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Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
 Evolutionary Computation
, 1994
"... In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about t ..."
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Cited by 524 (4 self)
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In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Paretooptimal points, instead of a single point. Since genetic algorithms(GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a number of solutions simultaneously. Although a vector evaluated GA (VEGA) has been implemented by Schaffer and has been tried to solve a number of multiobjective problems, the algorithm seems to have bias towards some regions. In this paper, we investigate Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Paretooptimal points sim...
Scalable Test Problems for Evolutionary MultiObjective Optimization
 Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH
, 2001
"... After adequately demonstrating the ability to solve di#erent twoobjective optimization problems, multiobjective evolutionary algorithms (MOEAs) must now show their e#cacy in handling problems having more than two objectives. In this paper, we have suggested three di#erent approaches for systema ..."
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Cited by 150 (22 self)
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After adequately demonstrating the ability to solve di#erent twoobjective optimization problems, multiobjective evolutionary algorithms (MOEAs) must now show their e#cacy in handling problems having more than two objectives. In this paper, we have suggested three di#erent approaches for systematically designing test problems for this purpose. The simplicity of construction, scalability to any number of decision variables and objectives, knowledge of exact shape and location of the resulting Paretooptimal front, and introduction of controlled di#culties in both converging to the true Paretooptimal front and maintaining a widely distributed set of solutions are the main features of the suggested test problems. Because of the above features, they should be found useful in various research activities on MOEAs, such as testing the performance of a new MOEA, comparing di#erent MOEAs, and better understanding of the working principles of MOEAs.
Escaping Hierarchical Traps with Competent Genetic Algorithms
 Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2001
, 2001
"... To solve hierarchical problems, one must be able to learn the linkage, represent partial solutions efficiently, and assure effective niching. We propose the hierarchical ... ..."
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Cited by 102 (49 self)
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To solve hierarchical problems, one must be able to learn the linkage, represent partial solutions efficiently, and assure effective niching. We propose the hierarchical ...
A survey of constraint handling techniques in evolutionary computation methods
 Proceedings of the 4th Annual Conference on Evolutionary Programming
, 1995
"... One of the major components of any evolutionary system is the eval� uation function. Evaluation functions are used to assign a quality measure for individuals in a population. Whereas evolutionary com� putation techniques assume the existence of an �e�cient � evaluation function for feasible individ ..."
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Cited by 102 (5 self)
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One of the major components of any evolutionary system is the eval� uation function. Evaluation functions are used to assign a quality measure for individuals in a population. Whereas evolutionary com� putation techniques assume the existence of an �e�cient � evaluation function for feasible individuals � there is no uniform methodology for handling �i.e. � evaluating � unfeasible ones. The simplest approach� incorporated by evolution strategies and a version of evolutionary programming �for numerical optimization problems� � is to reject un� feasible solutions. But several other methods for handling unfeasible individuals have emerged recently. This paper reviews such methods �using a domain of nonlinear programming problems � and discusses their merits and drawbacks. 1
An Indexed Bibliography of Genetic Algorithms in Power Engineering
, 1995
"... s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceed ..."
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Cited by 90 (10 self)
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s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986  Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987  1992 ffl EI M: The Engineering Index Monthly: Jan. 1993  Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina GorgesSchleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...
Guiding Multi Objective Evolutionary Algorithms Towards Interesting Regions
, 2000
"... Many real world design problems involve multiple, usually conicting optimization criteria. Often, it is very difficult to weigh the criteria exactly before alternatives are known. MultiObjective Evolutionary Algorithms based on the principle of pareto optimality are designed to produce the complete ..."
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Cited by 27 (3 self)
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Many real world design problems involve multiple, usually conicting optimization criteria. Often, it is very difficult to weigh the criteria exactly before alternatives are known. MultiObjective Evolutionary Algorithms based on the principle of pareto optimality are designed to produce the complete set of nondominated solutions, which allows the user to choose among many alternatives. However, although it is very difficult to exactly de ne the weighting of different optimization criteria, usually the user has some notion as to what range of weightings might be reasonable. In this paper, we present a novel, simple and intuitive way to integrate the user's preference into the evolutionary algorithm. On a number of test problems we show that the proposed algorithm efficiently guides the population towards the interesting region, allowing a faster convergence and a better coverage of the this area of the pareto optimal front.
On the Convergence and DiversityPreservation Properties of MultiObjective Evolutionary Algorithms
, 2001
"... Over the past few years, the research on evolutionary algorithms ..."
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Cited by 16 (4 self)
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Over the past few years, the research on evolutionary algorithms
Applying multicriteria optimisation to develop cognitive models
 In Proceedings of the UK Computational Intelligence Conference
"... A scientic theory is developed by modelling empirical data in a range of domains. The goal of developing a theory is to optimise the t of the theory to as many experimental settings as possible, whilst retaining some qualitative properties such as `parsimony ' or `comprehensibility'. W ..."
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Cited by 2 (2 self)
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A scientic theory is developed by modelling empirical data in a range of domains. The goal of developing a theory is to optimise the t of the theory to as many experimental settings as possible, whilst retaining some qualitative properties such as `parsimony ' or `comprehensibility'. We formalise the task of developing theories of human cognition as a problem in multicriteria optimisation. There are many challenges in this task, including the representation of competing theories, coordinating the t with multiple experiments, and bringing together competing results to provide suitable theories. Experiments demonstrate the development of a theory of categorisation, using multiple optimisation criteria in genetic algorithms to locate paretooptimal sets. 1
SANTA FE BUDAPEST COMPLEX SYSTEMS SUMMER SCHOOL 2002 1 Clustering under Multiobjective Optimization Constraints with Genetic Algorithms
"... Abstract  In this paper, we attempt to solve a clustering problem, which requires simultaneous optimization of several objective functions, with a genetic algorithm. Our emphasis is on the representation of the problem, and the choice of an appropriate aggregate tness function. On the toy problem ..."
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Abstract  In this paper, we attempt to solve a clustering problem, which requires simultaneous optimization of several objective functions, with a genetic algorithm. Our emphasis is on the representation of the problem, and the choice of an appropriate aggregate tness function. On the toy problem of assigning the students of the Santa Fe Complex Systems Summer School into working groups, we try to point out some important general aspects of the task.