In this study, we propose a stock trading system based on optimized technical analysis parameters for creating buy-sell points using genetic algorithms. The model is developed utilizing Apache Spark big data platform....
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In this study, we propose a stock trading system based on optimized technical analysis parameters for creating buy-sell points using genetic algorithms. The model is developed utilizing Apache Spark big data platform. The optimized parameters are then passed to a deep MLP neural network for buy-sell-hold predictions. Dow 30 stocks are chosen for model validation. Each Dow stock is trained separately using daily close prices between 1996-2016 and tested between 2007-2016. The results indicate that optimizing the technical indicator parameters not only enhances the stock trading performance but also provides a model that might be used as an alternative to Buy and Hold and other standard technical analysis models.
evolutionary algorithms (EAs) are populationbased, stochastic search algorithms that mimic natural evolution. Over the years, EAs have been successfully applied to many classification problems. In this paper, we prese...
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evolutionary algorithms (EAs) are populationbased, stochastic search algorithms that mimic natural evolution. Over the years, EAs have been successfully applied to many classification problems. In this paper, we present three novel evolutionary approaches and analyze their performances for synthesizing classifiers with EAs in supervised data mining scenarios. The first approach is based on encoding rule sets with bit string genomes, while the second one utilizes Genetic Programming (GP) to create decision trees with arbitrary expressions attached to the nodes. The novelty of these two approaches lies in the use of solutions on the Pareto front as an ensemble. The third approach, EDDIE-101, is also based on GP but uses a new, advanced fitness measure and some novel genetic operators. We compare these approaches to a number of well-known data mining methods, including C4.5 and Random-Forest, and show that the performances of our evolved classifiers can be very competitive as far as the solution quality is concerned. In addition, the proposed approaches work well across a wide range of configurations, and EDDIE-101 particularly has been highly efficient. To further evaluate the flexibility of EDDIE-101 across different problem domains, we also test it on some real financial datasets for finding investment opportunities and compare the results with those obtained using other classifiers. Numerical experiments confirm that EDDIE-101 can be successfully extended to financial forecasting.
Over the last years, the effects of neutrality have attracted the attention of many researchers in the evolutionary algorithms (EAs) community. A mutation from one gene to another is considered as neutral if this modi...
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Over the last years, the effects of neutrality have attracted the attention of many researchers in the evolutionary algorithms (EAs) community. A mutation from one gene to another is considered as neutral if this modification does not affect the phenotype. This article provides a general overview on the work carried out on neutrality in EAs. Using as a framework the origin of neutrality and its study in different paradigms of EAs (e.g., Genetic algorithms, Genetic Programming), we discuss the most significant works and findings on this topic. This work points towards open issues, which we belive the community needs to address.
Agents' perceptions on the state of the economy can be affected during economic crises. Tendency surveys are the main source of agents' expectations. The main objective of this study is to assess the impact of...
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Agents' perceptions on the state of the economy can be affected during economic crises. Tendency surveys are the main source of agents' expectations. The main objective of this study is to assess the impact of the 2008 financial crisis on agents' expectations. With this aim, we evaluate the capacity of survey-based expectations to anticipate economic growth in the United States, Japan, Germany and the United Kingdom. We propose a symbolic regression (SR) via genetic programming approach to derive mathematical functional forms that link survey-based expectations to GDP growth. By combining the main SR-generated indicators, we generate estimates of the evolution of GDP. Finally, we analyse the effect of the crisis on the formation of expectations, and we find an improvement in the capacity of agents' expectations to anticipate economic growth after the crisis in all countries except Germany.
In this study, we analyze solution methods for approximating the Pareto front of bi-objective mixed-integer linear programming problems. First of all, we discuss a two-stage evolutionary algorithm. Given the values fo...
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ISBN:
(纸本)9780983762461
In this study, we analyze solution methods for approximating the Pareto front of bi-objective mixed-integer linear programming problems. First of all, we discuss a two-stage evolutionary algorithm. Given the values for the integer variables, the second stage of the two-stage evolution algorithm generates the values for the continuous variables of the corresponding Pareto efficient solutions. Then, the corresponding Pareto efficient solutions of integer variables are compared in the first-stage of the two-stage evolutionary algorithm to determine the Pareto efficient integer solutions. These stages are repeated within an evolutionary heuristic structure to approximate the Pareto front. Secondly, we propose a decomposition approach to separate the integral part of the feasible region of the problem. The decomposition approach separates the problem into sub-problems, each of which has an additional constraint, and approximates the Pareto fronts of the sub-problems using the two-stage evolutionary algorithm discussed. Then, using the sub-problem Pareto fronts, the Pareto front of the main problem is approximated. A numerical study is conducted to compare the two-stage evolutionary algorithm with the decomposition approach, which uses the two-stage evolutionary algorithm.
In this article, a design methodology for complex composite aircraft structures is presented. The developed approach combines a multi-objective optimization method and a parameterized simulation model using a design c...
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In this article, a design methodology for complex composite aircraft structures is presented. The developed approach combines a multi-objective optimization method and a parameterized simulation model using a design concept database. Due to the combination of discrete and continuous design variables describing the structures, evolutionary algorithms are used within the presented optimization approach. The approach requires an evaluation of the design alternatives that is performed by parameterized simulation models. The variability of these models is achieved using a design concept database that contains different layouts for each implemented structural part. Due to the complexity of the generated aircraft structures, the finite element method is applied for the calculation of the structural behaviour. The applicability of the developed design approach will be demonstrated by optimizing two composite aircraft fuselage examples. The obtained results show that the developed methodology is useful and reliable for designing complex aircraft structures.
Linear functions have gained a lot of attention in the area of run time analysis of evolutionary computation methods and the corresponding analyses have provided many effective tools for analyzing more complex problem...
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In this paper, we propose a triple comparison-based interactive differential evolution (IDE) algorithm and a differential evolution (DE) algorithm. The comparison of target vector and trial vector supports a local fit...
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Content Based Image Retrieval (CBIR) system receives paramount importance now days. This is because of its wide applicability found in many areas including medical, science, security, Bioinformatics and entertainments...
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The paper characterises a class of problems for packing boxes in the container. It presents the current state of knowledge in this area and distinguishes evolutionary algorithms, as the most promising in the search fo...
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The paper characterises a class of problems for packing boxes in the container. It presents the current state of knowledge in this area and distinguishes evolutionary algorithms, as the most promising in the search for quasi-optimal loading conditions. The method proposed in the paper focuses on certain criteria important from a practical point of view, which in a formalised manner have not been included so far in the solving-problems models. Apart from the traditional consideration of the problem of three-dimensional space loading maximisation, the proposed method considers the deviation of the loaded container weight centre from its symmetry planes and the availability of packages during unloading. New elements in the described method are: the applied criteria, penalty function, way of coding the container loading state in the evolutionary algorithm and fast crossover and mutations operators dedicated to the adopted coding. It was observed that the evolution in the developed algorithm occurs properly, that is seeking to minimise the criteria. The article also includes a calculation example showing the effect of the method with the discussion of the results indicating the advantages and disadvantages of the proposed solution. The performance of the algorithm has been considered in the context of time necessary to obtain the acceptable solution and quality of the obtained solution. It was found that the algorithm in its current form is a strong base for its further improvement.
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