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Solving the problem of optimal design for a two-stage reducer by using a modified evolutionary algorithm
2020
MATEC Web of Conferences
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The work is devoted to solving the problem of selecting optimal geometric parameters of gears of a two-stage cylindrical reducer using a modified evolutionary algorithm (EA). ...
The solution of the specific problem of selecting optimal parameters for a serial reducer is given. ...
Thus, solving the problem of optimal design for a two-stage reducer by using a modified evolutionary algorithm is actual applied task. ...
doi:10.1051/matecconf/202031701004
fatcat:v7r4e65qnfekxcdklvz7ehz4fi
Advanced decision and intelligence technologies for manufacturing and logistics
2011
Journal of Intelligent Manufacturing
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"Design of an improved quantum-inspired evolutionary algorithm for a transportation problem in logistics systems" by Wang, Kowk, and Ip, extends the standard of Quantum-Inspired Evolutionary Algorithm ...
In order to solve the large stochastic optimization problems, a new heuristic search algorithm (HSA) is developed to reduce computational complexity. ...
doi:10.1007/s10845-011-0559-8
fatcat:w3zjeev5hjce3mgi2pwkzh2y5i
Fixed Parameter Evolutionary Algorithms and Maximum Leaf Spanning Trees: A Matter of Mutation
[chapter]
2010
Parallel Problem Solving from Nature, PPSN XI
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We investigate the NP-hard problem of computing a spanning tree that has a maximal number of leaves by evolutionary algorithms in the context of fixed parameter tractability (FPT) where the maximum number ...
Evolutionary algorithms have been shown to be very successful for a wide range of NP-hard combinatorial optimization problems. ...
In [6] , it has been shown that there are fixed parameter evolutionary algorithms for the vertex cover problem. ...
doi:10.1007/978-3-642-15844-5_21
dblp:conf/ppsn/KratschLNO10
fatcat:byxjfluezzgydcmzdrxqmcskda
A novel memetic algorithm for constrained optimization
2010
IEEE Congress on Evolutionary Computation
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In this paper, we present a memetic algorithm with novel local optimizer hybridization strategy for constrained optimization. The developed MA consists of multiple cycles. ...
The results favour our algorithm against the best-known algorithm in terms of the number of fitness evaluations used to reach the global optimum. ...
ACKNOWLEDGEMENT JS was funded to carry out this work by the grant BB/D019613/1 the Centre for Plant Integrative Biology, which is a Centre for Integrative Systems Biology funded by the UK BBSRC and EPSRC ...
doi:10.1109/cec.2010.5585938
dblp:conf/cec/SunG10
fatcat:q6lrks2s7vcl3ov2zubqvvmqwe
Energy Efficient Duty Cycle Design based on Quantum Immune Clonal Evolutionary Algorithm in Body Area Networks
2015
Proceedings of the 10th EAI International Conference on Body Area Networks
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In order to extend the network lifetime, we proposed a novel quantum immune clonal evolutionary algorithm (QICEA) for duty cycle design while maintaining full coverage in the monitoring area. ...
However, the duty cycle design is a highly complex NP-hard problem and its computational complexity is too high with exhaustive search algorithm for practical implementation. ...
The greedy algorithm is simple and fast, but it usually yields shorter network lifetime than evolutionary algorithms. GA method was first introduced in [3] . ...
doi:10.4108/eai.28-9-2015.2261427
dblp:conf/bodynets/ZhouDLFL15
fatcat:c5m5kwqqh5autpdv2w34t5rmoe
Scalable Distributed Genetic Algorithm for Data Ordering Problem with Inversion Using MapReduce
[chapter]
2014
IFIP Advances in Information and Communication Technology
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We present in this work a scalable distributed genetic algorithm of Data Ordering Problem with Inversion using the MapReduce paradigm. ...
This specific topic is appealing for reduction of the power dissipation in VLSI and in bioinformatics. ...
) [12] Evolutionary Approaches [6] The Evolutionary Algorithms (EAs) provide the best results quality. ...
doi:10.1007/978-3-662-44654-6_32
fatcat:bxwnq4jltvfwvgtihkpdnpamwa
Greedy heuristics and evolutionary algorithms for the bounded minimum-label spanning tree problem
2008
Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08
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Two greedy heuristics for the unbounded version of the problem are adapted to the bounded version. Two genetic algorithms for the problem encode labeled spanning trees as permutations of G's edges. ...
Given an edge-labeled, connected, undirected graph G and a bound r > 1, the bounded minimum-label spanning tree problem seeks a spanning tree on G whose edges carry the fewest possible labels and in which ...
Brüggemann, Monnot, and Woeginger [1] described the bounded problem and showed that it is NP-hard for r ≥ 3. To our knowledge, the evolutionary algorithm below is the first for the BMLST problem. ...
doi:10.1145/1389095.1389217
dblp:conf/gecco/KhaledJ08
fatcat:fnnvbvxqlfco7ic6ud5galtrg4
Adapting Problem Specifications and Design Solutions Using Co-evolution
[chapter]
2002
Adaptive Computing in Design and Manufacture V
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In this paper we present a co-evolutionary model of design in which potential solutions to a design problem evolve in parallel with the problem description. ...
In our model of co-evolutionary design, the fitness function is automatically changed as the problem space and solution space co-evolve. ...
The development of the structural system layout problem was done with the collaboration of Anja Wetzel and Michael Tang. ...
doi:10.1007/978-0-85729-345-9_22
fatcat:ui6m4nedmfgxzhoco6ejykiiay
Research on the Mechanical Automation Technology based on Evolutionary Algorithms and Artificial Intelligence Theory
2016
DEStech Transactions on Social Science Education and Human Science
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In this paper, we conduct research on the mechanical automation technology based on the evolutionary algorithms and artificial intelligence theory. ...
Under this basis, this paper proposes the new mechanical automation technology based on the evolutionary algorithms and artificial intelligence theory to propose the new perspective of dealing with the ...
The Evolutionary Algorithms. ...
doi:10.12783/dtssehs/isetem2016/4380
fatcat:igv7mqjcm5alrazqapqonaaena
Evolutionary design of 2-dimensional recursive filters via the computer language GENETICA
2006
IEEE transactions on circuits and systems - 2, Analog and digital signal processing
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The design of the 2-D filter is reduced to a constrained minimization problem the solution of which is achieved by the convergence of an appropriate evolutionary algorithm. ...
We use the computer language GENETICA, which provides the expressive power necessary to get an accurate problem formulation and supports an adjustable evolutionary computational system. ...
Fig. 2 . 2 Visualization of the evolutionary process minimizing the sum of absolute differences in 1018 computational cycles (p = 1). ...
doi:10.1109/tcsii.2005.862040
fatcat:rzb5o63jhvds5ktpnabcggtsi4
Optimal Task Allocation in Wireless Sensor Networks by Means of Social Network Optimization
2019
Mathematics
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In this paper, an evolutionary algorithm recently developed, named Social Network Optimization (SNO), has been applied to the problem of task allocation in a WSN. ...
The optimization results on two test cases have been analyzed: in the first one, no energy constraints have been added to the optimization, while in the second one, a minimum number of life cycles is imposed ...
Additionally, no comparison with other assessed evolutionary algorithms was proposed in order to validate SNO performance over the task allocation problem. ...
doi:10.3390/math7040315
fatcat:g3mlzsu4lfaurcupka2laztc2y
Evolutionary design optimization of traffic signals applied to Quito city
2017
PLoS ONE
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It couples an evolutionary algorithm with a microscopic transport simulator and uses the outcome of the optimization process to deepen our understanding of the problem and gain knowledge about the system ...
In addition, hierarchical clustering is performed on the best solutions found in several runs of the algorithm. ...
Acknowledgments Rolando Armas gratefully acknowledges the support of National Secretariat of Higher Education, Science, Technology and Innovation of Ecuador. ...
doi:10.1371/journal.pone.0188757
pmid:29236733
pmcid:PMC5728506
fatcat:wiipnnbdsjctpg5w64nrvy3sjy
Study of automatically generate test data based on evolutionary algorithm method
2014
TELKOMNIKA Indonesian Journal of Electrical Engineering
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The study of automatically generate test data based on evolutionary algorithm method focuseson the path coverage direction. ...
The key problem is how to construct a suitable and has a good orientation of the fitness function to evaluate the quality of a test data. ...
And in order to get the best effect,fitness functions need to be constantly adjusted according to the processing of evolutionary testing.There are two main problems existing in the current evolutionary ...
doi:10.11591/telkomnika.v12i1.3342
fatcat:z2slgtzmhjgyjgkpxnuqnyogca
Evolutionary feature selection applied to artificial neural networks for wood-veneer classification
2008
International Journal of Production Research
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In the first case, FeaSANNT greatly reduces the feature set with no degradation of the neural network accuracy. Moreover, ...
The novelty of the method lies in the implementation of the embedded approach in an evolutionary feature selection paradigm. ...
The main problem in the design of evolutionary feature selection algorithms for ANN classifiers is the time complexity of the evaluation procedures for the solutions. ...
doi:10.1080/00207540601139955
fatcat:227pnxaagrdmzko433u5irkerm
Speeding Up Evolutionary Algorithms Through Restricted Mutation Operators
[chapter]
2006
Lecture Notes in Computer Science
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For the Eulerian cycle problem; we present runtime bounds on evolutionary algorithms with a restricted operator that are much smaller than the best upper bounds for the general case. ...
We investigate the effect of restricting the mutation operator in evolutionary algorithms with respect to the runtime behavior. ...
For the Eulerian cycle problem we considered a simple evolutionary algorithms that work with a restricted mutation operator. ...
doi:10.1007/11844297_99
fatcat:pgokxyuodncztg4m3zig54qeve
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