One of the problems that single-threaded (non-parallel) evolutionary algorithms encounter is premature convergence and the lack of diversity in the population. To counteract this problem and improve the performance of...
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ISBN:
(纸本)9783319780542;9783319780535
One of the problems that single-threaded (non-parallel) evolutionary algorithms encounter is premature convergence and the lack of diversity in the population. To counteract this problem and improve the performance of evolutionary algorithms in terms of the quality of optimized solutions, a new subpopulation-based selection scheme - the convection selection - is introduced and analyzed in this work. This new selection scheme is compared against traditional selection of individuals in a single-population evolutionary processes. The experimental results indicate that the use of subpopulations with fitness-based assignment of individuals yields better results than both random assignment and a traditional, non-parallel evolutionary architecture.
Multi-objective evolutionary algorithms (MOEAs) have been widely studied by many researchers and they have been used to solve different real-world applications that have more than one objective function. However, most...
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ISBN:
(纸本)9781450363921
Multi-objective evolutionary algorithms (MOEAs) have been widely studied by many researchers and they have been used to solve different real-world applications that have more than one objective function. However, most MOEAs work well only when the number of objective functions is small such as two or three. The performance of MOEAs starts degrading significantly when number of objective functions increases. Therefore, there is increasing importance for studying and analyzing the effect of increasing the number of objective functions on the performance of current multi objective evolutionary algorithms. In this paper, the performance of three state-of-the-art multi objective evolutionary algorithms is investigated when increasing the number of objective functions. The tested algorithms are analyzed using test DTLZ test suit. The results show that SMPSO and NSGA-II algorithms are the best two algorithms for high number of objective functions. In addition, the results show that the running time of SMPSO and GDE3 algorithms was effected and increased much when the number of objective functions is large.
Recently, many-objective evolutionary algorithms have been studied by many researchers. Most of research works were concentrated on developing new methods that can deal with the high number of objective functions of s...
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ISBN:
(纸本)9781450365369
Recently, many-objective evolutionary algorithms have been studied by many researchers. Most of research works were concentrated on developing new methods that can deal with the high number of objective functions of such complex problems. Proposing new scalable benchmarks and valid performance metrics are another two commonly discussed domains in many-objective optimization problems. Most of proposed algorithms have been used to solve different real-world problems from several domains and applications. There are many research papers were published to review and compare the performance of current many-objective evolutionary algorithms. On the other hand there is no work that presented to highlight and review the usage of such algorithms in real problems and applications. Therefore there is an increasing significance for analyzing and reviewing of the complex real world problems that solved using many-objective optimization evolutionary algorithm. In this paper, we review the recently research work that have been done in the domain of solving complex real-world problems using many objective evolutionary algorithms. In addition, the most important issues of metrics, benchmarks and algorithms will be discussed briefly.
This article proposes the use of two evolutionary algorithms (EAs) to the dynamic difficulty adjustment (DDA) of a serious game in the rehabilitation robotics application. DDA occurs in runtime for a better user exper...
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This article proposes the use of two evolutionary algorithms (EAs) to the dynamic difficulty adjustment (DDA) of a serious game in the rehabilitation robotics application. DDA occurs in runtime for a better user experience with a game. This approach is used to improve the quality of the game experience and to avoid boredom or frustration for players with severe limitations imposed by pathologies such as stroke, cerebral palsy, and spinal cord injuries. The first EA solves the game adjustment problem, changing the game difficulty according to the player's skill, and the purpose of the second EA is to adjust the coefficients of the first EA's objective function so that it can work in a more effective way. To do so, the second EA uses results of game matches against simulated player profiles. The results shows that the presented method was able to identify a set of coefficients that allows the first EA to correctly adjust the difficulty level for all six tested player profiles.
It is commonly accepted that accurate early diagnosis and monitoring of neurodegenerative conditions is essential for effective disease management and delivery of medication and treatment. This research develops autom...
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It is commonly accepted that accurate early diagnosis and monitoring of neurodegenerative conditions is essential for effective disease management and delivery of medication and treatment. This research develops automatic methods for detecting brain imaging preclinical biomarkers for Parkinson's disease (PD) by considering the novel application of evolutionary algorithms. An additional novel element of this work is the use of evolutionary algorithms to both map and predict the functional connectivity in patients using rs-fMRI data. Specifically, Cartesian Genetic Programming was used to classify dynamic causal modelling data as well as timeseries data. The findings were validated using two other commonly used classification methods (Artificial Neural Networks and Support Vector Machines) and by employing k-fold cross-validation. Across dynamic causal modelling and timeseries analyses, findings revealed maximum accuracies of 75.21% for early stage (prodromal) PD patients in which patients reveal no motor symptoms versus healthy controls, 85.87% for PD patients versus prodromal PD patients, and 92.09% for PD patients versus healthy controls. Prodromal PD patients were classified from healthy controls with high accuracy – this is notable and represents the key finding since current methods of diagnosing prodromal PD have low reliability and low accuracy. Furthermore, Cartesian Genetic Programming provided comparable performance accuracy relative to Artificial Neural Networks and Support Vector Machines. Nevertheless, evolutionary algorithms enable us to decode the classifier in terms of understanding the data inputs that are used, more easily than in Artificial Neural Networks and Support Vector Machines. Hence, these findings underscore the relevance of both dynamic causal modelling analyses for classification and Cartesian Genetic Programming as a novel classification tool for brain imaging data with medical implications for disease diagnosis, particularly in early s
Purpose This study aims to short-therm forecasting of power generation output for this purpose, an adaptive neuro-fuzzy inference system (ANFIS) is designed to forecast the output power of power plant based on climate...
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Purpose This study aims to short-therm forecasting of power generation output for this purpose, an adaptive neuro-fuzzy inference system (ANFIS) is designed to forecast the output power of power plant based on climate factors considering wind speed and wind direction simultaneously. Design/methodology/approach Several methods and algorithms have been proposed for systems forecasting in various fields. One of the strongest methods for modeling complex systems is neuro-fuzzy that refers to combinations of artificial neural network and fuzzy logic. When the system becomes more complex, the conventional algorithms may fail for network training. In this paper, an integrated approach, including ANFIS and metaheuristic algorithms, is used for increasing forecast accuracy. Findings Power generation in power plants is dependent on various factors, especially climate factors. Operating power plant in Iran is very much influenced because of climate variation, including from tropical to subpolar, and severely varying temperature, humidity and air pressure for each region and each season. On the other hands, when wind speed and wind direction are used simultaneously, the training process does not converge, and the forecasting process is unreliable. The real case study is mentioned to show the ability of the proposed approach to remove the limitations. Originality/value First, ANFIS is applied for forecasting based on climate factors, including wind speed and wind direction, that have rarely been used simultaneously in previous studies. Second, the well-known and more widely used metaheuristic algorithms are applied to improve the learning process for forecasting output power and compare the results.
In this paper we introduce KafkEO, a cloud native evolutionary algorithms framework that is prepared to work with population-based metaheuristics by using micro-populations and stateless services as the main building ...
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With the rise of networked multi-core machines, we have seen an increased emphasis on parallel and distributed programming. In this paper we describe an implementation of Factored evolutionary algorithms (FEA) and Dis...
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Search-based software engineering, a discipline that often requires finding optimal solutions, can be a viable source for problems that bridge theory and practice of evolutionary computation. In this research we consi...
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We present refined results for the expected optimisation time of the (1+1) EA and the (1+) EA on LeadingOnes in the prior noise model, where in each fitness evaluation the search point is altered before evaluation wit...
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