This file was created by the TYPO3 extension publications
--- Timezone: CEST
Creation date: 2024-03-28
Creation time: 17:03:11
--- Number of references
134
inbook
JohannesLenglerandAndreOprisandDirkSudholt.
Analysing Equilibrium States for Population Diversity
1
2023
1
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2023)
ACM
Johannes
Lengler
Andre
Opris
Dirk
Sudholt
inbook
DucCuongDangandAndreOprisandBahareSalehiandDirkSudholt.
Analysing the Robustness of NSGA-II under Noise
2023
1
Duc-Cuong
Dang
Andre
Opris
Bahare
Salehi
Dirk
Sudholt
inbook
JoostJorritsmaandJohannesLenglerandDirkSudholt.
Comma Selection Outperform Plus Selection on OneMax with Randomly Planted Optima
2023
1
Joost
Jorritsma
Johannes
Lengler
Dirk
Sudholt
article
Bossek.
Do additional target points speed up evolutionary algorithms?
2023
1
03043975
10.1016/j.tcs.2023.113757
Theoretical Computer Science
950
113757
Jakob
Bossek
Dirk
Sudholt
inbook
JakobBossekandDirkSudholt.
Runtime Analysis of Quality Diversity Algorithms
2023
1
Jakob
Bossek
Dirk
Sudholt
book
Kneissl.
The Cost of Randomness in Evolutionary Algorithms: Crossover can Save Random Bits
2023
1
10.1007/978-3-031-30035-6\textunderscore 12
13987
Carlo
Kneissl
Dirk
Sudholt
article
VijayDhanjibhaiBhuva.
Evolutionary Algorithms for Cardinality-Constrained Ising Models
2022
1
10.1007/978-3-031-14721-0$\backslash$\textunderscore 32
Vijay Dhanjibhai
Bhuva
Duc-Cuong
Dang
Liam
Huber
Dirk
Sudholt
article
Fajardo.
Hard problems are easier for success-based parameter control
2022
1
10.1145/3512290.3528781
796-804
Mario Alejandro Hevia
Fajardo
Dirk
Sudholt
article
On the impact of the performance metric on efficient algorithm configuration
2022
11
08
1
1. https://www.sciencedirect.com/science/article/abs/pii/S0004370221001806
George T.
Hall
Dirk
Sudholt
Pietro S.
Oliveto
article
CovantesOsuna2021
Runtime Analysis of Restricted Tournament Selection for Bimodal Optimisation
2022
1
Evolutionary Computation
MIT Press
Edgar
Covantes Osuna
Dirk
Sudholt
article
Neumann.
The compact genetic algorithm struggles on Cliff functions
2022
1
10.1145/3512290.3528776
1426-1433
Frank
Neumann
Dirk
Sudholt
Carsten
Witt
article
HeviaFajardo.
Theoretical and Empirical Analysis of Parameter Control Mechanisms in the (1 + (\textgreekl, \textgreekl)) Genetic Algorithm
2022
1
2688-299X
10.1145/3564755
ACM Transactions on Evolutionary Learning and Optimization
2
1-39
4
Mario Alejandro
Hevia Fajardo
Dirk
Sudholt
article
Sudholt.
Theory and practice of population diversity in evolutionary computation
2022
1
10.1145/3377929.3389892
975-992
Dirk
Sudholt
Giovanni
Squillero
article
Tight Bounds on the Expected Runtime of a Standard Steady State Genetic Algorithm", Algorithmica
2022
1
Pietro S.
Oliveto
Dirk
Sudholt
Carsten
Witt
article
Sudholt2020
Analysing the Robustness of Evolutionary Algorithms to
Noise: Refined Runtime Bounds and an Example Where Noise is
Beneficial
2021
1
Algorithmica
83
976-1011
4
https://doi.org/10.1007/s00453-020-00671-0
Dirk
Sudholt
inproceedings
Bossek.09062021
Do additional optima speed up evolutionary algorithms?
2021
1
9781450383523
10.1145/3450218.3477309
Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms
ACM
New York, NY, USA
Finck, Steffen and Hellwig, Michael and Oliveto, Pietro S.
1--11
Jakob
Bossek
Dirk
Sudholt
inproceedings
Fajardo.09062021
Self-adjusting offspring population sizes outperform fixed parameters on the cliff function
2021
9781450383523
10.1145/3450218.3477306
Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms
ACM
New York, NY, USA
Finck, Steffen and Hellwig, Michael and Oliveto, Pietro S.
1--15
Mario Alejandro
Hevia Fajardo
Dirk
Sudholt
inproceedings
Self-Adjusting Population Sizes for Non-Elitist Evolutionary Algorithms: Why Success Rates Matter
1
2021
1
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2021)
ACM
Mario Alejandro
Hevia Fajardo
Dirk
Sudholt
article
The Complex Parameter Landscape of the Compact Genetic Algorithm
2021
1
Algorithmica
83
1096-1137
4
https://rdcu.be/b9KBR
Johannes
Lengler
Dirk
Sudholt
Carsten
Witt
article
Bossek.2021
Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem
2021
10.1007/s00453-021-00838-3
Algorithmica
Jakob
Bossek
Frank
Neumann
Pan
Peng
Dirk
Sudholt
inproceedings
Oliveto2020
A Tight Lower Bound on the Expected Runtime of Standard
Steady State Genetic Algorithms
2020
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2020)
ACM
1323-1331
https://doi.org/10.1145/3377930.3390212
Pietro S.
Oliveto
Dirk
Sudholt
Carsten
Witt
inproceedings
Hall2020
Analysis of the Performance of Algorithm Configurators for
Search Heuristics with Global Mutation Operators
2020
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2020)
ACM
823-831
https://doi.org/10.1145/3377930.3390218
George T.
Hall
Pietro S.
Oliveto
Dirk
Sudholt
inproceedings
Albunian2020
Causes and Effects of Fitness Landscapes in Unit Test
Generation
2020
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2020)
ACM
1204-1212
https://doi.org/10.1145/3377930.3390194
Nasser
Albunian
Gordon
Fraser
Dirk
Sudholt
article
Covantes2018b
Design and Analysis of Diversity-Based Parent Selection
Schemes for Speeding Up Evolutionary Multi-objective
Optimisation
2020
Theoretical Computer Science
832
123-142
https://authors.elsevier.com/c/1b6NN15DaHylEh
Edgar
Covantes Osuna
Wanru
Gao
Frank
Neumann
Dirk
Sudholt
inproceedings
Foster2020
Do Sophisticated Evolutionary Algorithms Perform Better
than Simple Ones?
2020
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2020)
ACM
184-192
https://doi.org/10.1145/3377930.3389830
Michael
Foster
Matthew
Hughes
George
O'Brien
Pietro S.
Oliveto
James
Pyle
Dirk
Sudholt
James
Williams
inproceedings
Hall2020a
Fast Perturbative Algorithm Configurators
2020
Parallel Problem Solving from Nature (PPSN 2020)
19--32
https://doi.org/10.1007/978-3-030-58112-1_2
George T.
Hall
Pietro S.
Oliveto
Dirk
Sudholt
inproceedings
Albunian2020a
Measuring and Maintaining Population Diversity in
Search-based Unit Test Generation
2020
Proceedings of the 12th Symposium on Search-Based
Software Engineering (SSBSE 2020)
153--168
https://doi.org/10.1007/978-3-030-59762-7_11
Nasser
Albunian
Gordon
Fraser
Dirk
Sudholt
article
Nguyen2019
Memetic Algorithms Outperform Evolutionary Algorithms in
Multimodal Optimisation
2020
1
Artificial Intelligence
287
103345
https://doi.org/10.1016/j.artint.2020.103345
Phan Trung Hai
Nguyen
Dirk
Sudholt
inproceedings
Bossek2020
More Effective Randomized Search Heuristics for Graph
Coloring Through Dynamic Optimization
2020
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2020)
ACM
1277-1285
https://doi.org/10.1145/3377930.3390174
Jakob
Bossek
Frank
Neumann
Pan
Peng
Dirk
Sudholt
inproceedings
Hevia2020
On the Choice of the Parameter Control Mechanism in the(1+(λ,λ)) Genetic Algorithm
2020
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2020)
ACM
832-840
https://doi.org/10.1145/3377930.3390200
Mario Alejandro
Hevia Fajardo
Dirk
Sudholt
article
Lehre2019
Parallel Black-Box Complexity with Tail Bounds
2020
1
IEEE Transactions on Evolutionary Computation
24
1010-1024
6
https://doi.org/10.1109/TEVC.2019.2954234
Per Kristian
Lehre
Dirk
Sudholt
article
Covantes2019
Runtime Analysis of Crowding Mechanisms for Multimodal
Optimisation
2020
IEEE Transactions on Evolutionary Computation
24
581--592
3
https://doi.org/10.1109/TEVC.2019.2914606
Edgar
Covantes Osuna
Dirk
Sudholt
incollection
Sudholt2018
The Benefits of Population Diversity in Evolutionary
Algorithms: A Survey of Rigorous Runtime Analyses
2020
Theory of Evolutionary Computation: Recent Developments
in Discrete Optimization
Springer
Benjamin Doerr and Frank Neumann
https://link.springer.com/chapter/10.1007/978-3-030-29414-4_8
Dirk
Sudholt
article
Sudholt.b
Theory and practice of population diversity in evolutionary computation
2020
1
10.1145/3520304.3533642
1469-1486
Dirk
Sudholt
Giovanni
Squillero
article
Nallaperuma2018
On the Analysis of Trajectory-Based Search Algorithms:
When is it Beneficial to Reject Improvements?
2019
Algorithmica
81
858--885
2
https://doi.org/10.1007/s00453-018-0462-1
Samadhi
Nallaperuma
Pietro S.
Oliveto
Jorge
Pérez Heredia
Dirk
Sudholt
article
Oliveto2018
On the Benefits and Risks of Using Fitness Sharing for
Multimodal Optimisation
2019
Theoretical Computer Science
773
53--70
https://doi.org/10.1016/j.tcs.2018.07.007
Pietro S.
Oliveto
Dirk
Sudholt
Christine
Zarges
article
Sudholt2018b
On the Choice of the Update Strength in
Estimation-of-Distribution Algorithms and Ant Colony
Optimization
2019
Algorithmica
81
1450--1489
4
https://doi.org/10.1007/s00453-018-0480-z
Dirk
Sudholt
Carsten
Witt
inproceedings
Hall2019
On the Impact of the Cutoff Time on the Performance of
Algorithm Configurators
2019
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2019)
ACM
907--915
https://doi.org/10.1145/3321707.3321879
George T.
Hall
Pietro S.
Oliveto
Dirk
Sudholt
article
Covantes2018a
On the Runtime Analysis of the Clearing
Diversity-Preserving Mechanism
2019
Evolutionary Computation
27
MIT Press
403-433
https://doi.org/10.1162/evco_a_00225
Edgar
Covantes Osuna
Dirk
Sudholt
article
Kotzing.
Preface to the Special Issue on Theory of Genetic and Evolutionary Computation
2019
1
0178-4617
10.1007/s00453-017-0379-0
Algorithmica
80
1575-1578
5
Timo
Kötzing
Dirk
Sudholt
inproceedings
Bossek2019
Runtime Analysis of Randomized Search Heuristics for
Dynamic Graph Coloring
2019
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2019)
ACM
1443--1451
https://doi.org/10.1145/3321707.3321792
Jakob
Bossek
Frank
Neumann
Pan
Peng
Dirk
Sudholt
inproceedings
Bossek2019a
Time Complexity Analysis of RLS and (1+1)~EA for the
Edge Coloring Problem
2019
Proceedings of the 15th ACM/SIGEVO Conference on
Foundations of Genetic Algorithms (FOGA 2019)
ACM
102--115
https://doi.org/10.1145/3299904.3340311
Jakob
Bossek
Dirk
Sudholt
inproceedings
CovantesOsuna2018a
Empirical Analysis of Diversity-preserving Mechanisms on
Example Landscapes for Multimodal Optimisation
2018
Parallel Problem Solving from Nature (PPSN~'18)
Springer
207--219
https://rdcu.be/bsfpE
Edgar
Covantes Osuna
Dirk
Sudholt
article
Dang2017
Escaping Local Optima Using Crossover with Emergent
Diversity
2018
IEEE Transactions on Evolutionary Computation
22
IEEE
484--497
https://doi.org/10.1109/TEVC.2017.2724201
Duc-Cuong
Dang
Per Kristian
Lehre
Tobias
Friedrich
Timo
Kötzing
Martin S.
Krejca
Pietro S.
Oliveto
Dirk
Sudholt
Andrew M.
Sutton
article
Oliveto2017
How to Escape Local Optima in Black Box Optimisation: When
Non-elitism Outperforms Elitism
2018
Algorithmica
80
1604--1633
http://dx.doi.org/10.1007/s00453-017-0369-2
Pietro S.
Oliveto
Tiago
Paixão
Jorge
Pérez Heredia
Dirk
Sudholt
Barbora
Trubenova
inproceedings
Lengler2018
Medium Step Sizes are Harmful for the Compact Genetic
Algorithm
2018
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2018)
ACM
1499--1506
https://doi.org/10.1145/3205455.3205576
Johannes
Lengler
Dirk
Sudholt
Carsten
Witt
inproceedings
Nguyen2018
Memetic Algorithms Beat Evolutionary Algorithms on the
Class of Hurdle Problems
2018
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2018)
ACM
1071--1078
https://doi.org/10.1145/3205455.3205456
Phan Trung Hai
Nguyen
Dirk
Sudholt
inproceedings
Sudholt2018a
On the Robustness of Evolutionary Algorithms to Noise:
Refined Results and an Example Where Noise Helps
2018
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2018)
ACM
1523--1530
https://doi.org/10.1145/3205455.3205595
Dirk
Sudholt
article
Doerr.
Preface to the Special Issue on Theory of Genetic and Evolutionary Computation
2018
1
0178-4617
10.1007/s00453-018-00543-8
Algorithmica
81
589-592
2
Carola
Doerr
Dirk
Sudholt
inproceedings
CovantesOsuna2018
Runtime Analysis of Probabilistic Crowding and Restricted
Tournament Selection for Bimodal Optimisation
2018
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2018)
ACM
929--936
https://doi.org/10.1145/3205455.3205591
Nominiert für einen best paper award in der Kategorie
\glqq{}Genetic Algorithms\grqq{}.
Edgar
Covantes Osuna
Dirk
Sudholt
inproceedings
CovantesOsuna2017
Analysis of the Clearing Diversity-Preserving
Mechanism
2017
Proceedings of Foundations of Genetic Algorithms
(FOGA 2017)
ACM Press
55--63
https://doi.org/10.1145/3040718.3040731
Edgar
Covantes Osuna
Dirk
Sudholt
article
Nallaperuma2017
Expected Fitness Gains of Randomized Search Heuristics for
the Traveling Salesperson Problem
2017
Evolutionary Computation
25
673--705
http://dx.doi.org/10.1162/EVCO_a_00199
Samadhi
Nallaperuma
Frank
Neumann
Dirk
Sudholt
article
Sudholt2016
How Crossover Speeds Up Building-Block Assembly in
Genetic Algorithms
2017
Evolutionary Computation
25
237-274
2
http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00171
Dirk
Sudholt
article
Corus2017
On Easiest Functions for Mutation Operators in Bio-Inspired
Optimisation
2017
Algorithmica
78
714--740
2
http://link.springer.com/article/10.1007/s00453-016-0201-4
Dogan
Corus
Jun
He
Thomas
Jansen
Pietro S.
Oliveto
Dirk
Sudholt
Christine
Zarges
article
Moraglio2017
Principled Design and Runtime Analysis of Abstract Convex
Evolutionary Search
2017
Evolutionary Computation
25
205--236
2
http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00169
Alberto
Moraglio
Dirk
Sudholt
article
PerezHeredia2016
Selection Limits to Adaptive Walks on Correlated
Landscapes
Adaptation depends critically on the effects of new
mutations and their dependency on the genetic background they occur
in. These two factors can be summarized by the fitness landscape.
However, it would require testing all mutations in all backgrounds,
making the definition and analysis of fitness landscapes mostly
inaccessible. Instead of postulating a particular fitness
landscape, we address this problem by considering general classes
of landscapes and calculating an upper limit for the time it takes
for a population to reach a fitness peak, circumventing the need to
have full knowledge about the fitness landscape. We analyse
populations in the weak-mutation regime and characterize the
conditions that enable them to quickly reach the fitness peak as a
function of the number of sites under selection. We show that for
additive landscapes there is a critical selection strength enabling
populations to reach high fitness genotypes, regardless of the
distribution of effects. This threshold scales with the number of
sites under selection, effectively setting a limit to adaptation,
and results from the inevitable increase in deleterious mutational
pressure as the population adapts in a space of discrete genotypes.
Furthermore, we show that for the class of all unimodal landscapes
this condition is sufficient but not necessary for rapid
adaptation, as in some highly epistatic landscapes the critical
strength does not depend on the number of sites under selection,
effectively removing this barrier to adaptation.
2017
10.1534/genetics.116.189340
Genetics
205
Genetics
803--825
2
http://www.genetics.org/content/early/2016/11/22/genetics.116.189340
Jorge
Pérez Heredia
Barbora
Trubenova
Dirk
Sudholt
Tiago
Paixão
inproceedings
CovantesOsuna2017a
Speeding Up Evolutionary Multi-objective Optimisation
Through Diversity-based Parent Selection
2017
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2017)
ACM
553--560
https://doi.org/10.1145/3071178.3080294
Edgar
Covantes Osuna
Wanru
Gao
Frank
Neumann
Dirk
Sudholt
inproceedings
Lissovoi2017
Theoretical Results on Bet-and-run As an Initialisation
Strategy
2017
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2017)
ACM
857--864
https://doi.org/10.1145/3071178.3071329
Andrei
Lissovoi
Dirk
Sudholt
Markus
Wagner
Christine
Zarges
article
Paixao2016
Towards a Runtime Comparison of Natural and Artificial
Evolution
Evolutionary algorithms (EAs) form a popular optimisation
paradigm inspired by natural evolution. In recent years the field
of evolutionary computation has developed a rigorous analytical
theory to analyse the runtimes of EAs on many illustrative
problems. Here we apply this theory to a simple model of natural
evolution. In the Strong Selection Weak Mutation (SSWM)
evolutionary regime the time between occurrences of new mutations
is much longer than the time it takes for a mutated genotype to
take over the population. In this situation, the population only
contains copies of one genotype and evolution can be modelled as a
stochastic process evolving one genotype by means of mutation and
selection between the resident and the mutated genotype. The
probability of accepting the mutated genotype then depends on the
change in fitness. We study this process, SSWM, from an algorithmic
perspective, quantifying its expected optimisation time for various
parameters and investigating differences to a similar evolutionary
algorithm, the well-known (1+1)?EA. We show that SSWM can have a
moderate advantage over the (1+1)?EA at crossing fitness valleys
and study an example where SSWM outperforms the (1+1)?EA by taking
advantage of information on the fitness gradient.
2017
Algorithmica
78
681--713
2
http://dx.doi.org/10.1007/s00453-016-0212-1
Tiago
Paixão
Jorge
Pérez Heredia
Dirk
Sudholt
Barbora
Trubenova
inproceedings
Nallaperuma2017a
When is It Beneficial to Reject Improvements?
2017
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2017)
ACM
1391--1398
https://doi.org/10.1145/3071178.3071273
Samadhi
Nallaperuma
Pietro S.
Oliveto
Jorge Pérez
Heredia
Dirk
Sudholt
inproceedings
Dang2016a
Emergence of Diversity and Its Benefits for
Crossover in Genetic Algorithms
Population diversity is essential for avoiding
premature convergence in Genetic Algorithms (GAs) and for the
effective use of crossover. Yet the dynamics of how diversity
emerges in populations are not well understood. We use rigorous
runtime analysis to gain insight into population dynamics and GA
performance for a standard
$\backslash$(($\backslash$mu+1)$\backslash$) GA and the
$\backslash$(Jump\_k$\backslash$) test function. By studying the
stochastic process underlying the size of the largest collection
of identical genotypes we show that the interplay of crossover
followed by mutation may serve as a catalyst leading to a sudden
burst of diversity. This leads to improvements of the expected
optimisation time of order $\backslash$(?mega(n/ $\backslash$log
n)$\backslash$) compared to mutation-only algorithms like the
$\backslash$((1+1)$\backslash$) EA.
2016
10.1007/978-3-319-45823-6_83
Proceedings of the 14th {{International Conference
Parallel Problem Solving From Nature}} ({{PPSN}})
Springer
sage-output
http://dx.doi.org/10.1007/978-3-319-45823-6_83
Nominiert für den best paper award.
Duc-Cuong
Dang
Per Kristian
Lehre
Tobias
Friedrich
Timo
Kötzing
Martin S.
Krejca
Pietro S.
Oliveto
Dirk
Sudholt
Andrew M.
Sutton
inproceedings
Dang2016
Escaping Local Optima with
Diversity-Mechanisms and Crossover
2016
Proceedings of the Genetic and Evolutionary
Computation Conference ({GECCO} 2016)
{ACM Press}
645--652
http://dx.doi.org/10.1145/2908812.2908956
Duc-Cuong
Dang
Tobias
Friedrich
Martin S.
Krejca
Timo
Kötzing
Per Kristian
Lehre
Pietro S.
Oliveto
Dirk
Sudholt
Andrew Michael
Sutton
inproceedings
Goldman2016
Runtime Analysis for the Parameter-less Population
Pyramid
2016
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2016)
ACM
669--676
http://dx.doi.org/10.1145/2908812.2908846
Brian W.
Goldman
Dirk
Sudholt
inproceedings
Sudholt2016a
Update Strength in EDAs and ACO: How to Avoid Genetic
Drift
2016
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2016)
ACM
61--68
http://dx.doi.org/10.1145/2908812.2908867
Nominiert für einen best paper award in der Kategorie
\glqq{}Ant Colony Optimization and Swarm Intelligence\grqq{}.
Dirk
Sudholt
Carsten
Witt
inproceedings
Oliveto2016
When Non-Elitism Outperforms Elitism for Crossing Fitness
Valleys
2016
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2016)
ACM
1163-1170
http://dx.doi.org/10.1145/2908812.2908909
Pietro S.
Oliveto
Tiago
Paixão
Jorge
Pérez Heredia
Dirk
Sudholt
Barbora
Trubenova
article
paixao_unified_2015
A Unified Model of Evolutionary Processes
2015
Journal of Theoretical Biology
383
28--43
sage-output-soon
http://dx.doi.org/10.1016/j.jtbi.2015.07.011
Unter den 5 meist heruntergeladenen
Open-Access-Artikeln der Zeitschrift (23. 11. 2015).
Tiago
Paixão
Golnaz
Badkobeh
Nick
Barton
Dogan
Corus
Duc-Cuong
Dang
Tobias
Friedrich
Per Kristian
Lehre
Dirk
Sudholt
Andrew M.
Sutton
Barbora
Trubenova
inproceedings
Badkobeh2015
Black-box Complexity of Parallel Search with Distributed
Populations
2015
Proceedings of Foundations of Genetic Algorithms
(FOGA 2015)
ACM Press
3--15
http://dx.doi.org/10.1145/2725494.2725504
Golnaz
Badkobeh
Per Kristian
Lehre
Dirk
Sudholt
article
Kempka2015
Design and analysis of different alternating variable
searches for search-based software testing
2015
Theoretical Computer Science
605
1--20
http://dx.doi.org/10.1016/j.tcs.2014.12.009
Joseph
Kempka
Phil
McMinn
Dirk
Sudholt
article
mambrini_design_2015
Design and Analysis of Schemes for Adapting Migration
Intervals in Parallel Evolutionary Algorithms
2015
Evolutionary Computation
23
559--582
sage-output-soon
http://dx.doi.org/10.1162/EVCO_a_00153
Andrea
Mambrini
Dirk
Sudholt
inproceedings
Paixao2015
First Steps Towards a Runtime Comparison of
Natural and Artificial Evolution
2015
10.1145/2739480.2754758
Proceedings of the Genetic and {{Evolutionary
Computation Conference}} ({{GECCO}} 2015)
{ACM}
1455--1462
sage-output
http://dx.doi.org/10.1145/2739480.2754758
Tiago
Paixão
Jorge Pérez
Heredia
Dirk
Sudholt
Barbora
Trubenova
inproceedings
Corus2015
On Easiest Functions for Somatic Contiguous
Hypermutations And Standard Bit Mutations
2015
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO~2015)
ACM Press
1399--1406
http://dx.doi.org/10.1145/2739480.2754799
Dogan
Corus
Jun
He
Thomas
Jansen
Pietro S.
Oliveto
Dirk
Sudholt
Christine
Zarges
incollection
Sudholt2012a
Parallel Evolutionary Algorithms
2015
Handbook of Computational Intelligence
Springer
Janusz Kacprzyk and Witold Pedrycz
929--959
http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-662-43504-5
Eingeladener Beitrag. 6180+ Downloads am 7. 9. 2019
Dirk
Sudholt
inproceedings
Nallaperuma2014
A Fixed Budget Analysis of Randomized Search Heuristics
for the Traveling Salesperson Problem
2014
Proceedings of the Genetic and Evolutionary
Computation Conference
(GECCO 2014)
ACM Press
807--814
http://dx.doi.org/10.1145/2576768.2598302
Nominiert f?r einen best paper award in der Kategorie
\glqq{}Genetic Algorithms\grqq{}.
Samadhi
Nallaperuma
Frank
Neumann
Dirk
Sudholt
article
Lassig2014
Analysis of speedups in parallel evolutionaryalgorithms and (1+λ)~EAs for combinatorialoptimization
Evolutionary algorithms are popular heuristics for
solving various combinatorial problems as they are easy to apply
and often produce good results. Island models parallelize evolution
by using different populations, called islands, which are connected
by a graph structure as communication topology. Each island
periodically communicates copies of good solutions to neighboring
islands in a process called migration. We consider the speedup
gained by island models in terms of the parallel running time for
problems from combinatorial optimization: sorting (as maximization
of sortedness), shortest paths and Eulerian cycles. The results
show in which settings and up to what degree evolutionary
algorithms can be parallelized efficiently. Our results include
offspring populations in ( 1 + λ ) {EAs} as a special case.
Potential speedups depend on many design choices such as the search
operators, representations and fitness functions used on the
islands, and also the parameters of the island model. In
particular, we show that a natural instance for Eulerian cycles
leads to exponential vs. logarithmic speedups, depending on the
frequency of migration.
2014
10.1016/j.tcs.2014.06.037
Theoretical Computer Science
551
66--83
Combinatorial optimization, Island model, Offspring
populations, Parallel evolutionary algorithms, runtime analysis,
Spatial structures
http://www.sciencedirect.com/science/article/pii/S0304397514004976
Jörg
Lässig
Dirk
Sudholt
inproceedings
Mambrini2014
Design and Analysis of Adaptive Migration Intervals in
Parallel Evolutionary Algorithms
2014
Proceedings of the Genetic and Evolutionary
Computation Conference
(GECCO 2014)
ACM Press
1047--1054
http://dx.doi.org/10.1145/2576768.2598347
Best paper award in der Kategorie \glqq{}Parallel
Evolutionary Systems\grqq{}.
Andrea
Mambrini
Dirk
Sudholt
article
Lassig2013a
General Upper Bounds on the Running Time of Parallel
Evolutionary Algorithms
2014
Evolutionary Computation
22
MIT Press
405--437
3
http://dx.doi.org/10.1162/EVCO_a_00114
Jörg
Lässig
Dirk
Sudholt
article
Minku2013
Improved Evolutionary Algorithm Design for the Project
Scheduling Problem Based on Runtime Analysis
2014
IEEE Transactions on Software Engineering
40
83--102
1
http://dx.doi.org/10.1109/TSE.2013.52
Zweitmeist heruntergeladener Artikel der Zeitschrift im
März 2014.
Leandro L.
Minku
Dirk
Sudholt
Xin
Yao
inproceedings
Oliveto2014a
On the Runtime Analysis of Fitness Sharing Mechanisms
2014
13th International Conference on Parallel Problem
Solving from Nature
(PPSN 2014)
8672
Springer
LNCS
932-941
http://dx.doi.org/10.1007/978-3-319-10762-2_92
Pietro S.
Oliveto
Dirk
Sudholt
Christine
Zarges
inproceedings
Oliveto2014
On the Runtime Analysis of Stochastic Ageing
Mechanisms
2014
Proceedings of the Genetic and Evolutionary
Computation Conference
(GECCO 2014)
ACM Press
113--120
http://dx.doi.org/10.1145/2576768.2598328
Best paper award in der Kategorie \glqq{}Artificial
Immune Systems\grqq{}.
Pietro S.
Oliveto
Dirk
Sudholt
article
Rowe2013
The choice of the offspring population size in the(1,λ) evolutionary algorithm
2014
http://dx.doi.org/10.1016/j.tcs.2013.09.036
Theoretical Computer Science
545
20--38
http://www.sciencedirect.com/science/article/pii/S030439751300755X
Jonathan E.
Rowe
Dirk
Sudholt
inproceedings
Badkobeh2014
Unbiased Black-Box Complexity of Parallel Search
2014
13th International Conference on Parallel Problem
Solving from Nature
(PPSN 2014)
8672
Springer
LNCS
892-901
http://dx.doi.org/10.1007/978-3-319-10762-2_88
Golnaz
Badkobeh
Per Kristian
Lehre
Dirk
Sudholt
article
Sudholt2012c
A New Method for Lower Bounds on the Running Time of
Evolutionary Algorithms
2013
IEEE Transactions on Evolutionary Computation
17
418-435
3
http://dx.doi.org/10.1109/TEVC.2012.2202241
Dirk
Sudholt
inproceedings
Kempka2013
A Theoretical Runtime and Empirical Analysis of
Different Alternating Variable Searches for Search-Based
Testing
2013
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2013)
ACM
1445--1452
http://dx.doi.org/10.1145/2463372.2463549
Joseph
Kempka
Phil
McMinn
Dirk
Sudholt
article
Lassig2013
Design and Analysis of Migration in Parallel
Evolutionary Algorithms
2013
Soft Computing
17
Springer
1121--1144
http://dx.doi.org/10.1007/s00500-013-0991-0
Sonderheft zum Thema \glqq{}Bio-inspired Algorithms with
Structured
Populations\grqq{} (Annahmequote 14%).
Jörg
Lässig
Dirk
Sudholt
article
monotone-journal
Mutation Rate Matters Even When Optimizing Monotonic
Functions
2013
Evolutionary Computation
21
1--21
1
http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00055
Benjamin
Doerr
Thomas
Jansen
Dirk
Sudholt
Carola
Winzen
Christine
Zarges
inproceedings
Doerr2013
When Do Evolutionary Algorithms Optimize Separable
Functions in Parallel?
2013
Proceedings of the Twelfth Workshop on Foundations of
Genetic Algorithms (FOGA 2013)
ACM
51--64
linear functions, pseudo-boolean optimization, runtime
analysis, separable functions, theory
http://dx.doi.org/10.1145/2460239.2460245
Benjamin
Doerr
Dirk
Sudholt
Carsten
Witt
article
ant-stochastic-journal
A Simple Ant Colony Optimizer for Stochastic Shortest
Path Problems
2012
Algorithmica
64
Springer
643--672
4
http://dx.doi.org/10.1007/s00453-011-9606-2
Dirk
Sudholt
Christian
Thyssen
inproceedings
Sudholt2012
Crossover Speeds Up Building-Block Assembly
2012
Proceedings of the Genetic and Evolutionary
Computation Conference
(GECCO 2012)
689--696
http://dx.doi.org/10.1145/2330163.2330260
Nominiert für einen best paper award in der Kategorie
\glqq{}Genetic Algorithms\grqq{}.
Dirk
Sudholt
inproceedings
Minku2012
Evolutionary Algorithms for the Project Scheduling
Problem: Runtime Analysis and Improved Design
2012
Proceedings of the Genetic and Evolutionary
Computation Conference
(GECCO 2012)
1221--1228
http://dx.doi.org/10.1145/2330163.2330332
Leandro L.
Minku
Dirk
Sudholt
Xin
Yao
inproceedings
Mambrini2012
Homogeneous and Heterogeneous Island Models for the Set
Cover Problem
2012
Parallel Problem Solving from Nature (PPSN 2012)
7491
Springer
LNCS
11--20
http://dx.doi.org/10.1007/978-3-642-32937-1_2
Andrea
Mambrini
Dirk
Sudholt
Xin
Yao
incollection
SudholtHandbook
Parametrization and Balancing Global and Local Search
2012
Handbook of Memetic Algorithms
379
Springer
Studies in Computational Intelligence
Carlos Cotta and Ferrante Neri and Pablo Moscato
http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-23246-6
Eingeladener Beitrag. 1360+ Downloads am 7. 9. 2019
Dirk
Sudholt
article
ant-sp-journal
Running time analysis of Ant Colony Optimization for
shortest path problems
2012
DOI: 10.1016/j.jda.2011.06.002
Journal of Discrete Algorithms
10
165--180
http://dx.doi.org/10.1016/j.jda.2011.06.002
Platz 6 in der Rangliste \glqq{}2011's top downloaded
articles from Journal of Discrete Algorithms\grqq{}
Dirk
Sudholt
Christian
Thyssen
inproceedings
Moraglio2012
Runtime Analysis of Convex Evolutionary Search
2012
Proceedings of the Genetic and Evolutionary
Computation Conference
(GECCO 2012)
http://dx.doi.org/10.1145/2330163.2330255
Best paper award in der Kategorie \glqq{}Genetic
Algorithms\grqq{}.
Alberto
Moraglio
Dirk
Sudholt
inproceedings
Rowe2012
The Choice of the Offspring Population Size in the (1,λ)~EA
2012
Proceedings of the Genetic and Evolutionary
Computation Conference
(GECCO 2012)
http://dx.doi.org/10.1145/2330163.2330350
Nominiert für einen best paper award in der Kategorie
\glqq{}Theory/ESEP\grqq{}.
Jon
Rowe
Dirk
Sudholt
inproceedings
Lassig2010c
Adaptive Population Models for Offspring Populations and
Parallel Evolutionary Algorithms
2011
Proceedings of the 11th Workshop on Foundations of
Genetic Algorithms (FOGA~2011)
ACM Press
181--192
http://dx.doi.org/10.1145/1967654.1967671
Jörg
Lässig
Dirk
Sudholt
inproceedings
Lassig2011a
Analysis of Speedups in Parallel Evolutionary Algorithms
for Combinatorial Optimization
2011
22nd International Symposium on Algorithms and
Computation (ISAAC~2011)
7074
Springer
LNCS
405-414
http://dx.doi.org/10.1007/978-3-642-25591-5_42
Jörg
Lässig
Dirk
Sudholt
inproceedings
Koetzing2011
How Crossover Helps in Pseudo-Boolean Optimization
2011
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO~2011)
ACM Press
989--996
http://dx.doi.org/10.1145/2001576.2001711
Best paper award in der Kategorie \glqq{}Genetic
Algorithms\grqq{}.
Timo
Kötzing
Dirk
Sudholt
Madeleine
Theile
article
Sudholt2010
Hybridizing Evolutionary Algorithms with Variable-Depth
Search to
Overcome Local Optima
2011
Algorithmica
59
343--368
3
http://dx.doi.org/10.1007/s00453-009-9384-2
Dirk
Sudholt
incollection
SudholtTRSH
Memetic Evolutionary Algorithms
2011
Theory of Randomized Search Heuristics --
Foundations and Recent Developments
World Scientific
Series on Theoretical Computer Science
Anne Auger and Benjamin Doerr
1
http://worldscibooks.com/compsci/7438.html
Dirk
Sudholt
inproceedings
Neumann2011
On the Effectiveness of Crossover for Migration in
Parallel Evolutionary Algorithms
2011
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO~2011)
ACM Press
1587--1594
http://dx.doi.org/10.1145/2001576.2001790
Frank
Neumann
Pietro S.
Oliveto
Günter
Rudolph
Dirk
Sudholt
article
Doerr2010a
Runtime Analysis of the 1-ANT Ant Colony Optimizer
2011
Theoretical Computer Science
412
1629--1644
17
http://dx.doi.org/10.1016/j.tcs.2010.12.030
Benjamin
Doerr
Frank
Neumann
Dirk
Sudholt
Carsten
Witt
inproceedings
Koetzing2010
Simple Max-Min Ant Systems and the Optimization of
Linear Pseudo-Boolean Functions
2011
Proceedings of the 11th Workshop on Foundations of
Genetic Algorithms (FOGA~2011)
ACM Press
209--218
http://dx.doi.org/10.1145/1967654.1967673
Timo
Kötzing
Frank
Neumann
Dirk
Sudholt
Markus
Wagner
inproceedings
Sudholt2010c
Using Markov-Chain Mixing Time Estimates for the
Analysis of Ant Colony Optimization
2011
Proceedings of the 11th Workshop on Foundations of
Genetic Algorithms (FOGA~2011)
ACM Press
139--150
http://dx.doi.org/10.1145/1967654.1967667
Dirk
Sudholt
inproceedings
Neumann2010a
A Few Ants are Enough: ACO with Iteration-Best
Update
2010
Proceedings of the Genetic and Evolutionary
Computation Conference
(GECCO 2010)
63--70
http://dx.doi.org/10.1145/1830483.1830493
Nominiert für einen best paper award in der Kategorie
\glqq{}Ant Colony Optimization/Swarm Intelligence\grqq{}.
Frank
Neumann
Dirk
Sudholt
Carsten
Witt
article
SauerwaldTCS
A Self-stabilizing Algorithm for Cut Problems in
Synchronous Networks
2010
Theoretical Computer Science
411
1599--1612
14--15
http://dx.doi.org/10.1016/j.tcs.2010.01.008
Thomas
Sauerwald
Dirk
Sudholt
article
Jansentoappear
Analysis of an Asymmetric Mutation Operator
2010
Evolutionary Computation
18
1--26
1
http://dx.doi.org/10.1162/evco.2010.18.1.18101
Thomas
Jansen
Dirk
Sudholt
inproceedings
Sudholt2010b
Analysis of an Iterated Local Search Algorithm for
Vertex Coloring
2010
21st International Symposium on Algorithms and
Computation (ISAAC 2010)
6506
Springer
LNCS
340--352
http://dx.doi.org/10.1007/978-3-642-17517-6_31
Dirk
Sudholt
Christine
Zarges
inproceedings
Horoba2010
Ant Colony Optimization for Stochastic Shortest Path
Problems
2010
Proceedings of the Genetic and Evolutionary
Computation Conference
(GECCO 2010)
1465--1472
http://dx.doi.org/10.1145/1830483.1830750
Nominiert für einen best paper award in der Kategorie
\glqq{}Theory\grqq{}. Eingeladen zu einem Sonderheft in
\emph{Algorithmica}.
Christian
Horoba
Dirk
Sudholt
inproceedings
Lassig2010b
Experimental Supplements to the Theoretical Analysis of
Migration in the Island Model
2010
11th International Conference on Parallel Problem
Solving from Nature (PPSN~2010)
6238
Springer
LNCS
224--233
http://dx.doi.org/10.1007/978-3-642-15844-5_23
Jörg
Lässig
Dirk
Sudholt
inproceedings
Sudholt2010a
General Lower Bounds for the Running Time of
Evolutionary Algorithms
2010
11th International Conference on Parallel Problem
Solving from Nature (PPSN~2010)
6238
Springer
LNCS
124--133
http://dx.doi.org/10.1007/978-3-642-15844-5_13
Dirk
Sudholt
inproceedings
Lassig2010a
General Scheme for Analyzing Running Times of Parallel
Evolutionary Algorithms
2010
11th International Conference on Parallel Problem
Solving from Nature (PPSN~2010)
6238
Springer
LNCS
234--243
http://dx.doi.org/10.1007/978-3-642-15844-5_24
Best paper award.
Jörg
Lässig
Dirk
Sudholt
inproceedings
Doerr2010
Optimizing Monotone Functions Can Be Difficult
2010
11th International Conference on Parallel Problem
Solving from Nature (PPSN~2010)
6238
Springer
LNCS
42--51
http://dx.doi.org/10.1007/978-3-642-15844-5_5
Benjamin
Doerr
Thomas
Jansen
Dirk
Sudholt
Carola
Winzen
Christine
Zarges
article
Sudholtsubmitteda
Runtime Analysis of a Binary Particle Swarm Optimizer
2010
Theoretical Computer Science
411
2084--2100
21
http://dx.doi.org/10.1016/j.tcs.2010.03.002
Dirk
Sudholt
Carsten
Witt
inproceedings
Lassig2010
The Benefit of Migration in Parallel Evolutionary
Algorithms
2010
Proceedings of the Genetic and Evolutionary
Computation Conference
(GECCO 2010)
1105--1112
http://dx.doi.org/10.1145/1830483.1830687
Best paper award in der Kategorie \glqq{}Parallel
Evolutionary Systems\grqq{}.
Jörg
Lässig
Dirk
Sudholt
article
Neumann2009
Analysis of Different MMAS ACO Algorithms on
Unimodal Functions
and Plateaus
2009
Swarm Intelligence
3
35--68
1
http://dx.doi.org/10.1007/s11721-008-0023-3
Sonderheft zum Thema \glqq{}Ant colony
optimization\grqq{} (Annahmequote 16%).
Frank
Neumann
Dirk
Sudholt
Carsten
Witt
article
Friedrichsubmitted
Analysis of Diversity-Preserving Mechanisms for Global
Exploration
2009
Evolutionary Computation
17
455--476
4
http://dx.doi.org/10.1162/evco.2009.17.4.17401
Tobias
Friedrich
Pietro S.
Oliveto
Dirk
Sudholt
Carsten
Witt
incollection
Neumann2009a
Computational Complexity of Ant Colony Optimization and
its Hybridization
with Local Search
2009
Innovations in Swarm Intelligence
Springer
SGI
Chee Peng Lim and Lakhmi C. Jain and Satchidananda
Dehuri
248
http://www.springer.com/engineering/book/978-3-642-04224-9
Frank
Neumann
Dirk
Sudholt
Carsten
Witt
inproceedings
Horoba2009a
Running Time Analysis of ACO Systems for Shortest Path
Problems
2009
Proceedings of Engineering Stochastic Local Search
Algorithms (SLS~'09)
5752
Springer
LNCS
76--91
http://dx.doi.org/10.1007/978-3-642-03751-1_6
Christian
Horoba
Dirk
Sudholt
article
Sudholtsubmitted
The Impact of Parametrization in Memetic Evolutionary
Algorithms
2009
Theoretical Computer Science
410
2511--2528
26
http://dx.doi.org/10.1016/j.tcs.2009.03.003
Dirk
Sudholt
thesis
Sudholt2008thesis
Computational Complexity of Evolutionary Algorithms,
Hybridizations, and Swarm Intelligence
Dissertation
2008
1
Technische Universität Dortmund
http://hdl.handle.net/2003/25954
Dirk
Sudholt
inproceedings
Sudholt2008
Memetic Algorithms with Variable-Depth Search to
Overcome Local Optima
2008
Proceedings of the Genetic and Evolutionary
Computation Conference
{(GECCO 2008)}
ACM Press
787-794
http://doi.acm.org/10.1145/1389095.1389251
Nominiert für einen best paper award in der Kategorie
\glqq{}Formal Theory\grqq{}. Eingeladen zu einem Sonderheft in
\emph{Algorithmica}.
Dirk
Sudholt
inproceedings
Neumann2008a
Rigorous Analyses for the Combination of Ant Colony
Optimization
and Local Search
2008
Proceedings of the Sixth International Conference on
Ant Colony Optimization
and Swarm Intelligence (ANTS~'08)
5217
Springer
LNCS
132--143
http://dx.doi.org/10.1007/978-3-540-87527-7_12
Frank
Neumann
Dirk
Sudholt
Carsten
Witt
inproceedings
Sudholt2008c
Runtime Analysis of Binary PSO
2008
Proceedings of the Genetic and Evolutionary
Computation Conference
{(GECCO 2008)}
ACM Press
135--142
http://doi.acm.org/10.1145/1389095.1389114
Dirk
Sudholt
Carsten
Witt
inproceedings
Sauerwald2008a
Self-stabilizing Cuts in Synchronous Networks
2008
International Colloquium on Structural Information
and Communication
Complexity (SIROCCO~'08)
5058
Springer
LNCS
234--246
http://dx.doi.org/10.1007/978-3-540-69355-0_20
Eingeladen zu einem Sonderheft in \emph{Theoretical
Computer Science}.
Thomas
Sauerwald
Dirk
Sudholt
inproceedings
Friedrich2008a
Theoretical Analysis of Diversity Mechanisms for Global
Exploration
2008
Proceedings of the Genetic and Evolutionary
Computation Conference
{(GECCO 2008)}
ACM Press
945--952
http://doi.acm.org/10.1145/1389095.1389276
Best paper award in der Kategorie \glqq{}Genetic
Algorithms\grqq{}.
Tobias
Friedrich
Pietro S.
Oliveto
Dirk
Sudholt
Carsten
Witt
inproceedings
Neumann2007
Comparing Variants of MMAS ACO Algorithms on
Pseudo-Boolean
Functions
2007
Proceedings of Engineering Stochastic Local Search
Algorithms (SLS~'07)
4638
Springer
LNCS
61--75
http://dx.doi.org/10.1007/978-3-540-74446-7_5
Frank
Neumann
Dirk
Sudholt
Carsten
Witt
inproceedings
Doerr2007b
On the Runtime Analysis of the 1-ANT ACO
Algorithm
2007
Proceedings of the Genetic and Evolutionary
Computation Conference
{(GECCO 2007)}
ACM Press
33--40
http://doi.acm.org/10.1145/1276958.1276964
Best paper award in der Kategorie \glqq{}Ant Colony
Optimization, Swarm Intelligence, and Artificial Immune
Systems\grqq{}.
Benjamin
Doerr
Frank
Neumann
Dirk
Sudholt
Carsten
Witt
inproceedings
Sudholt2006
Local Search in Evolutionary Algorithms: the Impact of
the Local
Search Frequency
2006
Proceedings of the 17th International Symposium on
Algorithms and
Computation (ISAAC~'06)
4288
Springer
LNCS
359-368
http://dx.doi.org/10.1007/11940128_37
Dirk
Sudholt
inproceedings
Sudholt2006a
On the Analysis of the (1+1)~Memetic Algorithm
2006
Proceedings of the Genetic and Evolutionary
Computation Conference
{(GECCO 2006)}
ACM Press
493-500
http://doi.acm.org/10.1145/1143997.1144087
Dirk
Sudholt
inproceedings
Sudholt2005
Crossover is Provably Essential for the Ising Model on
Trees
2005
Proceedings of the Genetic and Evolutionary
Computation Conference
{(GECCO 2005)}
ACM Press
1161-1167
http://doi.acm.org/10.1145/1068009.1068202
Dirk
Sudholt
inproceedings
Jansen2005
Design and analysis of an asymmetric mutation
operator
2005
Proceedings of the IEEE Congress on Evolutionary
Computation (CEC~'05)
IEEE Press
497--504
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1554684
Thomas
Jansen
Dirk
Sudholt
thesis
Sudholt2004
Evolutionäre Algorithmen als
Adaptationsschemata
Diplomarbeit
2004
1
Universität Dortmund
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Dirk
Sudholt
inproceedings
Briest2004e
Experimental Supplements to the Theoretical Analysis of
EAs on
Problems from Combinatorial Optimization
2004
Parallel Problem Solving From Nature (PPSN VIII)
3242
Springer
LNCS
21--30
http://dx.doi.org/10.1007/978-3-540-30217-9_3
Patrick
Briest
Dimo
Brockhoff
Sebastian
Degener
Matthias
Englert
Christian
Gunia
Oliver
Heering
Thomas
Jansen
Michael
Leifhelm
Kai
Plociennik
Heiko
Röglin
Andrea
Schweer
Dirk
Sudholt
Stefan
Tannenbaum
Ingo
Wegener
inproceedings
Briest2004d
The Ising Model: Simple Evolutionary Algorithms as
Adaptation Schemes
2004
Parallel Problem Solving From Nature (PPSN VIII)
3242
Springer
LNCS
31--40
http://dx.doi.org/10.1007/978-3-540-30217-9_4
Patrick
Briest
Dimo
Brockhoff
Sebastian
Degener
Matthias
Englert
Christian
Gunia
Oliver
Heering
Thomas
Jansen
Michael
Leifhelm
Kai
Plociennik
Heiko
Röglin
Andrea
Schweer
Dirk
Sudholt
Stefan
Tannenbaum
Ingo
Wegener