Chemical Reaction Optimization: A tutorial

Chemical Reaction Optimization (CRO) is a recently established metaheuristics for optimization, inspired by the nature of chemical reactions. A chemical reaction is a natural process of transforming the unstable substances to the stable ones. In microscopic view, a chemical reaction starts with some unstable molecules with excessive energy. The molecules interact with each other through a sequence of elementary reactions. At the end, they are converted to those with minimum energy to support their existence. This property is embedded in CRO to solve optimization problems. CRO can be applied to tackle problems in both the discrete and continuous domains. We have successfully exploited CRO to solve a broad range of engineering problems, including the quadratic assignment problem, neural network training, multimodal continuous problems, etc. The simulation results demonstrate that CRO has superior performance when compared with other existing optimization algorithms. This tutorial aims to assist the readers in implementing CRO to solve their problems. It also serves as a technical overview of the current development of CRO and provides potential future research directions. © 2012 The Author(s).published_or_final_versionSpringer Open Choice, 25 May 201

WallStreetBets Beyond GameStop, YOLOs, and the Moon: The Unique Traits of Reddit’s Finance Communities

While the effect of established social media on stock markets has been thoroughly investigated, the recent surge in retail investing and the emergence of different finance-related Reddit communities with unique new traits have led to new research questions. In this work, we aim to understand the linguistic and thematic characteristics and differences of the largest financial Reddit communities, r/WallStreetBets, r/stocks, and r/investing. Using different techniques for the analysis of linguistic features and topic modeling, we identify keywords and phrases that are most prominent in each community and determine each community’s thematic focus and risk affinity. An analysis of users that post on all of these communities confirm these findings, as they appear to adapt to the respective target audience when posting. The stock returns for each community prove consistent with their respective risk profile. Overall, we conclude that understanding these communities can help investors in making more informed investment decisions

A Framework for Leveraging Artificial Intelligence in Project Management

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis dissertation aims to support the project manager in their daily tasks. As we use artificial intelligence (AI) and machine learning (ML) in everyday life, it is necessary to include them in business and change traditional ways of working. For the purpose of this study, it is essential to understand challenges and areas of project management and how artificial intelligence can contribute to them. A theoretical overview, applying the knowledge of project management, will show a holistic view of the current situation in the enterprises. The research is about artificial intelligence applications in project management, the common activities in project management, the biggest challenges, and how AI and ML can support it. Understanding project managers help create a framework that will contribute to optimizing their tasks. After designing and developing the framework for applying artificial intelligence to project management, the project managers were asked to evaluate. This study is essential to increase awareness among the stakeholders and enterprises on how automation of the processes can be improved and how AI and ML can decrease the possibility of risk and cost along with improving the happiness and efficiency of the employees

An Evolutionary Approach to Multistage Portfolio Optimization

Portfolio optimization is an important problem in quantitative finance due to its application in asset management and corporate financial decision making. This involves quantitatively selecting the optimal portfolio for an investor given their asset return distribution assumptions, investment objectives and constraints. Analytical portfolio optimization methods suffer from limitations in terms of the problem specification and modelling assumptions that can be used. Therefore, a heuristic approach is taken where Monte Carlo simulations generate the investment scenarios and' a problem specific evolutionary algorithm is used to find the optimal portfolio asset allocations. Asset allocation is known to be the most important determinant of a portfolio's investment performance and also affects its risk/return characteristics. The inclusion of equity options in an equity portfolio should enable an investor to improve their efficient frontier due to options having a nonlinear payoff. Therefore, a research area of significant importance to equity investors, in which little research has been carried out, is the optimal asset allocation in equity options for an equity investor. A purpose of my thesis is to carry out an original analysis of the impact of allowing the purchase of put options and/or sale of call options for an equity investor. An investigation is also carried out into the effect ofchanging the investor's risk measure on the optimal asset allocation. A dynamic investment strategy obtained through multistage portfolio optimization has the potential to result in a superior investment strategy to that obtained from a single period portfolio optimization. Therefore, a novel analysis of the degree of the benefits of a dynamic investment strategy for an equity portfolio is performed. In particular, the ability of a dynamic investment strategy to mimic the effects ofthe inclusion ofequity options in an equity portfolio is investigated. The portfolio optimization problem is solved using evolutionary algorithms, due to their ability incorporate methods from a wide range of heuristic algorithms. Initially, it is shown how the problem specific parts ofmy evolutionary algorithm have been designed to solve my original portfolio optimization problem. Due to developments in evolutionary algorithms and the variety of design structures possible, a purpose of my thesis is to investigate the suitability of alternative algorithm design structures. A comparison is made of the performance of two existing algorithms, firstly the single objective stepping stone island model, where each island represents a different risk aversion parameter, and secondly the multi-objective Non-Dominated Sorting Genetic Algorithm2. Innovative hybrids of these algorithms which also incorporate features from multi-objective evolutionary algorithms, multiple population models and local search heuristics are then proposed. . A novel way is developed for solving the portfolio optimization by dividing my problem solution into two parts and then applying a multi-objective cooperative coevolution evolutionary algorithm. The first solution part consists of the asset allocation weights within the equity portfolio while the second solution part consists 'ofthe asset allocation weights within the equity options and the asset allocation weights between the different asset classes. An original portfolio optimization multiobjective evolutionary algorithm that uses an island model to represent different risk measures is also proposed.Imperial Users onl

The Bi-objective Periodic Closed Loop Network Design Problem

© 2019 Elsevier Ltd. This manuscript is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0). For further details please see: https://creativecommons.org/licenses/by-nc-nd/4.0/Reverse supply chains are becoming a crucial part of retail supply chains given the recent reforms in the consumers’ rights and the regulations by governments. This has motivated companies around the world to adopt zero-landfill goals and move towards circular economy to retain the product’s value during its whole life cycle. However, designing an efficient closed loop supply chain is a challenging undertaking as it presents a set of unique challenges, mainly owing to the need to handle pickups and deliveries at the same time and the necessity to meet the customer requirements within a certain time limit. In this paper, we model this problem as a bi-objective periodic location routing problem with simultaneous pickup and delivery as well as time windows and examine the performance of two procedures, namely NSGA-II and NRGA, to solve it. The goal is to find the best locations for a set of depots, allocation of customers to these depots, allocation of customers to service days and the optimal routes to be taken by a set of homogeneous vehicles to minimise the total cost and to minimise the overall violation from the customers’ defined time limits. Our results show that while there is not a significant difference between the two algorithms in terms of diversity and number of solutions generated, NSGA-II outperforms NRGA when it comes to spacing and runtime.Peer reviewedFinal Accepted Versio

Evolutionary Algorithms Based on Effective Search Space Reduction for Financial Optimization Problems

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 문병로.This thesis presents evolutionary algorithms incorporated with effective search space reduction for financial optimization problems. Typical evolutionary algorithms try to find optimal solutions in the original, or unrestricted search space. However, they can be unsuccessful if the optimal solutions are too complex to be discovered from scratch. This can be relieved by restricting the forms of meaningful solutions or providing the initial population with some promising solutions. To this end, we propose three evolution approaches including modular, grammatical, and seeded evolutions for financial optimization problems. We also adopt local optimizations for fine-tuning the solutions, resulting in hybrid evolutionary algorithms. First, the thesis proposes a modular evolution. In the modular evolution, the possible forms of solutions are statically restricted to certain combinations of module solutions, which reflect more domain knowledge. To preserve the module solutions, we devise modular genetic operators which work on modular search space. The modular genetic operators and statically defined modules help genetic programming focus on highly promising search space. Second, the thesis introduces a grammatical evolution. We restrict the possible forms of solutions in genetic programming by a context-free grammar. In the grammatical evolution, genetic programming works on more extended search space than modular one. Grammatically typed genetic operators are introduced for the grammatical evolution. Compared with the modular evolution, grammatical evolution requires less domain knowledge. Finally, the thesis presents a seeded evolution. Our seeded evolution provides the initial population with partially optimized solutions. The set of genes for the partial optimization is selected in terms of encoding complexity. The partially optimized solutions help genetic algorithm find more promising solutions efficiently. Since they are not too excessively optimized, genetic algorithm is still able to search better solutions. Extensive empirical results are provided using three real-world financial optimization problems: attractive technical pattern discovery, extended attractive technical pattern discovery, and large-scale stock selection. They show that our search space reductions are fairly effective for the problems. By combining the search space reductions with systematic evolutionary algorithm frameworks, we show that evolutionary algorithms can be exploited for realistic profitable trading.1. Introduction 1 1.1 Search Methods 3 1.2 Search Space Reduction 4 1.3 Main Contributions 5 1.4 Organization 7 2. Preliminaries 8 2.1 Evolutionary Algorithms 8 2.1.1 Genetic Algorithm 10 2.1.2 Genetic Programing 11 2.2 Evolutionary Algorithms in Finance 12 2.3 Search Space Reduction 12 2.3.1 Modular Evolution 12 2.3.2 Grammatical Evolution 13 2.3.3 Seeded Evolution 14 2.3.4 Summary 14 2.4 Terminology 15 2.4.1 Technical Pattern and Technical Trading Rule 15 2.4.2 Forecasting Model and Trading Model 16 2.4.3 Portfolio and Rebalancing 17 2.4.4 Data Snooping Bias 17 2.5 Financial Optimization Problems 19 2.5.1 Attractive Technical Pattern Discovery and Its Extension 19 2.5.2 Stock Selection 20 2.6 Issues 21 2.6.1 General Assumptions 21 2.6.2 Performance Measure 22 3. Modular Evolution 23 3.1 Modular Genetic Programming 24 3.2 Hybrid Genetic Programming 28 3.3 Attractive Technical Pattern Discovery 29 3.3.1 Introduction 29 3.3.2 Problem Formulation 31 3.3.3 Modular Search Space 33 3.3.4 Experimental Results 35 3.3.5 Summary 41 4. Grammatical Evolution 44 4.1 Grammatical Type System 45 4.2 Hybrid Genetic Programming 47 4.3 Extended Attractive Technical Pattern Discovery 51 4.3.1 Introduction 51 4.3.2 Problem Formulation 54 4.3.3 Experimental Results 56 4.3.4 Summary 73 5. Seeded Evolution 76 5.1 Heuristic Seeding 77 5.2 Hybrid Genetic Algorithm 78 5.3 Large-Scale Stock Selection 81 5.3.1 Introduction 81 5.3.2 Problem Formulation 83 5.3.3 Ranking with Partitions 85 5.3.4 Experimental Results 87 5.3.5 Summary 96 6. Conclusions 104Docto

Thinking with Uncertainty: Scaling Up and Down in the Cryptocurrency World

Against a background of uncertainty, this thesis draws on an understanding of anthropology that disturbs the rushed neoliberal temporality, and looks for mushrooms (Bear, 2014, 2020; Tsing, 2017). It looks closely at the strategies and relations used by occupants of the cryptocurrency space to make habitable a highly volatile and uncertain world. My research participants occupy the heart of contemporary capitalism: in start-up spaces and banks, and also the peripheries: as multi-level marketing investors and 'noisy' retails traders (Preda, 2017). They are united in their engagement with a highly volatile market and uncertain space. They turn to practices of storytelling (Jackson, 2002); take to stages to scale themselves up and scale the world down (Hart, 2014; Tsing, 2012); 'cook money' (Carsten, 1989); form arborescent and rhizomatic networks (Strathern, 2017); and take chances in the face of 'wage slavery', in order to scale their knowledge of the cryptocurrency world

Negotiating ludic normativity in Facebook meme pages

Title: Negotiating ludic normativity in Facebook meme pages Author: Ondřej Procházka Affiliation: Department of Culture Studies, Tilburg School of Humanities and Digital Sciences This thesis explores the capacity of Internet memes to inflect social realities in the communities organized around them on social media, particularly Facebook. Memes are not mere playful ‘jokes’ or ‘parodies’ spreading virally on the Internet in countless variations, they are also powerful tools for political investment aimed to sway public attention and opinions. Memes have been increasingly documented as a vital component in the unprecedented spread and ‘normalization’ of hateful sentiments and ideologies characterized by ‘fake news’ and ‘post-truth’ politics appealing to emotions rather than ‘facts’ in the digital mainstream. Based on author’s more than five-year observation of communities around Countryball memes, this work argues that much of the socio-cultural and communicative dynamics involving memes can be understood in terms of ludic play. The object of the study – Countryballs memes – are simple meme-comics featuring ball-shaped creatures in colors denoting nation-states while satirically reinventing international ‘drama’ through the prism of socio-cultural and linguistic stereotypes. Having become a household name among memes, Countryballs offer communicative resources to playfully engage not only with wider socio-political issues, but also to with the linguistic, semiotic and ideological boundaries of our communicative norms shaped by the affordances of social media. The present work demonstrates how play can be used as a useful concept for understanding not only how matters of public attention are packed, framed and transmitted in the digital culture via (Countryball) memes, but more importantly how such matters are in fact interpreted by those who engage with them. More specifically, it shows how play enables alternative modes of expression and meaning making with different normative patterns and preferences which stand outside ‘standard’, ‘rational’ or ‘civil’ expectations. And it is precisely ludic play that fosters different types of communication and sociality which are often done ‘just for fun’, however serious or offensive their effects may be. To identify these effects and their implications in the contemporary digital age, the thesis employs a discourse-analytical methodology informed by current advances in digital ethnography and sociolinguistics. It focuses on negotiations among participants in memetic communities about what counts as ‘appropriate’, ‘acceptable’ or ‘correct’ in their socio-communicative behavior. Together in four case studies, the present work provides a comprehensive account of how participants articulate, police, break and re-construct ludic normativity in connection with recent socio-political issues and digital culture at large. This includes the role of memes in the newly emerging forms of communication, in the rise of populism and nationalism, algorithmic manipulation and exploitation, curating digital content and more. The concept of play is continually revisited throughout the discussion against the developments in the scholarship on Internet memes and their ludic genealogy. In doing so, the thesis also revisits some of the traditional concepts such as the notion of ‘community’ and ‘communicative competence’ to arrive at more precise accounts of the concrete processes of globalization and digitalization in our societies and their effects