The philosophy of evolutionary algorithms is to emulate nature in selecting individuals in a population who will populate future generations. In order to speed up the evolutionary process in selecting the best individ...
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ISBN:
(纸本)9781618395528
The philosophy of evolutionary algorithms is to emulate nature in selecting individuals in a population who will populate future generations. In order to speed up the evolutionary process in selecting the best individuals, a fitness function is usually constructed to evaluate the current goodness of individuals in a population, based upon past and current information. However, such a function should not only evaluate the current goodness of individuals but should also predict (perceive) the future goodness of individuals. Further, tuning the mutation rate and crossover rate to produce new elitism by such a fitness function should improve the convergence of generations to a set of best individuals. This is the premise taken here and a modified evolutionary algorithm is developed by designing a heuristic fitness function that incorporates prediction. Simulation results are provided to compare the approach to traditional fitness function strategies.
Differential evolution （DE） is a kind of evolutionary algorithms, which is suitable for solving complex optimization problems. Mutation is a crucial step in DE that generates new solutions from old ones. It was argu...
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Differential evolution （DE） is a kind of evolutionary algorithms, which is suitable for solving complex optimization problems. Mutation is a crucial step in DE that generates new solutions from old ones. It was argued and has been commonly adopted in DE that the solutions selected for mutation should have mutually different indices. This restrained condition, however, has not been verified either theoretically or empirically yet. In this paper, we empirically investigate the selection of solutions for mutation in DE. From the observation of the extensive experiments, we suggest that the restrained condition could be relaxed for some classical DE versions as well as some advanced DE variants. Moreover, relaxing the restrained condition may also be useful in designing better future DE algorithms.
Complexity is commonly summarized as‘the actions of the whole are more than the sum of the actions of the parts’.Understanding how the coherence emerges from these natural and artificial systems provides a radical s...
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Complexity is commonly summarized as‘the actions of the whole are more than the sum of the actions of the parts’.Understanding how the coherence emerges from these natural and artificial systems provides a radical shift in the process of thought,and brings huge promises for controlling and fostering this *** authors define the term‘Complex System Engineering’to denote this approach,which aims at transferring the radical insights from Complex System Science to the pragmatic world of engineering,especially in the Computing System Engineering domain.A theoretical framework for Complex System Engineering is built by the morphogenetic engineering framework,which identifies a graduation of models,in growing order of generative *** implementation of Complex System Engineering requires a portfolio of operational solutions:The authors therefore provide a classification of Complex System application approaches to answer this challenge and support the emergence of Complex System Engineers capable of addressing the issues of an ever more connected world.
Conformer searching algorithms find minima in the Potential Energy Surface (PES) of a molecule, usually by following a torsion-driven approach. The minima represent conformers, which are interchangeable via free rotat...
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Conformer searching algorithms find minima in the Potential Energy Surface (PES) of a molecule, usually by following a torsion-driven approach. The minima represent conformers, which are interchangeable via free rotation around bonds. Conformers can be used as input to computational analyses, such as drug design, that can convey molecular reactivity, structure, and function. With an increasing number of rotatable bonds, finding optima in the PES becomes more complicated, as the dimensionality explodes. Kaplan is a new, free and open-source software package written by the author that uses a ring-based evolutionary Algorithm (EA) to find conformers. The ring, which contains population members (or pmems), is designed to allow initial PES exploration, followed by exploitation of individual energy wells, such that the most energetically-favourable structures are returned. The strengths and weaknesses of existing publicly available conformer searchers are discussed, including Balloon, RDKit, Openbabel, Confab, Frog2, and Kaplan. Since RDKit is usually considered to be the best free package for conformer searching, its conformers for the amino acids were optimised using the MMFF94 forcefield and compared to the conformers generated by Kaplan. Amino acid conformers are well characterised, and provide insight for protein substructure. Of the 20 molecules, Kaplan found a lower energy minima for 12 of the structures and tied for 5 of them. Kaplan allows the user to specify which dihedrals (by atom indices) to optimise and angles to use, a feature that is not offered by other programs. The results from Kaplan were compared to a known dataset of amino acid conformers. Kaplan identified all 57 conformers of methionine to within 1.2A, and found identical conformers for the 5 lowest-energy structures (i. e. within 0.083A), following forcefield optimisation.
During the past decade,research efforts have been gradually directed to the widely existing yet less noticed multimodal multi-objective optimization problems(MMOPs)in the multi-objective optimization ***,researchers h...
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During the past decade,research efforts have been gradually directed to the widely existing yet less noticed multimodal multi-objective optimization problems(MMOPs)in the multi-objective optimization ***,researchers have begun to investigate enhancing the decision space diversity and preserving valuable dominated solutions to overcome the shortage caused by a preference for objective space ***,many existing methods still have limitations,such as giving unduly high priorities to convergence and insufficient ability to enhance decision space *** overcome these shortcomings,this article aims to explore a promising region(PR)and enhance the decision space diversity for handling *** traditional methods,we propose the use of non-dominated solutions to determine a limited region in the PR in the decision space,where the Pareto sets(PSs)are included,and explore this region to assist in solving ***,we develop a novel neighbor distance measure that is more suitable for the complex geometry of PSs in the decision space than the crowding *** on the above methods,we propose a novel dual-population-based coevolutionary *** studies on three benchmark test suites demonstrates that our proposed methods can achieve promising performance and versatility on different *** effectiveness of the proposed neighbor distance has also been justified through comparisons with crowding distance methods.
Evapotranspiration is an essential component of the hydrological cycle that is of particular interest for water resource *** quantification is helpful in irrigation scheduling,water balance studies,water allocation,**...
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Evapotranspiration is an essential component of the hydrological cycle that is of particular interest for water resource *** quantification is helpful in irrigation scheduling,water balance studies,water allocation,*** of reference evapotranspiration(ET0)using both gene expression programming(GEP)and artificial neural network(ANN)techniques was done using the daily meteorological data of the Pantnagar region,India,from 2010 to 2019.A total of 15 combinations of inputs were used in developing the ET0 *** model with the least number of inputs consisted of maximum and minimum air temperatures,whereas the model with the highest number of inputs consisted of maximum air temperature,minimum air temperature,mean relative humidity,number of sunshine hours,wind speed at 2mheight and extra-terrestrial radiation as inputs and with ET0 as the output for all the *** the GEP models were developed for a single functional set and pre-defined genetic operator values,while the best structure in each ANN model was found based on the performance during the testing *** was found that ANN models were superior to GEP models for the estimation *** was evident from the reduction in RMSE values ranging from 2%to 56%during training and testing phases in all the ANN models compared with GEP *** ANN models showed an increase of about 0.96%to 9.72%of R2 value compared to the respective GEP *** comparative study of these models with multiple linear regression(MLR)depicted that the ANN and GEP models were superior to MLR models.
Many software engineering tasks can potentially be automated using search heuristics. However, much work is needed in designing and evaluating search heuristics before this approach can be routinely applied to a softw...
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Many software engineering tasks can potentially be automated using search heuristics. However, much work is needed in designing and evaluating search heuristics before this approach can be routinely applied to a software engineering problem. Experimental methodology should be complemented with theoretical analysis to achieve this ***, there have been significant theoretical advances in the runtime analysis of evolutionary algorithms (EAs) and other search heuristics in other problem domains. We suggest that these methods could be transferred and adapted to gain insight into the behaviour of search heuristics on software engineering problems while automating software engineering.
In this study, stochastic computational intelligence techniques are presented for the solution of Troesch's boundary value problem. The proposed stochastic solvers use the competency of a feed-forward artificial n...
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In this study, stochastic computational intelligence techniques are presented for the solution of Troesch's boundary value problem. The proposed stochastic solvers use the competency of a feed-forward artificial neural network for mathematical modeling of the problem in an unsupervised manner, whereas the learning of unknown parameters is made with local and global optimization methods as well as their combinations. Genetic algorithm (GA) and pattern search (PS) techniques are used as the global search methods and the interior point method (IPM) is used for an efficient local search. The combination of techniques like GA hybridized with IPM (GA-IPM) and PS hybridized with IPM (PS-IPM) are also applied to solve different forms of the equation. A comparison of the proposed results obtained from GA, PS, IPM, PS-IPM and GA-IPM has been made with the standard solutions including well known analytic techniques of the Adomian decomposition method, the variational iterational method and the homotopy perturbation method. The reliability and effectiveness of the proposed schemes, in term of accuracy and convergence, are evaluated from the results of statistical analysis based on sufficiently large independent runs. [ABSTRACT FROM AUTHOR]
Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are *** a...
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Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are *** a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec ***,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables ***,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is *** data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between *** by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these *** mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast ***-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.
Community detection is a complex optimization problem that consists on searching homogeneous communities that belong to a given graph. This graph, which represent a network, has properties that enable the detection of...
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ISBN:
(纸本)9781450349390
Community detection is a complex optimization problem that consists on searching homogeneous communities that belong to a given graph. This graph, which represent a network, has properties that enable the detection of characteristics or functional relationships in the network. A large number of approaches have been proposed to solve this problem in different disciplines. Nevertheless, only a few research papers have applied community detection to power grids. This paper presents a new evolutionary algorithm for community detection that is applied in power grids. This evolutionary approach employs an efficient initialization strategy that only considers feasible solutions and uses two different search operators that allow the algorithm to obtain a good convergence and diversity of solutions. The preliminary results show that the proposed algorithm obtain quality results in real power grids.
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