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Statements

Subject Item
n2:RIV%2F61989100%3A27740%2F14%3A86092657%21RIV15-MSM-27740___
rdf:type
n19:Vysledek skos:Concept
dcterms:description
Linear ordering problem is a popular NP-hard combinatorial optimization problem attractive for its complexity, rich library of test data, and variety of real world applications. It has been solved by a number of heuristic as well as metaheuristic methods in the past. The implementation of nature-inspired metaheuristic optimization and search methods usually depends on streams of integer and floating point numbers generated in course of their execution. The pseudo-random numbers are utilized for an in-silico emulation of probability-driven natural processes such as arbitrary modification of genetic information (mutation, crossover), partner selection, and survival of the fittest (selection, migration) and environmental effects (small random changes in particle motion direction and velocity). Deterministic chaos is a well known mathematical concept that can be used to generate sequences of seemingly random real numbers within selected interval in a predictable and well controllable way. In the past, it has been used as a basis for various pseudo-random number generators with interesting properties. Recently, it has been shown that it can be successfully used as a source of stochasticity for nature-inspired algorithms solving a continuous optimization problem. In this work we compare effectiveness of the differential evolution with different pseudo-random number generators and chaotic systems as sources of stochasticity when solving the linear ordering problem. Linear ordering problem is a popular NP-hard combinatorial optimization problem attractive for its complexity, rich library of test data, and variety of real world applications. It has been solved by a number of heuristic as well as metaheuristic methods in the past. The implementation of nature-inspired metaheuristic optimization and search methods usually depends on streams of integer and floating point numbers generated in course of their execution. The pseudo-random numbers are utilized for an in-silico emulation of probability-driven natural processes such as arbitrary modification of genetic information (mutation, crossover), partner selection, and survival of the fittest (selection, migration) and environmental effects (small random changes in particle motion direction and velocity). Deterministic chaos is a well known mathematical concept that can be used to generate sequences of seemingly random real numbers within selected interval in a predictable and well controllable way. In the past, it has been used as a basis for various pseudo-random number generators with interesting properties. Recently, it has been shown that it can be successfully used as a source of stochasticity for nature-inspired algorithms solving a continuous optimization problem. In this work we compare effectiveness of the differential evolution with different pseudo-random number generators and chaotic systems as sources of stochasticity when solving the linear ordering problem.
dcterms:title
Can deterministic chaos improve differential evolution for the linear ordering problem? Can deterministic chaos improve differential evolution for the linear ordering problem?
skos:prefLabel
Can deterministic chaos improve differential evolution for the linear ordering problem? Can deterministic chaos improve differential evolution for the linear ordering problem?
skos:notation
RIV/61989100:27740/14:86092657!RIV15-MSM-27740___
n4:aktivita
n5:P n5:S
n4:aktivity
P(ED1.1.00/02.0070), P(EE.2.3.20.0072), P(GA13-08195S), S
n4:dodaniDat
n13:2015
n4:domaciTvurceVysledku
n7:3433390 n7:9175970 n7:4347269
n4:druhVysledku
n9:D
n4:duvernostUdaju
n20:S
n4:entitaPredkladatele
n10:predkladatel
n4:idSjednocenehoVysledku
6120
n4:idVysledku
RIV/61989100:27740/14:86092657
n4:jazykVysledku
n6:eng
n4:klicovaSlova
linear ordering problem; evolution; differential; deterministic
n4:klicoveSlovo
n11:differential n11:deterministic n11:evolution n11:linear%20ordering%20problem
n4:kontrolniKodProRIV
[5719549757C1]
n4:mistoKonaniAkce
Beijing
n4:mistoVydani
New York
n4:nazevZdroje
Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
n4:obor
n21:IN
n4:pocetDomacichTvurcuVysledku
3
n4:pocetTvurcuVysledku
3
n4:projekt
n8:GA13-08195S n8:ED1.1.00%2F02.0070 n8:EE.2.3.20.0072
n4:rokUplatneniVysledku
n13:2014
n4:tvurceVysledku
Snášel, Václav Zelinka, Ivan Krömer, Pavel
n4:typAkce
n12:WRD
n4:zahajeniAkce
2014-07-06+02:00
s:numberOfPages
6
n17:doi
10.1109/CEC.2014.6900589
n18:hasPublisher
Institute of Electrical and Electronics Engineers
n22:isbn
978-1-4799-1488-3
n16:organizacniJednotka
27740