Feature-Based Diversity Optimization for Problem Instance Classification

Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Traveling Salesperson problem. In this paper, we present a general framework that is able to construct a diverse set of instances that are hard or easy for a given search heuristic. Such a diverse set is obtained by using an evolutionary algorithm for constructing hard or easy instances that are diverse with respect to different features of the underlying problem. Examining the constructed instance sets, we show that many combinations of two or three features give a good classification of the TSP instances in terms of whether they are hard to be solved by 2-OPT.Comment: 20 pages, 18 figure

Discrepancy-based Evolutionary Diversity Optimization

Diversity plays a crucial role in evolutionary computation. While diversity has been mainly used to prevent the population of an evolutionary algorithm from premature convergence, the use of evolutionary algorithms to obtain a diverse set of solutions has gained increasing attention in recent years. Diversity optimization in terms of features on the underlying problem allows to obtain a better understanding of possible solutions to the problem at hand and can be used for algorithm selection when dealing with combinatorial optimization problems such as the Traveling Salesperson Problem. We explore the use of the star-discrepancy measure to guide the diversity optimization process of an evolutionary algorithm. In our experimental investigations, we consider our discrepancy-based diversity optimization approaches for evolving diverse sets of images as well as instances of the Traveling Salesperson problem where a local search is not able to find near optimal solutions. Our experimental investigations comparing three diversity optimization approaches show that a discrepancy-based diversity optimization approach using a tie-breaking rule based on weighted differences to surrounding feature points provides the best results in terms of the star discrepancy measure

Diversity optimization and parameterized analysis of heuristic search methods for combinatorial optimization problems

Heuristic search algorithms belong to the most successful approaches for many combinatorial optimization problems which have wide real world applications in various areas. The heuristic algorithms usually provide solutions with acceptable quality in reasonable timeframe which is different from exact algorithms. Fixed-parameter approach provides a way for understanding how and why heuristic methods perform well for prominent combinatorial optimization problems. In this thesis, there are two main topics discussed. Firstly, we integrate the well-known branching approach for the classical combinatorial optimization problem, namely minimum vertex cover problem, to a local search algorithm and compare its performance with the core component of the state-of-the-art algorithm. After that, we investigate how well-performing local search algorithms for small or medium size instances can be scaled up to solve massive input instances. A parallel kernelization technique is proposed which is motivated by the assumption that huge graphs are composed of several easy to solve components while the overall problem is hard to solve. Using evolutionary algorithms to generate a diverse set of solutions where all of them meet certain quality criteria has gained increasing interests in recent years. As the second section, we put forward an evolutionary algorithm which allows us to maximize the diversity over a set of solutions with good quality and then focus on the theoretical analysis of the algorithm to provide understanding of how evolutionary algorithms maximize the diversity of a population and guarantee the quality of all solutions at the same time. Then the idea is extended to evolving hard/easy optimization problem instances with diverse feature values. The feature-based analysis of heuristic search algorithms plays an important role in understanding the behaviour of the algorithm and our results show good classification of the problem instances in terms of hardness based on different combinations of feature values.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2016

Species diversity of coral reef fishes around the West Island of Sanya City, South China Sea, based on environmental DNA

West Island is the second largest island in the Hainan Province, China and its surrounding sea area has a well-preserved coral reef ecosystem and high species diversity of coral reef fishes. Undoubtedly, coral reef fishes and coral reefs have complex symbiotic relationships and fish species diversity should reflect the healthy status of coral reef ecosystems. Environmental DNA (eDNA) is a useful and sensitive tool to detect fish species and causes less environmental damage than traditional fish survey methods. This paper investigated coral reef fish species of West Island, Hainan Province, China, based on eDNA and provided scientific data for understanding and protection of the coral reef ecosystem of the South China Sea.The sea area surrounding West Island is the most important component of the coral reef ecosystem in the northern part of the South China Sea, which is also an essential part of the largest National Coral Reef Reserve in China. However, complete data of fish species distributed in this region have been a long-term gap. This study provides information on 41 fish species belonging to 28 genera, 16 families and three orders in this sea area and is the first complete record of coral reef fishes surrounding the West Island. In addition, the information of Molecular Operational Taxonomic Units (MOTUs) for taxon identification were also provided and it could contribute to building specific eDNA taxonomy database of coral reef fishes of the South China Sea. The study includes three datasets, with aspects of fish taxon-occurrences, MOTUs sequences and information of environmental indicators surrounding West Island, Hainan Province, China. The “fish taxon occurrences” dataset presents records involving taxonomic, distribution, habitat condition, latitude and longitude of 41 coral reef fish species detected, based on eDNA, the “MOTUs sequences” dataset provides MOTUs sequences and their abundance of 31 species detected and the “information of environmental indicators” dataset presents records of transparency, temperature, water pressure, dissolved oxygen, electrical conductivity, hydrogen and redox potential measured from five sampling localities

Evolution of artistic image variants through feature based diversity optimisation

Measures aimed to improve the diversity of images and image features in evolutionary art help to direct search toward more novel and creative parts of the artistic search domain. To date such measures have not focused on selecting from all individuals based on their contribution to diversity of feature metrics. In recent work on TSP problem instance classification, selection based on a direct measure of each individual's contribution to diversity was successfully used to generate hard and easy TSP instances. In this work we use this search framework to evolve diverse variants of a source image in one and two feature dimensions. The resulting images show the spectrum of effects from transforming images to score across the range of each feature. The results also reveal interesting correlations between feature values in two dimensions.Brad Alexander, James Kortman, Aneta Neuman

An assessment of the interaction for three Chrysanthemum indicum flavonoids and α-amylase by surface plasmon resonance

This study evaluated the interaction of Chrysanthemum indicum (CI) flavonoids (luteolin, acacetin, and buddleoside) with α-amylase. Surface plasmon resonance (SPR) assay showed their equilibrium dissociation constants (KD) are 1.9695 ± 0.12, 2.9240 ± 0.20, and 3.2966 ± 0.08 mM at pH 6.0, respectively. Furthermore, their binding affinities were influenced by KCl, MgCl2, and CaCl2. Enzymatic kinetic studies revealed that three flavonoids exhibited noncompetitive α-amylase inhibitory activity. The inhibitory sequence is luteolin > acacetin > buddleoside, which was in accordance with the results of binding affinity from SPR. 1,1-diphenyl-2-picryl hydrazyl radical assay demonstrated that antioxidant activities of three flavonoids were inhibited significantly with α-amylase. Meanwhile, the study reveals that hydroxyl on C′-4, C′-5, and C-7 of flavonoids play an important role on the interaction of three flavonoids with α-amylase. Also, SPR could be used as sensor for rapid screening inhibitors of α-amylase and provide useful information for the application of C. indicum flavonoids in food and pharmaceutical area

Molecularly Engineered Macrophage‐Derived Exosomes with Inflammation Tropism and Intrinsic Heme Biosynthesis for Atherosclerosis Treatment

Atherosclerosis (AS) is a major contributor to cardiovascular diseases worldwide, and alleviating inflammation is a promising strategy for AS treatment. Here, we report molecularly engineered M2 macrophage-derived exosomes (M2 Exo) with inflammation-tropism and anti-inflammatory capabilities for AS imaging and therapy. M2 Exo are derived from M2 macrophages and further electroporated with FDA-approved hexyl 5-aminolevulinate hydrochloride (HAL). After systematic administration, the engineered M2 Exo exhibit excellent inflammation-tropism and anti-inflammation effects via the surface-bonded chemokine receptors and the anti-inflammatory cytokines released from the anti-inflammatory M2 macrophages. Moreover, the encapsulated HAL can undergo intrinsic biosynthesis and metabolism of heme to generate anti-inflammatory carbon monoxide and bilirubin, which further enhance the anti-inflammation effects and finally alleviate AS. Meanwhile, the intermediate protoporphyrin IX (PpIX) of the heme biosynthesis pathway permits the fluorescence imaging and tracking of AS.This work was supported by the National Natural Science Foundation of China (No. 91859123 and 21874011), the National Science and Technology Major Project (No. 2018ZX 10301405-001), and China Postdoctoral Science Foundation (No. 2018M630076)