Preferences and their application in evolutionary multiobjective optimization
IEEE Transactions on Evolutionary ComputationPublished 1 January 2002
Dragoljub Cvetković, Ian C. Parmee
Citations233
SJR quartileQ1
SJR score3.50
SNIP3.12
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A new preference method is described and its usefulness was demonstrated in a real-world project of conceptual airframe design and theoretical results relating to complexity and sensitivity of the algorithm are presented and discussed.
Abstract
The paper describes a new preference method and its use in multiobjective optimization. These preferences are developed with a goal to reduce the cognitive overload associated with the relative importance of a certain criterion within a multiobjective design environment involving large numbers of objectives. Their successful integration with several genetic-algorithm-based design search and optimization techniques (weighted sums, weighted Pareto, weighted co-evolutionary methods, and weighted scenarios) are described and theoretical results relating to complexity and sensitivity of the algorithm are presented and discussed. Its usefulness was demonstrated in a real-world project of conceptual airframe design.
Keywords
Computer ScienceEngineering
IEEE Transactions on Evolutionary ComputationA fast and elitist multiobjective genetic algorithm: NSGA-II
47,759 Citations2002Kalyanmoy Deb, Amrit Pratap +2 more
This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
The MIT Press eBooksAdaptation in Natural and Artificial Systems
35,568 Citations1992John H. Holland
Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways.
Psychological ReviewThe magical number seven, plus or minus two: Some limits on our capacity for processing information.
17,493 Citations1956George Miller
The theory provides us with a yardstick for calibrating the authors' stimulus materials and for measuring the performance of their subjects, and the concepts and measures provided by the theory provide a quantitative way of getting at some of these questions.
Psychological ReviewThe magical number seven, plus or minus two: Some limits on our capacity for processing information.
17,276 Citations1994George Miller
About some experiments that tested how accurately people can assign numbers to the magnitudes of various aspects of a stimulus, and since the results are analyzed in terms of the concepts of information theory, I shall have to preface my discussion with a few remarks about this theory.
Information SciencesThe concept of a linguistic variable and its application to approximate reasoning-III
12,148 Citations1975L. A. Zadeh
Much of what constitutes the core of scientific knowledge may be regarded as a reservoir of concepts and techniques which can be drawn upon to construct mathematical models of various types of systems and thereby yield quantitative information concerning their behavior.
Information SciencesThe concept of a linguistic variable and its application to approximate reasoning—I
12,036 Citations1975Lotfi A. Zadeh
It is shown that probabilities, too, can be treated as linguistic variables with values such as likely, very likely, unlikely, etc.
Psychological ReviewTelling more than we can know: Verbal reports on mental processes.
11,285 Citations1977Richard E. Nisbett, Timothy D. Wilson
IEEE Transactions on Systems Man and CyberneticsOutline of a New Approach to the Analysis of Complex Systems and Decision Processes
8,667 Citations1973Lotfi A. Zadeh
By relying on the use of linguistic variables and fuzzy algorithms, the approach provides an approximate and yet effective means of describing the behavior of systems which are too complex or too ill-defined to admit of precise mathematical analysis.
Journal of the Operational Research SocietyDecisions with Multiple Objectives: Preferences and Value Tradeoffs.
8,440 Citations1994Ralph L. Keeney, Howard Raiffa +1 more
Medical Entomology and ZoologyMultiple Attribute Decision Making: Methods and Applications
8,268 Citations1981Shu-Jen J. Chen, C. L. Hwang +2 more
Evolutionary ComputationMuiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
6,869 Citations1994N. Srinivas, Kalyanmoy Deb
Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously are investigated and suggested to be extended to higher dimensional and more difficult multiobjective problems.
The Knowledge Engineering ReviewIntelligent agents: theory and practice
6,644 Citations1995Michael Wooldridge, Nicholas R. Jennings
The aim of this paper is to point the reader at what it is perceived to be the most important theoretical and practical issues associated with the design and construction of intelligent agents.
Lecture notes in computer scienceA Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II
5,246 Citations2000Kalyanmoy Deb, Samir Agrawal +2 more
Simulation results on five difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to PAES and SPEA--two other elitist multi-objective EAs which pay special attention towards creating a diverse Paretimal front.
Mathematics and Computers in SimulationSimulated annealing: theory and applications.
3,005 Citations1988
Information SciencesThe concept of a linguistic variable and its application to approximate reasoning—II
2,509 Citations1975Lotfi A. Zadeh
The nonrestricted nonfuzzy variable u constitutes the base variable for X and R(X;u) is a fuzzy subset of U which represents a fuzzy restriction on the values of u imposed by X.
IEEE Press eBooksEvolutionary Computation: Towards a New Philosophy of Machine Intelligence
2,288 Citations1995David B. Fogel
Evolutionary ComputationAn Overview of Evolutionary Algorithms in Multiobjective Optimization
2,259 Citations1995Carlos M. Fonseca, P.J. Fleming
Current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of population-based approaches and the more recent ranking schemes based on the definition of Pareto optimality.
Evolution and Optimum Seeking
2,200 Citations1995Hans–Paul Schwefel
Problems and Methods of Optimization Hill Climbing Strategies Random Strategies Evolution Strategies for Numerical Optimization Comparison of Direct Search Strategies for Parameter Optimization.
Lecture notes in economics and mathematical systemsFuzzy Multiple Attribute Decision Making
1,844 Citations1992Shu‐Jen Chen, Ching-Lai Hwang
Organizational Behavior and Human PerformanceComparison of Bayesian and regression approaches to the study of information processing in judgment
1,841 Citations1971Paul Slovic, Sarah Lichtenstein
This work examines the models that have been developed for describing and prescribing the use of information in decision making, the major experimental paradigms, and the major empirical results and conclusions of these two approaches.
Journal of the ACMA Theorem on Boolean Matrices
1,657 Citations1962Stephen Warshall
It is proved the validity of an algorithm whose running time goes up slightly faster than the square of d, the running times of which increase-other things being equal-as the cube of d.
Evolutionary ComputationMulti-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems
1,441 Citations1999Kalyanmoy Deb
The problem features that may cause a multi-objective genetic algorithm (GA) difficulty in converging to the true Pareto-optimal front are studied to enable researchers to test their algorithms for specific aspects of multi- objective optimization.
Mathematical and Computer ModellingVery fast simulated re-annealing
1,285 Citations1989Lester Ingber
An algorithm is developed to statistically find the best global fit of a nonlinear nonconvex cost-function over a D-dimensional space and it is argued that this algorithm permits an annealing schedule for ''temperature'' T decreasing exponentially in annealed-time k, T = T"0exp(-ck^1^/^D).
Readings in Multiple Criteria Decision Aid
587 Citations1990Carlos A. Bana e Costa
This chapter discusses decision-Aid and Decision-Making in Ill-Structured Environments and Measurement Theory And Decision Aid, and the Outranking Approach and the Foundations of Electre Methods.
Decision Support SystemsThe PROMCALC & GAIA decision support system for multicriteria decision aid
489 Citations1994Jean‐Pierre Brans, Bertrand Mareschal
Evolutionary ComputationSelf-Adaptive Genetic Algorithms with Simulated Binary Crossover
430 Citations2001Kalyanmoy Deb, Hans-Georg Beyer
The remarkable similarity in the working principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need for emphasizing further studies on self- Adaptive GAs.
Optimal Mutation Rates in Genetic Search
371 Citations1993Thomas Bäck
The results indicate that a variation of the mutation rate is useful in cases where the tness function is a multimodal pseudo boolean function where multimodality may be caused by the objective function as well as the encoding mechanism.
Multiple Criteria Decision Support in Engineering Design
351 Citations1998Pratyush Sen, Jianbo Yang
Water Resources ResearchMultiobjectives in water resource systems analysis: The Surrogate Worth Trade Off Method
342 Citations1974Yacov Y. Haimes, Warren A. Hall
A new method for solving non-commensurable multiobjective functions, namely, the surrogate worth trade off method, is developed and successfully solved via the Reid and Vemuri multi objective problem in water resources.
European Journal of Operational ResearchDecision-aid and decision-making
337 Citations1990Bernard Roy
Advances in Engineering SoftwareGuidance in evolutionary multi-objective optimization
307 Citations2001J. Branke, Thomas Kaussler +1 more
This paper presents a novel, simple, and intuitive way to integrate the user's preference into the evolutionary algorithm by allowing to define linear maximum and minimum trade-off functions.
Handling preferences in evolutionary multiobjective optimization: a survey
267 Citations2002Carlos A. Coello Coello
The most important preference handling approaches used with evolutionary algorithms, analyzing their advantages and disadvantages are reviewed, and then, some of the potential areas of future research in this discipline are proposed.
Journal of Risk and UncertaintyNontransitive preferences in decision theory
249 Citations1991Peter C. Fishburn
Evolutionary ComputationThe Science of Breeding and Its Application to the Breeder Genetic Algorithm (BGA)
244 Citations1993Heinz Mühlenbein, Dirk Schlierkamp-Voosen
It is shown how the response to selection equation and the concept of heritability can be applied to predict the behavior of the BGA and it is shown that recombination and mutation are complementary search operators.
The Logic of Preference
241 Citations1968Nicholas Rescher
Finding Acceptable Solutions in the Pareto-Optimal Range using Multiobjective Genetic Algorithms
240 Citations1998Peter J. Bentley, Jonathan P. Wakefield
The paper initially explores exactly why separate objectives can cause problems in a genetic algorithm (GA), and a technique to guide the GA to converge on the subset of acceptable solutions is then introduced.
Natural computing seriesMulti-objective Evolutionary Algorithms: Introducing Bias Among Pareto-optimal Solutions
132 Citations2003Kalyanmoy Deb
This chapter presents a few classical techniques to identify a preferred or a compromise solution, and suggests a biased sharing technique which can be used during the optimization phase to find a biased distribution of Pareto-optimal solutions in the region of interest.
Evolutionary ComputationMultiobjective Satisfaction within an Interactive Evolutionary Design Environment
125 Citations2000Ian C. Parmee, Dragan Cvetković +2 more
The development of certain elements within an interactive evolutionary conceptual design environment that allows off-line processing of such information leading to a redefinition of the design space are described.
Journal of Optimization Theory and ApplicationsMaximal vectors and multi-objective optimization
117 Citations1976Jeffrey Lin
A Non-Generational Genetic Algorithm for Multiobjective Optimization.
85 Citations1997Manuel Valenzuela-Rendón, Eduardo Uresti-Charre
A non-generational genetic algorithm for multiobjective optimization that clearly outperforms previous eeorts based on generational genetic algorithms on three multi-Objective optimization problems of growing dii-culty.
Decision-Aid and Decision-Making
79 Citations1990Bernard Roy
European Journal of Operational ResearchA model for the relative importance of the criteria in the Multiplicative AHP and SMART
79 Citations1996Freerk A. Lootsma
The relative importance of any pair of criteria is defined as the substitution rate between the relative gains and losses of the alternatives when the authors move along an indifference curve, which has a plausible basis in psycho-physical research on the relationship between physical stimuli and sensory responses.
Lecture notes in economics and mathematical systemsCharacterization of Pareto and Lexicographic Optimal Solutions
74 Citations1980Aharon Ben‐Tal
Two important solution concepts in the theory of multicriteria decision making are Pareto optimum and Lexicographic optimum.
Genetic and Evolutionary Computation ConferencePreliminary airframe design using co-evolutionary multiobjective genetic algorithms
68 Citations1999Ian C. Parmee, Andrew H. Watson
A novel multiobjective optimisation approach utilising a genetic algorithm for the preliminary design of airframes by comparing the values of the variable parameters of identified solutions relating to a single objective with those of the solutions of the other GA's.
Measurement Theory and Decision Aid
53 Citations1990Jean‐Claude Vansnick
When facing a decision problem, the first thing to do is to structure this problem in order to be possible to apply to this model a systematic treatment, which depends on the model but also on the information obtainable from the decision-maker.
Genetic algorithm-based multi-objective optimisation and conceptual engineering design
35 Citations2003Dragoljub Cvetković, Ian C. Parmee
A method based on preference relations for transforming non-crisp (qualitative) relationships between objectives in multi-objective optimisation into quantitative attributes (numbers) is presented.
European Journal of Operational ResearchMulticriteria decision analysis in a decision tree
34 Citations1997Freerk A. Lootsma
This work proposes to use the scores of cardinal methods for multicriteria decision analysis in order to represent the relative performance of the outcomes, and evaluates the chance forks in the tree via the corresponding aggregation procedure.
Designer’s Preferences and Multi—objective Preliminary Design Processes
28 Citations2000Dragan Cvetković, Ian C. Parmee
A method based on preference relations for transforming non—crisp (qualitative) relationships between objectives in multi—objective optimisation into quantitative attributes (i.e. numbers) is presented.
University of Huddersfield Repository (University of Huddersfield)Finding Acceptable Solutions in the Pareto-Optimal Range using Multiobjective Genetic Algorithms
28 Citations1998Peter J. Bentley, JP Wakefield
Use of preferences for GA-based multi-objective optimisation
25 Citations1999Dragan Cvetković, Ian C. Parmee
A method based on preference relations for transforming non–crisp (qualitative) relationships between objectives in multi–objective optimisation into quantitative attributes (i.e. numbers) is presented.
Multi-objective Optimisation and Preliminary Airframe Design
24 Citations1998Dragan Cvetković, Ian C. Parmee +1 more
PEARL (University of Plymouth)Evolutionary multi-objective decision support systems for conceptual design
23 Citations2000Dragan Cvetković
In this thesis the problem of conceptual engineering design and the possible use of adaptive search techniques and other machine based methods therein are investigated and the issue of complexity and sensitivity is addressed.
Basic Concepts of Preference Modelling
21 Citations1990Philippe Vincke
Including real-life problem preferences in genetic algorithms to improve optimisation of production schedules
12 Citations1997K.J. Shaw
By extending standard GA implementations to adopt the inclusion of user preferences, schedule Optimisation system can demonstrate the full capabilities of GAs for tackling difficult industrial problems such as manufacturing schedule optimisation.