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292
Handling Preferences in Evolutionary Multiobjective Optimization: A Survey
- In 2000 Congress on Evolutionary Computation
, 2000
"... Despite the relatively high volume of research conducted on evolutionary multiobjective optimization in the last few years, little attention has been paid to the decision making process that is required to select a final solution to the multiobjective optimization problem at hand. This paper reviews ..."
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Cited by 50 (3 self)
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Despite the relatively high volume of research conducted on evolutionary multiobjective optimization in the last few years, little attention has been paid to the decision making process that is required to select a final solution to the multiobjective optimization problem at hand. This paper reviews the most important preference handling approaches used with evolutionary algorithms, analyzing their advantages and disadvantages, and then, it proposes some of the potential areas of future research in this discipline. 1 Introduction Most real-world problems are multiobjective in nature, because they consider several objectives (or alternatives) that are to be optimized simultaneously. Normally, these objectives are non-commensurable (i.e., they are measured in different units), and are in conflict with each other. Multiobjective optimization problems (MOPs) have received considerable attention in Operations Research (see for example [23, 7, 27, 12]), and they have recently become a very ...
Using Unconstrained Elite Archives for Multi-Objective Optimisation
- IEEE Transactions on Evolutionary Computation
, 2001
"... Multi-Objective Evolutionary Algorithms (MOEAs) have been the subject of numer- ous studies over the past 20 years. Recent work has highlighted the use of an active archive of elite, non-dominated solutions to improve the optimisation speed of these algorithms. ..."
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Cited by 50 (13 self)
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Multi-Objective Evolutionary Algorithms (MOEAs) have been the subject of numer- ous studies over the past 20 years. Recent work has highlighted the use of an active archive of elite, non-dominated solutions to improve the optimisation speed of these algorithms.
Multi-Objective Design Space Exploration Using Genetic Algorithms
- International Workshop on Hardware/Software Codesign
, 2002
"... In this work, we provide a technique for efficiently exploring a parameterized system-on-a-chip (SoC) architecture to find all Paretooptimal configurations in a multi-objective design space. Globally, our approach uses a parameter dependency model of our target parameterized SoC architecture to exte ..."
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Cited by 48 (0 self)
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In this work, we provide a technique for efficiently exploring a parameterized system-on-a-chip (SoC) architecture to find all Paretooptimal configurations in a multi-objective design space. Globally, our approach uses a parameter dependency model of our target parameterized SoC architecture to extensively prune non-optimal subspaces. Locally, our approach applies Genetic Algorithms (GAs) to discover Pareto-optimal configurations within the remaining design points. The computed Pareto-optimal configurations will represent the range of performance (e.g., timing and power) tradeoffs that are obtainable by adjusting parameter values for a fixed application that is mapped on the parameterized SoC architecture. We have successfully applied our technique to explore Pareto-optimal configurations for a number of applications mapped on a parameterized SoC architecture.
A Short Tutorial on Evolutionary Multiobjective Optimization
, 2001
"... This tutorial will review some of the basic concepts related to evolutionary multiobjective optimization (i.e., the use of evolutionary algorithms to handle more than one objective function at a time). The most commonly used evolutionary multiobjective optimization techniques will be described and c ..."
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Cited by 44 (0 self)
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This tutorial will review some of the basic concepts related to evolutionary multiobjective optimization (i.e., the use of evolutionary algorithms to handle more than one objective function at a time). The most commonly used evolutionary multiobjective optimization techniques will be described and criticized, including some of their applications. Theory, test functions and metrics will be also discussed. Finally, we will provide some possible paths of future research in this area.
A Unified Model for Multi-Objective Evolutionary Algorithms with Elitism
- In Congress on Evolutionary Computation (CEC 2000
, 2000
"... Though it has been claimed that elitism could improve evolutionary multi-objective search significantly, a thorough and extensive evaluation of its effects is still missing. Guidelines on how elitism could successfully be incorporated have not yet been developed. This paper presents a unified model ..."
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Cited by 42 (6 self)
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Though it has been claimed that elitism could improve evolutionary multi-objective search significantly, a thorough and extensive evaluation of its effects is still missing. Guidelines on how elitism could successfully be incorporated have not yet been developed. This paper presents a unified model of multi-objective evolutionary algorithms, in which arbitrary variation and selection operators can be combined as building blocks, including archiving and re-insertion strategies. The presented model enables most specific multi-objective (evolutionary) algorithm to be formulated as an instance of it, which will be demonstrated by simple examples. We will further show how elitism can be quantified by the model's parameters and how this allows an easy evaluation of the effect of elitism on different algorithms. 1 Introduction The aim of this study is to provide a systematic approach to elitism in multi-objective evolutionary algorithms (MOEA). Multi-objective optimization can be seen as a ...
A Micro-Genetic Algorithm for Multiobjective Optimization
"... In this paper, we propose a multiobjective optimization approach based on a micro genetic algorithm (micro-GA) which is a genetic algorithm with a very small population (four individuals were used in our experiment) and a reinitialization process. We use three forms of elitism and a memory to genera ..."
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Cited by 41 (0 self)
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In this paper, we propose a multiobjective optimization approach based on a micro genetic algorithm (micro-GA) which is a genetic algorithm with a very small population (four individuals were used in our experiment) and a reinitialization process. We use three forms of elitism and a memory to generate the initial population of the micro-GA. Our approach is tested with several standard functions found in the specialized literature. The results obtained are very encouraging, since they show that this simple approach can produce an important portion of the Pareto front at a very low computational cost.
Constraint-handling in genetic algorithms through the use of dominance-based tournament selection,
- Advanced Engineering Informatics
, 2002
"... In this paper, we propose a dominance-based selection scheme to incorporate constraints into the fitness function of a genetic algorithm used for global optimization. The approach does not require the use of a penalty function and, unlike traditional evolutionary multiobjective optimization techniqu ..."
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Cited by 38 (2 self)
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In this paper, we propose a dominance-based selection scheme to incorporate constraints into the fitness function of a genetic algorithm used for global optimization. The approach does not require the use of a penalty function and, unlike traditional evolutionary multiobjective optimization techniques, it does not require niching (or any other similar approach) to maintain diversity in the population. We validated the algorithm using several test functions taken from the specialized literature on evolutionary optimization. The results obtained indicate that the approach is a viable alternative to the traditional penalty function, mainly in engineering optimization problems.
Multipoint and Multiobjective Aerodynamic
- Shape Optimization,” AIAA Journal
"... A gradient-based Newton–Krylov algorithm is presented for the aerodynamic shape optimization of single- and multi-element airfoil configurations. The flow is governed by the compressible Navier–Stokes equations in conjunction with a one-equation transport turbulence model. The preconditioned general ..."
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Cited by 35 (16 self)
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A gradient-based Newton–Krylov algorithm is presented for the aerodynamic shape optimization of single- and multi-element airfoil configurations. The flow is governed by the compressible Navier–Stokes equations in conjunction with a one-equation transport turbulence model. The preconditioned generalized minimal residual method is applied to solve the discrete-adjoint equation, which leads to a fast computation of accurate objective function gradients. Optimization constraints are enforced through a penalty formulation, and the resulting unconstrained problem is solved via a quasi-Newton method. The new algorithm is evaluated for several design examples, including the lift enhancement of a takeoff configuration and a lift-constrained drag minimization at multiple transonic operating points. Furthermore, the new algorithm is used to compute a Pareto front based on competing objectives, and the results are validated using a genetic algorithm. Overall, the new algorithm provides an efficient approach for addressing the issues of complex aerodynamic design.
Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning
- International Journal of Approximate Reasoning
, 2007
"... This paper examines the interpretability-accuracy tradeoff in fuzzy rule-based classifiers using a multiobjective fuzzy genetics-based machine learning (GBML) algorithm. Our GBML algorithm is a hybrid version of Michigan and Pittsburgh approaches, which is implemented in the framework of evolutionar ..."
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Cited by 35 (7 self)
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This paper examines the interpretability-accuracy tradeoff in fuzzy rule-based classifiers using a multiobjective fuzzy genetics-based machine learning (GBML) algorithm. Our GBML algorithm is a hybrid version of Michigan and Pittsburgh approaches, which is implemented in the framework of evolutionary multiobjective optimization (EMO). Each fuzzy rule is represented by its antecedent fuzzy sets as an integer string of fixed length. Each fuzzy rule-based classifier, which is a set of fuzzy rules, is represented as a concatenated integer string of variable length. Our GBML algorithm simultaneously maximizes the accuracy of rule sets and minimizes their complexity. The accuracy is measured by the number of correctly classified training patterns while the complexity is measured by the number of fuzzy rules and/or the total number of antecedent conditions of fuzzy rules. We examine the interpretability-accuracy tradeoff for training patterns through computational experiments on some benchmark data sets. A clear tradeoff structure is visualized for each data set. We also examine the interpretability-accuracy tradeoff for test patterns. Due to the overfitting to training patterns, a clear tradeoff structure is not always obtained in computational experiments for test patterns.
On The Effects of Archiving, Elitism, And Density Based Selection in Evolutionary Multi-Objective Optimization
- In
, 2001
"... . This paper studies the influence of what are recognized as key issues ..."
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Cited by 33 (7 self)
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. This paper studies the influence of what are recognized as key issues
