2013

Generalized Decomposition
I. Giagkiozis, R.C. Purshouse, and P.J. Fleming
The 7th International Conference on Evolutionary Multi-Criterion Optimization 2013 (To appear)

[Abstract][BibTeX] [PDF]

@incollection {giagkiozis2013gendecomp,
author = {Giagkiozis, I. and Purshouse, R.C. and Fleming, P.J.},
title = {Generalized Decomposition},
booktitle = {Evolutionary Multi-Criterion Optimization},
series = {Lecture Notes in Computer Science},
publisher = {Springer Berlin},
isbn = {},
pages = {},
volume = {},
year = {2013}
}
Decomposition-based algorithms seem promising for manyobjective optimization problems. However, the issue of selecting a set of weighting vectors for more than two objectives is still unresolved and ad-hoc methods are predominantly used. In the present work, a novel concept is introduced which we call generalized decomposition. Generalized decomposition enables the analyst to adapt the generated distribution of Pareto optimal points, according to the preferences of the decision maker. Also it is shown that generalized decomposition unifies the three performance objectives in multi-objective optimization algorithms to only one, that of convergence to the Pareto front.

Whatever works best for you - a new method for a priori and progressive multi-objective optimisation
Rui Wang, R.C. Purshouse, and P.J. Fleming
International Conference of Evolutionary Multi-Criterion Optimization 2013 (To appear)

[Abstract][BibTeX]

@incollection{giagkiozis2013gendecomp,
author = {Wang, Rui and Purshouse, R.C. and Fleming, P.J.},
title = {Whatever works best for you - a new method for a priori and progressive multi-objective optimisation},
booktitle = {Evolutionary Multi-Criterion Optimization},
series = {Lecture Notes in Computer Science},
publisher = {Springer Berlin},
isbn = {},
pages = {},
volume = {},
year = {2013}
}
Various multi-objective evolutionary algorithms (MOEAs) have been developed to help a decision maker (DM) search for his/her preferred solutions to multi-objective problems. However, none of these approaches has catered simultaneously for the two fundamental ways that DM can specify his/her preferences: weights and aspiration levels. In this paper, we propose an approach named iPICEA-g that allows the DM to specify his preference in either format. iPICEA-g is based on the preference-inspired co-evolutionary algorithm (PICEA-g). Solutions are guided toward regions of interest (ROIs) to the DM by co-evolving sets of goal vectors exclusively generated in the ROIs. Moreover, a friendly deci- sion making technique is developed for interaction with the optimization process: the DM speci es his preferences easily by interactively brushing his preferred regions in the objective space. No direct elicitation of num- bers is required, reducing the cognitive burden on DM. The performance of iPICEA-g is tested on a set of benchmark problems and is shown to be good.

Adjusting for unrecorded consumption in survey and per capita sales data: Quantification of impact on gender-and age-specific alcohol-attributable fractions for oral and pharyngeal cancers in Great Britain
Meier P.S., Meng Y., Holmes J., Baumberg B., Purshouse R.C., Hill-McManus D., and Brennan A.
Alcohol and Alcoholism (to appear)

[BibTeX]

@article{meier2013adjusting,
title={Adjusting for unrecorded consumption in survey and per capita sales data: Quantification of impact on gender-and age-specific alcohol-attributable fractions for oral and pharyngeal cancers in Great Britain},
author={Meier, P.S. and Meng, Y. and Holmes, J. and Baumberg, B. and Purshouse, R. and Hill-McManus, D. and Brennan, A.},
journal={Alcohol and Alcoholism},
year={2013},
publisher={Med Council on Alcohol}
}

Modelling the cost-effectiveness of alcohol screening and brief interventions in primary care in England
Purshouse R.C., Brennan A., Rafia R., Latimer N.R., Archer R.J., Angus C.R., Preston L.R., and Meier P.S.
Alcohol and Alcoholism (to appear)

[BibTeX]

@article{purshouse2013modelling,
title={Modelling the Cost-Effectiveness of Alcohol Screening and Brief Interventions in Primary Care in England},
author={Purshouse, R.C. and Brennan, A. and Rafia, R. and Latimer, N.R. and Archer, R.J. and Angus, C.R. and Preston, L.R. and Meier, P.S.},
journal={Alcohol and Alcoholism},
year={2013},
publisher={Med Council on Alcohol}
}

2012

Methods for Many-Objective Optimization: An Analysis
I. Giagkiozis, and P.J. Fleming
Research Report No. 1030, November 2012

[Abstract][BibTeX][PDF]

@techreport{giagkiozis2012parvsdecomp,
author	= {Giagkiozis, I. and Fleming, P.J.},
title	= {Methods for Many-Objective Optimization: An Analysis},
publisher = {Automatic Control and Systems Engineering, University of Sheffield},
year	= {2012},
number	= {No. 1030},
month	= {November},
type	= {Research Report}
}
Decomposition-based methods are often cited as the solution to problems related with many-objective optimization. Decomposition-based methods employ a scalarizing function to reduce a many-objective problem into a set of single objective problems, which upon solution yields a good approximation of the set of optimal solutions. This set is commonly referred to as Pareto front. In this work we explore the implications of using decomposition-based methods over Pareto-based methods from a probabilistic point of view. Namely, we investigate whether there is an advantage of using a decomposition-based method, for example using the Chebyshev scalarizing function, over Pareto-based methods.

Generalized Decomposition and Cross Entropy Methods for Many-Objective Optimization
I. Giagkiozis, R.C. Purshouse, and P.J. Fleming
Research Report No. 1029, November 2012

[Abstract][BibTeX][PDF]

@techreport{giagkiozis2012mace,
author	= {Giagkiozis, I. and Purshouse, R.C. and Fleming, P.J.},
title	= {Generalized Decomposition and Cross Entropy Methods for Many-Objective Optimization},
publisher = {Automatic Control and Systems Engineering, University of Sheffield},
year	= {2012},
url   = {http://eprints.whiterose.ac.uk/74767/},
number	= {No. 1029},
month	= {November},
type	= {Research Report}
}
Decomposition-based algorithms for multi-objective optimization problems have increased in popularity in the past decade. Although their convergence to the Pareto optimal front (PF) is in several instances superior to that of Pareto-based algorithms, the problem of selecting a way to distribute or guide these solutions in a high-dimensional space has not been explored. In this work, we introduce a novel concept which we call generalized decomposition. Generalized decomposition provides a framework with which the decision maker (DM) can guide the underlying evolutionary algorithm toward specific regions of interest or the entire Pareto front with the desired distribution of Pareto optimal solutions. Additionally, it is shown that generalized decomposition simplifies many-objective problems by unifying the three performance objectives of multi-objective evolutionary algorithms -- convergence to the PF, evenly distributed Pareto optimal solutions and coverage of the entire front -- to only one, that of convergence. A framework, established on generalized decomposition, and an estimation of distribution algorithm (EDA) based on low-order statistics, namely the cross-entropy method (CE), is created to illustrate the benefits of the proposed concept for many objective problems. This choice of EDA also enables the test of the hypothesis that low-order statistics based EDAs can have comparable performance to more elaborate EDAs.

Increasing the Density of Available Pareto Optimal Solutions
Giagkiozis, I. and Fleming, P.J.
Research Report No. 1028, Novemeber 2012

[Abstract][BibTeX][PDF][Code]

@techreport{giagkiozis2012incdens,
author= {Giagkiozis, I. and Fleming, P.J.},
title	= {Increasing the Density of Available Pareto Optimal Solutions},
publisher = {Automatic Control and Systems Engineering, University of Sheffield},
year	= {2012},
url   = {http://eprints.whiterose.ac.uk/74769/}
type	= {Research Report},
number= {No. 1028},
month	= {November}
}
The set of available multi-objective optimization algorithms continues to grow. This fact can be partially attributed to their widespread use and applicability. However this increase also suggests several issues remain to be addressed satisfactorily. One such issue is the diversity and the number of solutions available to the decision maker (DM). Even for algorithms very well suited for a particular problem, it is difficult - mainly due to the computational cost - to use a population large enough to ensure the likelihood of obtaining a solution close to the DMs preferences. In this paper we present a novel methodology that produces additional Pareto optimal solutions from a Pareto optimal set obtained at the end run of any multi-objective optimization algorithm. This method, which we refer to as Pareto estimation, is tested against a set of 3 and 3-objective test problems and a 3-objective portfolio optimization problem to illustrate its' utility for a real-world problem.

Preference-inspired Co-evolutionary Algorithms for Many-objective Optimisation
Rui Wang, R.C. Purshouse, and P.J. Fleming
Evolutionary Computation, IEEE Transactions on - to appear

[Abstract][BibTeX]

@article{wang2012piceag,
author = {Wang, R. and Purshouse, R.C. and Fleming, P.J.},
issn = {1089-778X},
journal = {Evolutionary Computation, IEEE Transactions on  - to appear},
year={2013},
title = Preference\-inspired Co\-evolutionary Algorithms for Many\-objective Optimisation,
}
Abstract¡ªThe simultaneous optimisation of many objectives (say, in excess of 3), in order to obtain a full and satisfactory set of trade-off solutions to support a posteriori decision-making, remains a challenging problem. The concept of co-evolving a family of decision-maker preferences together with a population of candidate solutions is studied here and demonstrated to have promising performance characteristics for such problems. After introducing the concept of the preference-inspired coevolutionary algorithm (PICEA), a realisation of this concept, PICEA-g, is systematically compared with four of the best-inclass evolutionary algorithms; random search is also studied as a baseline approach. The four evolutionary algorithms used in the comparison are a Pareto-dominance relation based algorithm (NSGA-II), an -dominance relation based algorithm (- MOEA), a scalarizing function based algorithm (MOEA/D) and an indicator based algorithm (HypE). It is demonstrated that, for bi-objective problems, all of the multi-objective evolutionary algorithms perform competitively. As the number of objectives increases, PICEA-g and HypE, which have a comparable performance, tend to outperform NSGA-II, -MOEA and MOEA/D. All the algorithms outperformed random search.

Local preference-inspired co-evolutionary algorithms
Rui Wang, R.C. Purshouse, and P.J. Fleming
GECCO 2012: Proceedings of the Genetic and Evolutionary Computation Conference

[Abstract][BibTeX]

@inproceedings{wang2012localpiceag,
address = {Philadelphia, USA},
title={Local preference-inspired co-evolutionary algorithms},
author={Wang, R. and Purshouse, R.C. and Fleming, P.J.},
booktitle={ GECCO 2012: Proceedings of the Genetic and Evolutionary Computation Conference}
pages={513--520},
year={2012},
organization={ACM}
}
Preference-inspired co-evolutionary algorithms (PICEAs) are a new class of approaches which have been demonstrated to perform well on multi-objective problems (MOPs). The good performance of PICEAs is largely due to its clever fitness calculation method which is in a competitive co-evolutionary way. However, this fitness calculation method has a potential limitation. In this work, we analyze this limitation and propose to implement PICEAs within a local structure (LPICEAs). By using the local structure, the benefits of local operations are incorporated into PICEAs. Meanwhile, the limitation of the original fitness calculation method is solved. In details, the candidate solutions are firstly partitioned into several clusters according to a clustering technique. Then the evolutionary operations, i.e. selection-for-survival and genetic-variation are executed on each cluster, separately. To validate the performance of LPICEAs, LPICEAs are compared to PICEAs on some benchmarks functions. Experimental results indicate LPICEAs significantly outperform PICEAs on most of the benchmarks. Moreover, the influence of LPICEAs to the tuning of the parameter k , i.e. the number of clusters used in LPICEAs is studied. The results indicate that the performance of LPICEAs is sensitive to the parameter k .

2011

Diversity management in evolutionary many-objective optimization
Adra, S.F. and Fleming, P.J.
IEEE Transactions on Evolutionary Computation, 2011

[BibTeX]

@article{adra2011diversity,
title={Diversity management in evolutionary many-objective optimization},
author={Adra, S.F. and Fleming, P.J.},
journal={Evolutionary Computation, IEEE Transactions on},
volume={15},
number={2},
pages={183--195},
year={2011},
publisher={IEEE}
}

Preference-driven co-evolutionary algorithms show promise for many-objective optimisation
Purshouse R.C., Jalb{u{a}} C., and Fleming P.J.
6th International Conference on Evolutionary Multi-Criterion Optimization

[BibTeX]

@article{purshouse2011preference,
title={Preference-driven co-evolutionary algorithms show promise for many-objective optimisation},
author={Purshouse, R. and Jalb{\u{a, C. and Fleming, P.},
journal={Evolutionary Multi-Criterion Optimization},
pages={136–150},
year={2011},
publisher={Springer}
}
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2010

A many-objective optimisation decision-making process applied to automotive diesel engine calibration
Adra, S. and Griffin, I. and Fleming, P.
Simulated Evolution and Learning, 2010

[BibTeX]

@article{lygoe2010many,
title={A many-objective optimisation decision-making process applied to automotive diesel engine calibration},
author={Lygoe, R. and Cary, M. and Fleming, P.},
journal={Simulated Evolution and Learning},
pages={638--646},
year={2010},
publisher={Springer}
}

Estimated effect of alcohol pricing policies on health and health economic outcomes in England: an epidemiological model
Purshouse R.C., Meier P.S., Brennan A., Taylor K.B., and Rafia R.
The Lancet

[BibTeX]

@article{purshouse2010estimated,
title={Estimated effect of alcohol pricing policies on health and health economic outcomes in England: an epidemiological model},
author={Purshouse, R.C. and Meier, P.S. and Brennan, A. and Taylor, K.B. and Rafia, R.},
journal={The Lancet},
volume={375},
number={9723},
pages={1355--1364},
year={2010},
publisher={Elsevier}
}

2009

Convergence acceleration operator for multiobjective optimization
Adra, Salem F and Dodd, Tony J and Griffin, Ian A and Fleming, Peter J
IEEE Transactions on Evolutionary Computation, 2009

[BibTeX]

@article{adra2009convergence,
title={Convergence acceleration operator for multiobjective optimization},
author={Adra, Salem F and Dodd, Tony J and Griffin, Ian A and Fleming, Peter J},
journal={Evolutionary Computation, IEEE Transactions on},
volume={13},
number={4},
pages={825--847},
year={2009},
publisher={IEEE}
}

Policy options for alcohol price regulation: The importance of modelling population heterogeneity
Meier P.S., Purshouse R.C., and Brennan A.
Addiction

[BibTeX]

@article{meier2009policy,
title={Policy options for alcohol price regulation: the importance of modelling population heterogeneity},
author={Meier, P.S. and Purshouse, R. and Brennan, A.},
journal={Addiction},
volume={105},
number={3},
pages={383--393},
year={2009},
publisher={Wiley Online Library}
}

2008

Multiobjective optimization using variable complexity modelling for control system design
Silva, V.V.R. and Fleming, P.J. and Sugimoto, J. and Yokoyama, R.
Applied Soft Computing, 2008

[BibTeX]

@article{silva2008multiobjective,
title={Multiobjective optimization using variable complexity modelling for control system design},
author={Silva, V.V.R. and Fleming, P.J. and Sugimoto, J. and Yokoyama, R.},
journal={Applied Soft Computing},
volume={8},
number={1},
pages={392--401},
year={2008},
publisher={Elsevier}
}

2007

A comparative study of progressive preference articulation techniques for multiobjective optimisation
Adra, S. and Griffin, I. and Fleming, P.
Evolutionary Multi-Criterion Optimization, 2007

[BibTeX]

@article{adra2007comparative,
title={A comparative study of progressive preference articulation techniques for multiobjective optimisation},
author={Adra, S. and Griffin, I. and Fleming, P.},
journal={Evolutionary Multi-Criterion Optimization},
pages={908--921},
year={2007},
publisher={Springer}
}

Optimal advertising campaign generation for multiple brands using MOGA
Fleming, P.J. and Pashkevich, M.A.
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2007

[BibTeX]

@article{fleming2007optimal,
title={Optimal advertising campaign generation for multiple brands using MOGA},
author={Fleming, P.J. and Pashkevich, M.A.},
journal={Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on},
volume={37},
number={6},
pages={1190--1201},
year={2007},
publisher={IEEE}
}

On the evolutionary optimization of many conflicting objectives
Purshouse, Robin C and Fleming, Peter J
IEEE Transactions on Evolutionary Computation, 2007

[BibTeX]

@article{purshouse2007evolutionary,
title={On the evolutionary optimization of many conflicting objectives},
author={Purshouse, Robin C and Fleming, Peter J},
journal={Evolutionary Computation, IEEE Transactions on},
volume={11},
number={6},
pages={770--784},
year={2007},
publisher={IEEE}
}

Computational steering of a multi-objective evolutionary algorithm for engineering design
Shenfield, A. and Fleming, P.J. and Alkarouri, M.
Engineering Applications of Artificial Intelligence, 2007

[BibTeX]

@article{shenfield2007computational,
title={Computational steering of a multi-objective evolutionary algorithm for engineering design},
author={Shenfield, A. and Fleming, P.J. and Alkarouri, M.},
journal={Engineering Applications of Artificial Intelligence},
volume={20},
number={8},
pages={1047--1057},
year={2007},
publisher={Elsevier}
}

On the evolutionary optimization of many conflicting objectives
Purshouse, R.C. and Fleming P.J.
IEEE Transactions on Evolutionary Computation

[BibTeX]

@article{purshouse2007evolutionary,
title={On the evolutionary optimization of many conflicting objectives},
author={Purshouse, R.C. and Fleming, P.J.},
journal={Evolutionary Computation, IEEE Transactions on},
volume={11},
number={6},
pages={770--784},
year={2007},
publisher={IEEE}
}

2006

Staged combustion control design for aero engines
Breikin, TV and Herbert, ID and Kim, SK and Regunath, S. and Hargrave, SM and Thompson, HA and Fleming, PJ
Control Engineering Practice, 2006

[BibTeX]

@article{breikin2006staged,
title={Staged combustion control design for aero engines},
author={Breikin, TV and Herbert, ID and Kim, SK and Regunath, S. and Hargrave, SM and Thompson, HA and Fleming, PJ},
journal={Control Engineering Practice},
volume={14},
number={4},
pages={387--396},
year={2006},
publisher={Elsevier}
}

Stability analysis of the particle dynamics in particle swarm optimizer
Kadirkamanathan, V. and Selvarajah, K. and Fleming, P.J.
IEEE Transactions on Evolutionary Computation, 2006

[BibTeX]

@article{kadirkamanathan2006stability,
title={Stability analysis of the particle dynamics in particle swarm optimizer},
author={Kadirkamanathan, V. and Selvarajah, K. and Fleming, P.J.},
journal={Evolutionary Computation, IEEE Transactions on},
volume={10},
number={3},
pages={245--255},
year={2006},
publisher={IEEE}
}

Linear matrix inequalities and evolutionary optimization in multiobjective control
Molina-Cristobal, A and Griffin, IA and Fleming, PJ and Owens, DH
International Journal of Systems Science, 2006

[BibTeX]

@article{molina2006linear,
title={Linear matrix inequalities and evolutionary optimization in multiobjective control},
author={Molina-Cristobal, A and Griffin, IA and Fleming, PJ and Owens, DH},
journal={International Journal of Systems Science},
volume={37},
number={8},
pages={513--522},
year={2006},
publisher={Taylor \& Francis}
}

2005

Performance optimization of gas turbine engine
Silva, V.V.R. and Khatib, W. and Fleming, P.J.
Engineering Applications of Artificial Intelligence, 2005

[BibTeX]

@article{silva2005performance,
title={Performance optimization of gas turbine engine},
author={Silva, V.V.R. and Khatib, W. and Fleming, P.J.},
journal={Engineering Applications of Artificial Intelligence},
volume={18},
number={5},
pages={575--583},
year={2005},
publisher={Elsevier}
}

Evolution of mathematical models of chaotic systems based on multiobjective genetic programming
Rodríguez-Vázquez, Katya and Fleming, Peter J.
Knowledge and Information Systems, 2005

[BibTeX]

@article{rodriguez2005evolution,
title={Evolution of mathematical models of chaotic systems based on multiobjective genetic programming},
author={Rodr{\'\i}guez-V{\'a}zquez, Katya and Fleming, Peter J},
journal={Knowledge and Information Systems},
volume={8},
number={2},
pages={235--256},
year={2005},
publisher={Springer}
}

Many-objective optimization: An engineering design perspective
Fleming, P. and Purshouse, R. and Lygoe, R.
Evolutionary Multi-Criterion Optimization, 2005

[BibTeX]

@article{fleming2005many,
title={Many-objective optimization: An engineering design perspective},
author={Fleming, P. and Purshouse, R. and Lygoe, R.},
journal={Evolutionary Multi-Criterion Optimization},
pages={14--32},
year={2005},
publisher={Springer}
}

2004

Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming
Rodriguez-Vazquez, K. and Fonseca, C.M. and Fleming, PJ
IEEE Systems, Man and Cybernetics, Part A: Systems and Humans, 2004

[BibTeX]

@article{rodriguez2004identifying,
title={Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming},
author={Rodriguez-Vazquez, K. and Fonseca, C.M. and Fleming, PJ},
journal={Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on},
volume={34},
number={4},
pages={531--545},
year={2004},
publisher={IEEE}
}

2003

The application of evolutionary optimisation to a noisy engine maintenance problem
Argyle, J. and Fleming, P. and Thompson, H. and Crocker, J.
Intelligent Control Systems and Signal Processing (ICONS 2003)

[BibTeX]

@article{argyle2003application,
title={The application of evolutionary optimisation to a noisy engine maintenance problem},
author={Argyle, J. and Fleming, P. and Thompson, H. and Crocker, J.},
journal={Intelligent Control Systems and Signal Processing 2003:(ICONS 2003); A Proceedings Volume from the IFAC International Conference, Faro, Algarve, Portugal, 8-11 April 2003},
pages={341},
year={2003},
publisher={Pergamon Pr}
}

An adaptive divide-and-conquer methodology for evolutionary multi-criterion optimisation
Purshouse, Robin C. and Fleming, Peter J.
Evolutionary Multi-Criterion Optimization, 2002

[BibTeX]

@article{purshouse2003adaptive,
title={An adaptive divide-and-conquer methodology for evolutionary multi-criterion optimisation},
author={Purshouse, R. and Fleming, P.},
journal={Evolutionary Multi-Criterion Optimization},
pages={72--72},
year={2003},
publisher={Springer}
}

Conflict, harmony, and independence: Relationships in evolutionary multi-criterion optimisation
Purshouse R.C. and Fleming P.J.
2nd International Conference on Evolutionary Multi-Criterion Optimization

[BibTeX]

@article{purshouse2003conflict,
title={Conflict, harmony, and independence: Relationships in evolutionary multi-criterion optimisation},
author={Purshouse, R. and Fleming, P.},
journal={Evolutionary Multi-Criterion Optimization},
pages={67--67},
year={2003},
publisher={Springer}
}

Evolutionary many-objective optimisation: An exploratory analysis
Purshouse R.C. and Fleming P.J.
2003 Congress on Evolutionary Computation

[BibTeX]

@inproceedings{purshouse2003evolutionary,
title={Evolutionary many-objective optimisation: An exploratory analysis},
author={Purshouse, R.C. and Fleming, P.J.},
booktitle={Evolutionary Computation, 2003. CEC'03. The 2003 Congress on},
volume={3},
pages={2066--2073},
year={2003},
organization={IEEE}
}

Evolutionary algorithms in control systems engineering: A survey
Fleming P.J. and Purshouse R.C.
Control Engineering Practice

[BibTeX]

@article{fleming2002evolutionary,
title={Evolutionary algorithms in control systems engineering: a survey},
author={Fleming, P.J. and Purshouse, R.C.},
journal={Control engineering practice},
volume={10},
number={11},
pages={1223--1241},
year={2002},
publisher={Elsevier}
}

2002

Evolutionary algorithms in control systems engineering: A survey
Fleming, P.J. and Purshouse, R.C.
Control Engineering Practice, 2002

[BibTeX]

@article{fleming2002evolutionary,
title={Evolutionary algorithms in control systems engineering: A survey},
author={Fleming, P.J. and Purshouse, R.C.},
journal={Control Engineering Practice},
volume={10},
number={11},
pages={1223--1241},
year={2002},
publisher={Elsevier}
}

Fuzzy scheduling control of a gas turbine aero-engine: a multiobjective approach
Chipperfield, A.J. and Bica, B. and Fleming, P.J.
IEEE Transactions on Industrial Electronics, 2002

[BibTeX]

@article{chipperfield2002fuzzy,
title={Fuzzy scheduling control of a gas turbine aero-engine: a multiobjective approach},
author={Chipperfield, A.J. and Bica, B. and Fleming, P.J.},
journal={Industrial Electronics, IEEE Transactions on},
volume={49},
number={3},
pages={536--548},
year={2002},
publisher={IEEE}
}

Combinatorial library design using a multiobjective genetic algorithm
Gillet, V.J. and Khatib, W. and Willett, P. and Fleming, P.J. and Green, D.V.S.
Journal of Chemical Information and Computer Sciences, 2002

[BibTeX]

@article{gillet2002combinatorial,
title={Combinatorial library design using a multiobjective genetic algorithm},
author={Gillet, V.J. and Khatib, W. and Willett, P. and Fleming, P.J. and Green, D.V.S.},
journal={Journal of Chemical Information and Computer Sciences},
volume={42},
number={2},
pages={375--385},
year={2002},
publisher={ACS Publications}
}

Combinatorial library design using a multiobjective genetic algorithm
Gillet, V.J. and Willett, P. and Fleming, P.J. and Green, D.V.S.
Journal of Molecular Graphics and Modelling, 2002

[BibTeX]

@article{gillet2002designing,
title={Designing focused libraries using MoSELECT},
author={Gillet, V.J. and Willett, P. and Fleming, P.J. and Green, D.V.S.},
journal={Journal of Molecular Graphics and Modelling},
volume={20},
number={6},
pages={491--498},
year={2002},
publisher={Elsevier}
}

Multiobjective optimization in quantitative structure-activity relationships: Deriving accurate and interpretable QSARs
Nicolotti, O. and Gillet, V.J. and Fleming, P.J. and Green, D.V.S.
Journal of Medicinal Chemistry, 2002

[BibTeX]

@article{nicolotti2002multiobjective,
title={Multiobjective optimization in quantitative structure-activity relationships: Deriving accurate and interpretable QSARs},
author={Nicolotti, O. and Gillet, V.J. and Fleming, P.J. and Green, D.V.S.},
journal={Journal of Medicinal Chemistry},
volume={45},
number={23},
pages={5069--5080},
year={2002},
publisher={ACS Publications}
}

Why use elitism and sharing in a multi-objective genetic algorithm
Purshouse, Robin C. and Fleming, Peter J.
Proceedings of the Genetic and Evolutionary Computation Conference, 2002

[BibTeX]

@inproceedings{purshouse2002use,
title={Why use elitism and sharing in a multi-objective genetic algorithm},
author={Purshouse, Robin C. and Fleming, Peter J.},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
pages={520--527},
year={2002},
organization={Morgan Kaufmann Publishers Inc.}
}

2001

On-line evolution of robust control systems: an industrial active magnetic bearing application
Schroder, P. and Green, B. and Grum, N. and Fleming, PJ
Control Engineering Practice, 2001

[BibTeX]

@article{schroder2001line,
title={On-line evolution of robust control systems: an industrial active magnetic bearing application},
author={Schroder, P. and Green, B. and Grum, N. and Fleming, PJ},
journal={Control Engineering Practice},
volume={9},
number={1},
pages={37--49},
year={2001},
publisher={Elsevier}
}

2000

Multi-objective optimization approach to the ALSTOM gasifier problem
Griffin, IA and Schroder, P and Chipperfield, AJ and Fleming, PJ
Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2000

[BibTeX]

@article{griffin2000multi,
title={Multi-objective optimization approach to the ALSTOM gasifier problem},
author={Griffin, IA and Schroder, P and Chipperfield, AJ and Fleming, PJ},
journal={Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering},
volume={214},
number={6},
pages={453--469},
year={2000},
publisher={Prof Eng Publishing}
}

1999

Distributed aero-engine control systems architecture selection using multi-objective optimisation
Thompson, H.A. and Chipperfield, AJ and Fleming, PJ and Legge, C.
Control Engineering Practice, 1999

[BibTeX]

@article{thompson1999distributed,
title={Distributed aero-engine control systems architecture selection using multi-objective optimisation},
author={Thompson, H.A. and Chipperfield, AJ and Fleming, PJ and Legge, C.},
journal={Control Engineering Practice},
volume={7},
number={5},
pages={655--664},
year={1999},
publisher={Elsevier}
}

1998

Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation
Fonseca, C.M. and Fleming, P.J.
IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 1998

[BibTeX]

@article{fonseca1998multiobjective1,
title={Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation},
author={Fonseca, C.M. and Fleming, P.J.},
journal={Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on},
volume={28},
number={1},
pages={26--37},
year={1998},
publisher={IEEE}
}

Multiobjective optimization and multiple constraint handling with evolutionary algorithms. II. Application example
Fonseca, C.M. and Fleming, P.J.
IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 1998

[BibTeX]

@article{fonseca1998multiobjective2,
title={Multiobjective optimization and multiple constraint handling with evolutionary algorithms. II. Application example},
author={Fonseca, Carlos M and Fleming, Peter J},
journal={Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on},
volume={28},
number={1},
pages={38--47},
year={1998},
publisher={IEEE}
}

Multi-objective genetic programming for nonlinear system identification
Rodriguez-Vazquez, K. and Fleming, PJ
Electronics Letters, 1998

[BibTeX]

@article{rodriguez1998multi,
title={Multi-objective genetic programming for nonlinear system identification},
author={Rodriguez-Vazquez, K. and Fleming, PJ},
journal={Electronics Letters},
volume={34},
number={9},
pages={930--931},
year={1998},
publisher={IET}
}

1997

Evolutionary H infin; design of an electromagnetic suspension control system for a maglev vehicle
Dakev, Nikolay V and Whidborne, James F and Chipperfield, Andrew J and Fleming, PJ
Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 1997

[BibTeX]

@article{dakev1997evolutionary,
title={Evolutionary H infin; design of an electromagnetic suspension control system for a maglev vehicle},
author={Dakev, Nikolay V and Whidborne, James F and Chipperfield, Andrew J and Fleming, PJ},
journal={Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering},
volume={211},
number={5},
pages={345--355},
year={1997},
publisher={Prof Eng Publishing}
}

1996

Multiobjective gas turbine engine controller design using genetic algorithms
Fonseca, C.M. and Fleming, P.J.
IEEE Transactions on Industrial Electronics, 1996

[BibTeX]

@article{chipperfield1996multiobjective,
title={Multiobjective gas turbine engine controller design using genetic algorithms},
author={Chipperfield, A. and Fleming, P.},
journal={Industrial Electronics, IEEE Transactions on},
volume={43},
number={5},
pages={583--587},
year={1996},
publisher={IEEE}
}

On the performance assessment and comparison of stochastic multiobjective optimizers
Fonseca, C.M. and Fleming, P.J.
Parallel problem solving from nature—ppsn IV, 1996

[BibTeX]

@article{fonseca1996performance,
title={On the performance assessment and comparison of stochastic multiobjective optimizers},
author={Fonseca, C. and Fleming, P.J.},
journal={Parallel problem solving from nature—ppsn IV},
pages={584--593},
year={1996},
publisher={Springer}
}

Non-linear system identification with multiobjective genetic algorithms
Fonseca, C.M. and Fleming, P.J.
Proc 13th World Congress of IFAC, 1996

[BibTeX]

@inproceedings{fonseca1996sysid,
author={Fonseca, C.M. and Fleming, P.J.},
booktitle={Proc 13th World Congress of IFAC, 1996}, 
title={Non-linear system identification with multiobjective genetic algorithms},
year={1996},
pages={187 - 192}
}

1995

Multiobjective genetic algorithms made easy: selection, sharing and mating restriction
Fonseca, C.M. and Fleming, P.J.
Genetic Algorithms in Engineering Systems: Innovations and Applications, 1995

[BibTeX]

@inproceedings{fonseca1995madeeasy,
author={Fonseca, C.M. and Fleming, P.J.},
booktitle={Genetic Algorithms in Engineering Systems: Innovations and Applications, 1995. GALESIA. First International Conference on (Conf. Publ. No. 414)}, 
title={Multiobjective genetic algorithms made easy: selection sharing and mating restriction},
year={1995},
month={sep},
pages={45 -52},
doi={10.1049/cp:19951023}
}

An overview of evolutionary algorithms in multiobjective optimization
Fonseca, C.M. and Fleming, P.J.
Evolutionary computation, 1995

[BibTeX]

@article{fonseca1995overview,
title={An overview of evolutionary algorithms in multiobjective optimization},
author={Fonseca, C.M. and Fleming, P.J.},
journal={Evolutionary computation},
volume={3},
number={1},
pages={1--16},
year={1995},
publisher={MIT Press}
}

1994

Multiobjective optimal controller design with genetic algorithms
Fonseca, C.M. and Fleming, P.J.
International Conference on Control, 1994

[BibTeX]

@inproceedings{fonseca1994multiobjective,
title={Multiobjective optimal controller design with genetic algorithms},
author={Fonseca, C.M. and Fleming, P.J.},
booktitle={International Conference on Control, 1994},
volume={1},
pages={745--749},
year={1994},
organization={IET}
}

1993

Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization
Fonseca, C.M. and Fleming, P.J.
Proceedings of the Fifth International Conference on Genetic Algorithms, 1993

[BibTeX]

@article{fonseca1993genetic,
title={Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization},
author={Fonseca, C.M. and Fleming, P.J.},
journal={Proceedings of the Fifth International Conference on Genetic Algorithms},
volume={1},
pages={416},
year={1993},
publisher={San Mateo, California}
}

1986

Application of multi-objective optimisation to compensator design for SISO control systems
Fleming, P.J. and Pashkevich, A.P.
Electronics Letters, 1986

[BibTeX]

@article{fleming1986siso,
author={Fleming, P.J. and Pashkevich, A.P.},
journal={Electronics Letters}, 
title={Application of multi-objective optimisation to compensator design for SISO control systems},
year={1986},
month={27},
volume={22},
number={5},
pages={258 -259},
doi={10.1049/el:19860177},
ISSN={0013-5194},}

1985

A non-linear programming approach to the computer-aided design of regulators using a linear-quadratic formulation
Fleming, P.
International Journal of Control, 1985

[BibTeX]

@article{fleming1985formulation,
title={A non-linear programming approach to the computer-aided design of regulators using a linear-quadratic formulation},
author={Fleming, P.},
journal={International Journal of Control},
vol={22}
year={1985}
}

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