Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

A similarity-based neighbourhood search for enhancing the balance exploration–exploitation of differential evolution

The success of search-based optimisation algorithms depends on appropriately balancing exploration and exploitation mechanisms during the course of the search. We introduce a mechanism that can be used with Differential Evolution (de) algorithms to adaptively manage the balance between the diversification and intensification phases, depending on current progress. The method—Similarity-based Neighbourhood Search (sns)—uses information derived from measuring Euclidean distances among solutions in the decision space to adaptively influence the choice of neighbours to be used in creating a new solution. sns is integrated into explorative and exploitative variants of jade, one of the most frequently used adaptive de approaches. Furthermore, shade, which is another state-of-the-art adaptive de variant, is also considered to assess the performance of the novel sns. A thorough experimental evaluation is conducted using a well-known set of large-scale continuous problems, revealing that incorporating sns allows the performance of both explorative and exploitative variants of de to be significantly improved for a wide range of the test-cases considered. The method is also shown to outperform variants of de that are hybridised with a recently proposed global search procedure, designed to speed up the convergence of that algorithm

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Genome sequences reveal global dispersal routes and suggest convergent genetic adaptations in seahorse evolution

Seahorses have a circum-global distribution in tropical to temperate coastal waters. Yet, seahorses show many adaptations for a sedentary, cryptic lifestyle: they require specific habitats, such as seagrass, kelp or coral reefs, lack pelvic and caudal fins, and give birth to directly developed offspring without pronounced pelagic larval stage, rendering long-range dispersal by conventional means inefficient. Here we investigate seahorses’ worldwide dispersal and biogeographic patterns based on a de novo genome assembly of Hippocampus erectus as well as 358 re-sequenced genomes from 21 species. Seahorses evolved in the late Oligocene and subsequent circum-global colonization routes are identified and linked to changing dynamics in ocean currents and paleo-temporal seaway openings. Furthermore, the genetic basis of the recurring “bony spines” adaptive phenotype is linked to independent substitutions in a key developmental gene. Analyses thus suggest that rafting via ocean currents compensates for poor dispersal and rapid adaptation facilitates colonizing new habitats.Fil: Chunyan, Li. Southern Marine Science and Engineering Guangdong Laboratory; China. Pilot National Laboratory for Marine Science and Technology; China. Chinese Academy of Sciences; República de ChinaFil: Olave, Melisa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; Argentina. University of Konstanz; AlemaniaFil: Hou, Yali. Chinese Academy of Sciences; República de ChinaFil: Geng, Qi. Chinese Academy of Sciences; República de China. Southern Marine Science and Engineering Guangdong Laboratory; ChinaFil: Schneider, Ralf. University Of Konstanz; Alemania. Helmholtz Centre for Ocean Research Kie; AlemaniaFil: Zeixa, Gao. Huazhong Agricultural University; ChinaFil: Xiaolong, Tu. Allwegene Technologies ; ChinaFil: Xin, Wang. Chinese Academy of Sciences; República de ChinaFil: Furong, Qi. China National Center for Bioinformation; China. University of Chinese Academy of Sciences; ChinaFil: Nater, Alexander. University of Konstanz; AlemaniaFil: Kautt, Andreas F.. University of Konstanz; Alemania. Harvard University; Estados UnidosFil: Wan, Shiming. Chinese Academy of Sciences; República de ChinaFil: Yanhong, Zhang. Chinese Academy of Sciences; República de ChinaFil: Yali, Liu. Chinese Academy of Sciences; República de ChinaFil: Huixian, Zhang. Chinese Academy of Sciences; República de ChinaFil: Bo, Zhang. Chinese Academy of Sciences; República de ChinaFil: Hao, Zhang. Chinese Academy of Sciences; República de ChinaFil: Meng, Qu ,. Chinese Academy of Sciences; República de ChinaFil: Shuaishuai, Liu. Chinese Academy of Sciences; República de ChinaFil: Zeyu, Chen. Chinese Academy of Sciences; República de China. University of Chinese Academy of Sciences; ChinaFil: Zhong, Jia. Chinese Academy of Sciences; República de ChinaFil: Zhang, He. BGI-Shenzhen; ChinaFil: Meng, Lingfeng. BGI-Shenzhen; ChinaFil: Wang, Kai. Ludong University; ChinaFil: Yin, Jianping. Chinese Academy of Sciences; República de ChinaFil: Huang, Liangmin. Chinese Academy of Sciences; República de China. University of Chinese Academy of Sciences; ChinaFil: Venkatesh, Byrappa. Institute of Molecular and Cell Biology; SingapurFil: Meyer, Axel. University of Konstanz; AlemaniaFil: Lu, Xuemei. Chinese Academy of Sciences; República de ChinaFil: Lin, Qiang. Chinese Academy of Sciences; República de China. Southern Marine Science and Engineering Guangdong Laboratory; China. Pilot National Laboratory for Marine Science and Technology; China. University of Chinese Academy of Sciences; Chin

Evolutionary Algorithms and Computational Methods for Derivatives Pricing

This work aims to provide novel computational solutions to the problem of derivative pricing. To achieve this, a novel hybrid evolutionary algorithm (EA) based on particle swarm optimisation (PSO) and differential evolution (DE) is introduced and applied, along with various other state-of-the-art variants of PSO and DE, to the problem of calibrating the Heston stochastic volatility model. It is found that state-of-the-art DEs provide excellent calibration performance, and that previous use of rudimentary DEs in the literature undervalued the use of these methods. The use of neural networks with EAs for approximating the solution to derivatives pricing models is next investigated. A set of neural networks are trained from Monte Carlo (MC) simulation data to approximate the closed form solution for European, Asian and American style options. The results are comparable to MC pricing, but with offline evaluation of the price using the neural networks being orders of magnitudes faster and computationally more efficient. Finally, the use of custom hardware for numerical pricing of derivatives is introduced. The solver presented here provides an energy efficient data-flow implementation for pricing derivatives, which has the potential to be incorporated into larger high-speed/low energy trading systems

Network Theoretic Analyses and Enhancements of Evolutionary Algorithms

Information in evolutionary algorithms is available at multiple levels; however most analyses focus on the individual level. This dissertation extracts useful information from networks and communities formed by examining interrelationships between individuals in the populations as they change with time. Network theoretic analyses are extremely useful in multiple fields and applications, e.g., biology (regulation of gene expression), organizational behavior (social networks), and intelligence data analysis (message traffic on the Internet). Evolving populations are represented as dynamic networks, and we show that changes in population characteristics can be recognized at the level of the networks representing successive generations, with implications for possible improvements in the evolutionary algorithm, e.g., in deciding when a population is prematurely converging, and when a reinitialization of the population may be beneficial to avoid computational effort, or to improve the probability of finding better points to examine. In this dissertation, we show that network theoretic analyses can be applied to study, analyze and improve the performance of evolutionary algorithms. We propose various approaches to study the dynamic behavior of evolutionary algorithms, each highlighting the benefits of studying community-level behaviors, using graph properties and metrics to analyze evolutionary algorithms, identifying imminent convergence, and identifying time points at which it would help to reseed a fraction of the population. Improvements to evolutionary algorithms result in alleviating the effects of premature convergence occurrences, and saving computational effort by reaching better solutions faster. We demonstrate that this new approach, using network science to analyze evolutionary algorithms, is advantageous for a variety of evolutionary algorithms, including Genetic Algorithms, Particle Swarm Optimization, and Learning Classifier Systems

探索ダイナミクス分析とその進化的アルゴリズムヘの応用

富山大学・富理工博甲第181号・李小司・2020/9/28富山大学202