The objective of this paper is to demonstrate that unique alternative designs can be efficiently found by searching the discarded data (or graveyard) from a multiobjective genetic algorithm (MOGA). Motivation for using graveyard data to generate design alternatives arises from the computational cost associated with real-time design space exploration of multiobjective optimization problems. The effectiveness of this approach is explored by comparing (1) the uniqueness of alternatives found using graveyard data and those generated using an optimization-based search, and (2) how alternative generation near the Pareto frontier is impacted. Two multiobjective case study problems are introduced—a two bar truss and an I-beam design optimization. Results from these studies indicate that using graveyard data allows for the discovery of alternative designs that are at least 70% as unique as alternatives found using an optimization-based alternative identification approach, while saving a significant number of functional evaluations. Additionally, graveyard data are shown to be better suited for alternative generation near the Pareto frontier than standard sampling techniques. Finally, areas of future work are also discussed.

References

References
1.
Otto
,
K. N.
, and
Antonsson
,
E. K.
,
1991
, “
Trade-Off Strategies in Engineering Design
,”
Res. Eng. Des.
,
3
(
2
), pp.
87
103
.10.1007/BF01581342
2.
Mattson
,
C. A.
, and
Messac
,
A.
,
2005
, “
Pareto Frontier Based Concept Selection Under Uncertainty, With Visualization
,”
Optim. Eng.
, Kluwer Publishers—Special Issue on Multidisciplinary Design Optimization, Invited Paper,
6
(
1
), pp.
85
115
.10.1023/B:OPTE.0000048538.35456.45
3.
Gurnani
,
A.
,
Ferguson
,
S.
,
Donndelinger
,
J.
, and
Lewis
,
K.
,
2005
, “
A Constraint-Based Approach to Feasibility Assessment in Conceptual Design
,”
Artif. Intell. Eng. Des.
, Anal. Manuf., Special Issue on constraints and Design,
20
(
4
), pp.
351
367
.1017/S0890060406060252
4.
Stump
,
G.
,
Yukish
,
M.
, and
Simpson
,
T. W.
,
2004
, “
The ARL Trade Space Visualizer: An Engineering Decision-Making Tool
,”
Proceedings of the Tenth AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
,
Albany
,
NY
, Paper No. AIAA-2004-4568.
5.
Ross
,
A. M.
,
Hastings
,
D. E.
,
Warmkessel
,
J. M.
, and
Diller
,
N. P.
2004
, “
Multi-Attribute Tradespace Exploration as Front End for Effective Space System Design
,”
J. Spacecr. Rockets
,
41
(
1
), pp.
20
28
.10.2514/1.9204
6.
Stump
,
G.
,
Lego
,
S.
,
Yukish
,
M.
,
Simpson
,
T. W.
, and
Donndelinger
,
J. A.
,
2009
, “
Visual Steering Commands for Trade Space Exploration: User-Guided Sampling With Example
,”
J. Comput. Inf. Sci. Eng.
,
9
(
4
), p.
044501
.10.1115/1.3243633
7.
Daskilewicz
,
M. J.
, and
German
,
B. J.
,
2012
, “
Rave: A Computational Framework to Facilitate Research in Design Decision Support
,”
J. Comput. Inf. Sci. Eng.
,
12
(
2
), p.
021005
.10.1115/1.4006464
8.
See
,
T. K.
,
Gurnani
,
A.
, and
Lewis
,
K.
,
2004
, “
Multi-Attribute Decision Making Using Hypothetical Equivalents and Inequivalents
,”
J. Mech. Des.
,
126
(
6
), pp.
950
959
.10.1115/1.1814389
9.
See
,
T. K.
, and
Lewis
,
K.
,
2006
, “
A Formal Approach to Handling Conflicts in Multiattribute Group Decision Making
,”
J. Mech. Des.
,
128
(
4
), pp.
678
689
.10.1115/1.2197836
10.
Hollingsworth
,
P. M.
,
2004
, “
Requirements Controlled Design: A Method for Discovery of Discontinuous System Boundaries in the Requirements Hyperspace
,” Ph.D. thesis,
Georgia Tech
,
Atlanta, GA
.
11.
Ross
,
A. M.
, and
Hastings
,
D. E.
,
2006
, “
Assessing Changeability in Aerospace Systems Architecting and Design Using Dynamic Multi-Attribute Tradespace Exploration
,” AIAA Space 2006, San Jose, CA, Paper No. AIAA-2006-7255.
12.
Hazelrigg
,
G. A.
,
1998
, “
A Framework for Decision-Based Engineering Design
,”
J. Mech. Des.
,
120
(
4
), pp.
653
658
.10.1115/1.2829328
13.
Balling
,
R.
,
1999
, “
Design by Shopping: A New Paradigm?
,”
Proceedings of the Third World Congress of Structural and Multidisciplinary Optimization WCSMO-3
,
Buffalo, NY
,
University at Buffalo
, pp.
295
297
.
14.
Winer
,
E. H.
, and
Bloebaum
,
C. L.
,
2001
, “
Visual Design Steering for Optimization Solution Improvement
,”
Struct. Multidisc. Optim.
,
22
(
3
), pp.
219
229
.10.1007/s001580100139
15.
Winer
,
E. H.
, and
Bloebaum
,
C. L.
,
2002
, “
Development of Visual Design Steering as an Aid in Large-Scale Multidisciplinary Design Optimization. Part I: Method Development
,”
Struct. Multidisc. Optim.
,
23
(
6
), pp.
412
424
.10.1007/s00158-002-0203-5
16.
Brill
,
E. D.
, Jr
.,
1979
, “
The Use of Optimization Models in Public-Sector Planning
,”
Manage. Sci.
,
25
(
5
), pp.
413
422
.10.1287/mnsc.25.5.413
17.
Chang
,
S-Y.
,
Brill
,
E. D.
, Jr.
, and
Hopkins
,
L. D.
,
1983
, “
Modeling to Generate Alternatives: A Fuzzy Approach
,”
Fuzzy Sets Syst.
,
9
(
1–3
), pp.
137
151
.10.1016/S0165-0114(83)80014-1
18.
Huang
,
G. H.
,
Gaetz
,
B. W.
, and
Patry
,
G. G.
,
1996
, “
A Grey Hop, Skip, and Jump Approach: Generating Alternatives for Expansion Planning of Waste Management Facilities
,”
Can. J. Civil Eng.
,
23
(
6
), pp.
1207
1219
.10.1139/l96-930
19.
DeCarolis
,
J. F.
,
2011
, “
Using Modeling to Generate Alternatives (MGA) to Expand Our Thinking on Energy Futures
,”
Energy Econ.
,
33
(
2
), pp.
145
152
.10.1016/j.eneco.2010.05.002
20.
Nunez
,
M.
,
Datta
,
V. C.
,
Molina-Cristobal
,
A.
,
Guenov
,
M.
, and
Riaz
,
A.
,
2012
, “
Enabling Exploration in the Conceptual Design and Optimisation of Complex Systems
,”
J. Eng. Des.
,
23
(
10–11
), pp.
849
872
.10.1080/09544828.2012.706800
21.
Jeon
,
Y. H.
,
Jun
,
S.
,
Kang
,
S.
, and
Lee
,
D. H.
,
2012
, “
Systematic Design Space Exploration and Rearrangement of the MDO Problem by Using Probabilistic Methodology
,”
J. Mech. Sci. Technol.
,
26
(
9
), pp.
2825
2836
.10.1007/s12206-012-0735-6
22.
Rahman
,
S.
, and
Shrestha
,
G.
,
1992
, “
A Technique to Incorporate New Information in Evaluating Generation Alternatives
,”
IEEE Trans. Power Syst.
,
7
(
2
), pp.
900
906
.10.1109/59.141802
23.
Daniel
,
H. L.
,
Ranjithan
,
S. R.
,
Brill
,
E. D.
, Jr
.
, and
Baugh
,
J. W.
, Jr.
,
2001
, “
Genetic Algorithm Approaches for Addressing Unmodeled Objectives in Optimization Problems
,”
Eng. Optim.
,
33
(
5
), pp.
549
569
.10.1080/03052150108940933
24.
Neches
,
R.
, and
Madni
,
A. M.
,
2012
, “
Towards Affordably Adaptable and Effective Systems
,”
Syst. Eng.
,
16
, pp.
224
234
.10.1002/sys.21234
25.
Simpson
,
T. W.
, and
Martins
,
J. R.
,
2011
, “
Multidisciplinary Design Optimization for Complex Engineered Systems: Report From a National Science Foundation Workshop
,”
ASME J. Mech. Des.
,
133
(
10
), p.
101002
.10.1115/1.4004465
26.
Madni
,
A. M.
,
Brenner
,
M. A.
,
Costea
,
I.
,
MacGregor
,
D.
, and
Meshkinpour
,
F.
,
1985
, “
Option Generation: Problems, Principles, and Computer-Based Aiding
,”
Proceedings of the 1985 International Conference on Systems
, Man, and Cybernetics, Tuscon, AZ, pp.
757
760
.
27.
Goldberg
,
D. E.
,
1989
,
Genetic Algorithms in Search, Optimization, and Machine Learning
,
Addison-Wesley
,
New York, NY
.
28.
Deb
,
K.
,
Pratap
,
A.
,
Agarwal
,
S.
, and
Meyarivan
,
T.
,
2002
, “
A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II
,”
IEEE Trans. Evol. Comput
,
6
(
2
), pp.
182
197
.10.1109/4235.996017
29.
Deb
,
K.
,
2009
,
Multi-Objective Optimization Using Evolutionary Algorithms
,
Wiley
,
New York
, NY.
30.
Mackenna
,
A.
,
2011
, “
Rapid Ship Design Environment
,”
Physics-Based Modeling in Design and Development for U.S. Defense Conference
,”
Denver
, CO, Paper No. 13623.
31.
Simpson
,
T. W.
,
Rosen
,
D.
,
Allen
,
J. K.
, and
Mistree
,
F.
,
1996
, “
Metrics for Assessing Design Freedom and Information Certainty in the Early Stages of Design
,”
Proceedings of the 1996 ASME DETC and CIE
,
Irving
,
CA
, Paper No. 96-DETC/DTM-1521.
32.
Finch
,
W. W.
, and
Ward
,
A. C.
,
1997
, “
A Set-Based System for Eliminating Infeasible Designs in Engineering Problems Dominated by Uncertainty
,”
Proceedings of the 1997 ASME Design Engineering Technical Conferences
,
Sacramento
,
CA
, Paper No. DETC97/DTM-3886.
33.
Chen
,
W.
, and
Lewis
,
K.
,
1999
, “
A Robust Design Approach for Achieving Flexibility in Multidisciplinary Design
,”
AIAA J.
,
37
, pp.
982
989
.10.2514/2.805
34.
Shan
,
S.
, and
Wang
,
G. G.
,
2004
, “
Space Exploration and Global Optimization for Computationally Intensive Design Problems: a Rough Set Based Approach
,”
Struct. Multidisc. Optim.
,
28
(
6
), pp.
427
441
.10.1007/s00158-004-0448-2
35.
Madhavan
,
K.
,
Shahan
,
D.
,
Seepersad
,
C. C.
,
Hlavinka
,
D. A.
, and
Benson
,
W.
,
2008
, “
An Industrial Trial of a Set-Based Approach to Collaborative Design
,”
Proceedings of the IDETC/CIE 2008
,
New York
,
NY
, Paper No. DETC2008/49953.
36.
Campbell
,
M.
, “
The Skewboid Method: A Simple and Effective Approach to Pareto Relaxation and Filtering
,”
2012
, ASME IDETC, Chicago, IL, Paper No. DETC2012-70323.
37.
Messac
,
A.
,
Martinez
,
M. P.
, and
Simpson
,
T. W.
,
2002
, “
Introduction of a Product Family Penalty Function Using Physical Programming
,”
ASME J. Mech. Des.
,
124
(
2
), pp.
164
172
.10.1115/1.1467602
38.
Chen
,
W.
,
Sahai
,
A.
,
Messac
,
A.
, and
Sundararaj
,
G. J.
,
2000
, “
Exploration of the Effectiveness of Physical Programming in Robust Design
,”
ASME J. Mech. Des.
,
122
(
2
), pp.
155
163
.10.1115/1.533565
39.
Liu
,
H.
,
Chen
,
W.
,
Scott
,
M. J.
, and
Qureshi
,
K.
,
2008
, “
Determination of Ranged Sets of Design Specifications by Incorporating Design-Space Heterogeneity
,”
Eng. Optim.
,
40
(
11
), pp.
1011
1029
.10.1080/03052150802378558
40.
Bertsche
,
B.
,
2008
,
Reliability in Automotive and Mechanical Engineering: Determination of Component and System Reliability
,
Springer-Verlag
,
New York
, NY.
41.
Gunawan
,
S.
, and
Azarm
,
S.
,
2005
, “
A Feasibility Robust Optimization Method Using Sensitivity Region Concept
,”
J. Mech. Des.
,
127
(
5
), pp.
858
865
.10.1115/1.1903000
42.
Li
,
M.
,
Williams
,
N.
, and
Azarm
,
S.
,
2009
, “
Interval Uncertainty Reduction and Single-Disciplinary Sensitivity Analysis With Multi-Objective Optimization
,”
J. Mech. Des.
,
131
(
3
), p.
031007
.10.1115/1.3066736
43.
Eddy
,
J.
, and
Lewis
,
K.
,
2002
, “
Visualization of Multi-Dimensional Design and Optimization Data Using Cloud Visualization
,”
Proceedings of the 2002 ASME Design Engineering Technical Conferences
,
Montreal
,
Canada
, Paper No. DETC2002/DAC-34130.
44.
Shneiderman
B.
,
1992
, “
Tree Visualization with Tree-Maps: 2-D Space-Filling Approach
,”
ACM Trans. Graph.
,
11
(
1
), pp.
92
99
.10.1145/102377.115768
45.
Honeycomb™
,” http://www.hivegroup.com/
46.
Chien
,
S.-F.
, and
Flemming
,
U.
,
2002
, “
Design Space Navigation in Generative Design Systems
,”
Autom. Constr.
,
11
(
1
), pp.
1
22
.10.1016/S0926-5805(00)00084-4
47.
Aksit
,
M.
, and
Marcelloni
,
F.
,
2001
, “
Deferring Elimination of Design Alternatives in Object-Oriented Methods
,”
Concurrency Computation: Pract. Exper.
,
13
(
14
), pp.
1247
1279
.10.1002/cpe.611
48.
Ahn
,
B. S.
, and
Park
,
H.
,
2008
, “
An Efficient Pruning Method for Decision Alternatives of OWA Operators
,”
IEEE Trans. Fuzzy Syst.
,
16
(
6
), pp.
1542
1549
.10.1109/TFUZZ.2008.2005012
49.
Stump
,
G. M.
,
Yukish
,
M.
, and
Merenich
,
J. J.
,
2005
, “
Tracing Interesting Features in Trade Spaces to Conceptual Model Design Rules
,”
2005 IEEE Aerospace Conference
, Vols.
1–4
, pp.
4227
4238
.
50.
Malakooti
,
B.
, and
Raman
,
V.
,
2000
, “
Clustering and Selection of Multiple Criteria Alternatives Using Unsupervised and Supervised Neural Networks
,”
J. Intell. Manuf.
,
11
(
5
), pp.
435
451
.10.1023/A:1008934512672
51.
Chen
,
Y.
,
Kilgour
,
D. M.
, and
Hipel
,
K. W.
,
2012
, “
A Decision Rule Aggregation Approach to Multiple Criteria-Multiple Participant Sorting
,”
Group Decis. Negotiation
,
21
(
5
), pp.
727
745
.10.1007/s10726-011-9246-6
52.
Malak
,
R.
,
2008
, “
Using Parameterized Efficient Sets to Model Alternatives for Systems Design Decisions
,” Ph.D. thesis,
Georgia Tech
,
Atlanta, GA
.
53.
Kurtoglu
,
T.
, and
Campbell
,
M. I.
,
2009
, “
An Evaluation Scheme for Assessing the Worth of Automatically Generated Design Alternatives
,”
Res. Eng. Des.
,
20
(
1
), pp.
59
76
.10.1007/s00163-008-0062-1
54.
Kim
,
J. K.
, and
Kim
,
T. G.
,
2006
, “
A Plan-Generation-Evaluation Framework for Design Space Exploration of Digital Systems Design
,”
IEICE Trans. Fundam. Electron., Commun. Comput. Sci.
,
E89-A
(
3
), pp.
772
781
.10.1093/ietfec/e89-a.3.772
55.
Halim
,
I.
, and
Srinivasan
,
R.
,
2008
, “
Designing Sustainable Alternatives for Batch Operations Using an Intelligent Simulation-Optimization Framework
,”
Chem. Eng. Res. Des.
,
86
(
7A
), pp.
809
822
.10.1016/j.cherd.2008.02.015
56.
Smith
,
G.
,
Richardson
,
J.
,
Summers
,
J. D.
, and
Mocko
,
G. M.
,
2012
, “
Concept Exploration Through Morphological Charts: An Experimental Study
,”
ASME J. Mech. Des.
,
134
(
5
), p.
051004
.10.1115/1.4006261
57.
Krishnan
,
V.
, and
Katkoori
,
S.
,
2006
, “
A Genetic Algorithm for the Design Space Exploration of Datapaths During High-Level Synthesis
,”
IEEE Trans. Evol. Comput.
,
10
(
3
), pp.
213
229
.10.1109/TEVC.2005.860764
58.
Collignan
,
A.
,
Sebastian
,
P.
,
Pailhes
,
J.
, and
Ledoux
,
Y.
,
2012
, “
Arc-Elasticity and Hierarchical Exploration of the Neighborhood of Solutions in Mechanical Design
,”
Adv. Eng. Inf.
,
26
(
3
), pp.
603
617
.10.1016/j.aei.2012.04.001
59.
Shir
,
O.
,
Preuss
,
M.
,
Naujoks
,
B.
, and
Emmerich
,
M.
,
2009
, “
Enhancing Decision Space Diversity in Evolutionary Multiobjective Algorithms
,”
Evol. Multi-Criterion Optim.
,
5467
, pp.
95
109
.10.1007/978-3-642-01020-0
60.
Toffolo
,
A.
, and
Benini
,
E.
,
2003
, “
Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms
,”
Evol. Comput.
,
11
(
2
), pp.
151
167
.10.1162/106365603766646816
61.
Tamaskar
,
S.
,
Neema
,
K.
,
Kotegawa
,
T.
, and
DeLaurentis
,
D.
,
2011
, “
Complexity Enables Design Space Exploration
,”
Conference Proceedings—IEEE International Conference on Systems
, Man, and Cybernetics, pp.
1250
1255
.
62.
Kang
,
E.
,
Jackson
,
E.
, and
Schulte
,
W.
,
2011
, “
An Approach for Effective Design Space Exploration
,”
Lect. Notes Comput. Sci.
,
6662
, pp.
33
54
.10.1007/978-3-642-21292-5
63.
Ferguson
,
S.
,
Gurnani
,
A.
,
Donndelinger
,
J.
, and
Lewis
,
K.
,
2005
, “
A Study of Convergence and Mapping in Multibobjective Optimization Problems
,”
Int. J. Veh. Syst. Model. Test.
,
1
(
1/2/3
), pp.
192
215
.10.1504/IJVSMT.2005.008579
64.
Simon
,
P. R.
, and
Ferguson
,
S.
,
2010
, “
Investigating the Significance of ‘One-to-Many’ Mappings in Multiobjective Optimization
,”
2010 ASME Design Engineering Technical Conferences
, Montreal, Quebec, Paper No. DETC2010-28689.
65.
Brill
,
E. D.
,
Chang
,
S.-Y.
, and
Hopkins
,
L. D.
,
1982
, “
Modeling to Generate Alternatives: The HSJ Approach and an Illustration Using a Problem in Land Use Planning
,”
Manage. Sci.
,
28
(
3
), pp.
221
235
.10.1287/mnsc.28.3.221
66.
Kripakaran
,
P.
, and
Gupta
,
A.
,
2006
, “
MGA–A Mathematical Approach to Generate Design Alternatives
,”
Lect. Notes Comput. Sci.
,
4200
, pp.
408
415
.10.1007/11888598
67.
Zechman
,
E. M.
, and
Ranjithan
,
R. S.
,
2007
, “
Generating Alternatives Using Evolutionary Algorithms for Water Resources and Environmental Management Problems
,”
J. Water Resour. Plann. Manage.
,
133
(
2
), pp.
156
165
.10.1061/(ASCE)0733-9496(2007)133:2(156)
68.
Foster
,
G.
,
2011
, “
Expansion of Alternative Generation Techniques
,” Master’s thesis,
North Carolina State University
,
Raleigh, NC
.
69.
Foster
,
G.
, and
Ferguson
,
S.
,
2010
, “
Exploring the Impact of Distance Metrics on Alternative Generation in a Multiobjective Problem
,”
13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference
,
Fort Worth, TX
, Paper No. AIAA-2010-9091.
70.
Foster
,
G.
, and
Ferguson
,
S.
,
2011
, “
Assessing the Effectiveness of Using Graveyard Data for Generating Design Alternatives
,”
Proceedings of the 2011 ASME Design Engineering Technical Conference
,
Washington, DC
, Paper No. DETC2011-48636.
71.
Sofge
,
D. A.
,
2002
, “
Using Genetic Algorithm Based Variable Selection to Improve Neural Network Models for Real-World Systems
,”
Proceedings of the 2002 International Conference on Machine Learning and Applications
.
72.
Simpson
,
T. W.
,
Poplinski
,
J. D.
,
Koch
,
P. N.
, and
Allen
,
J. K.
,
2001
, “
Metamodels for Computer-Based Engineering Design: Survey and Recommendations
,”
Eng. Comput.
,
17
(
2
), pp.
129
150
.10.1007/PL00007198
73.
Turner
,
C. J.
,
2011
, “
Metamodeling in Product and Process Design
,”
Proceedings of the 2011 IDETC and CIE
,
Washington, DC
, Paper No. DETC2011-47483.
74.
Viana
,
F. A. C.
,
Gogu
,
C.
, and
Haftka
,
R. T.
,
2010
, “
Making the Most Out of Surrogate Models: Tricks of the Trade
,”
Proceedings of the 2010 ASME IDETC and CIE
,
Montreal
,
Canada
, Paper No. DETC2010-28813.
75.
The MathWorks, Inc.
, “
Find Minimum of Function Using Pattern Search—MATLAB
,” http://www.mathworks.com/help/toolbox/gads/patternsearch.html
76.
Audet
,
C.
, and
Dennis
,
J. E.
, Jr.
,
2006
,
Analysis of Generalized Pattern Searches
, DTIC Document.
77.
Kolda
,
T. G.
,
Lewis
,
R. M.
, and
Torczon
,
V.
,
2003
, “
Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods
,”
SIAM Rev.
,
45
, pp.
385
482
.10.1137/S003614450242889
78.
The MathWorks, Inc.
, “
Genetic Algorithm Options: Options Reference (Global Optimization Toolbox)
,” http://www.mathworks.com/help/toolbox/gads/f6174dfi10.html
79.
Azarm
,
S.
,
Reynolds
,
B. J.
, and
Narayanan
,
S.
,
1999
, “
Comparison of Two Multiobjective Optimization Techniques With and Within Genetic Algorithms
,”
Proceedings of the 1999 ASME Design Engineering Technical Conferences
,
Las Vegas, NV
, Paper No. DETC99/DAC-8584.
80.
Hacker
,
K.
, and
Lewis
,
K.
,
2002
, “
Robust Design Through the Use of a Hybrid Genetic Algorithm
,”
Proceedings of the 2002 DETC and CIE Conference
,
Montreal, Canada
, Paper No. DETC2002/DAC-34108.
81.
The MathWorks, Inc.
, “
Find Minimum of Unconstrained Multivariable Function Using Derivative-Free Method—MATLAB
,” http://www.mathworks.com/help/techdoc/ref/fminsearch.html
82.
The MathWorks, Inc.
, “
Find Minimum of Function Using Genetic Algorithm—MATLAB
,” http://www.mathworks.com/help/toolbox/gads/ga.html
83.
The MathWorks, Inc.
, “
Find Minimum of Constrained Nonlinear Multivariable Function—MATLAB
,” http://www.mathworks.com/help/toolbox/optim/ug/fmincon.html
84.
Tang
,
B.
,
1993
, “
Orthogonal Array-Based Latin Hypercubes
,”
J. Am. Stat. Assoc.
,
88
(
424
), pp.
1392
1397
.10.1080/01621459.1993.10476423
85.
Nievergelt
,
J.
,
2000
, “
Exhaustive Search, Combinatorial Optimization and Enumeration: Exploring the Potential of Raw Computing Power
,”
Lect. Notes Comput. Sci.
,
1963
, pp.
18
35
.10.1007/3-540-44411-4
You do not currently have access to this content.