese people are at high risk for diet related diseases, such as type II diabetes. Breadfruit, long a standard starchy staple of Marshallese cuisine, was discovered to be the most important and socially shared traditional Marshallese food. Although breadfruit is gaining popularity in Western markets as a healthy superfood on par with kale and açaí, it is not yet readily available for purchase in Springdale. The practice of substituting higher-Glycemic Index (GI) white rice for lower-GI breadfruit began in the RMI during the 1930s and has carried over to the Springdale community today, where 46.5% of Marshallese adults have type II diabetes (a disease associated with higher dietary GI). The fact that breadfruit has such high cultural value and salience, despite infrequent consumption, represents Marshallese concepts of dietary change and constancy. Ultimately, the results of this work serve to illustrate how human diasporic groups adapt and respond to dramatic socio-ecological changes and challenges through culturally-constructed food beliefs, preferences, and consumption patterns

Rethinking paradigms for studying mechanisms of action of plant bioactives

Many foods in our diets such as berries, tea, chocolate and wine contain flavonoids, which are natural components of plants. A substantial body of evidence supports the role of flavonoids in providing protection against cardio-metabolic diseases and disorders. Despite the nearly exponential growth in flavonoid research in the past 20 years, limited progress has been made in understanding how these dietary components work. Research initially focused on their antioxidant activity without taking into account their metabolism, which now appears extensive. This has provided a new research impetus to understand the biological activity of the flavonoid metabolites. Here, we outline recent research, which suggests a highly complex interplay between metabolism, intestinal microflora, the immune system and various tissues of our body