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Engineering Applications of Artificial Intelligence
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Characterizing and predicting community members from evolutionary and heterogeneous networks
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Consideration of Partial User Preferences in Evolutionary Multiobjective Optimization
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A Diversity Management Operator for Evolutionary Many-Objective Optimisation
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Advanced Engineering Informatics
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
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Advanced Engineering Informatics
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
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Dynamic multiple swarms in multiobjective particle swarm optimization
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A fast multi-objective evolutionary algorithm based on a tree structure
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Skyline queries with constraints: Integrating skyline and traditional query operators
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Expert Systems with Applications: An International Journal
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IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Multicriteria network design using evolutionary algorithm
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Applications of a multi-objective genetic algorithm to engineering design problems
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A diversity preserving selection in multiobjective evolutionary algorithms
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A generic optimising feature extraction method using multiobjective genetic programming
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A territory defining multiobjective evolutionary algorithms and preference incorporation
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SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
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A novel probabilistic encoding for EAs applied to biclustering of microarray data
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Particle swarm optimisation of interplanetary trajectories from Earth to Jupiter and Saturn
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Multi-level ranking for constrained multi-objective evolutionary optimisation
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CSR'06 Proceedings of the First international computer science conference on Theory and Applications
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In optimization, multiple objectives and constraints cannot be handled independently of the underlying optimizer. Requirements such as continuity and differentiability of the cost surface add yet another conflicting element to the decision process. While “better” solutions should be rated higher than “worse” ones, the resulting cost landscape must also comply with such requirements. Evolutionary algorithms (EAs), which have found application in many areas not amenable to optimization by other methods, possess many characteristics desirable in a multiobjective optimizer, most notably the concerted handling of multiple candidate solutions. However, EAs are essentially unconstrained search techniques which require the assignment of a scalar measure of quality, or fitness, to such candidate solutions. After reviewing current revolutionary approaches to multiobjective and constrained optimization, the paper proposes that fitness assignment be interpreted as, or at least related to, a multicriterion decision process. A suitable decision making framework based on goals and priorities is subsequently formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies. Finally, the ranking of an arbitrary number of candidates is considered. The effect of preference changes on the cost surface seen by an EA is illustrated graphically for a simple problem. The paper concludes with the formulation of a multiobjective genetic algorithm based on the proposed decision strategy. Niche formation techniques are used to promote diversity among preferable candidates, and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape