Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Applied Intelligence
Multiobjective-Based Concepts to Handle Constraints in Evolutionary Algorithms
ENC '03 Proceedings of the 4th Mexican International Conference on Computer Science
Multiobjective Evolutionary Algorithms and Applications (Advanced Information and Knowledge Processing)
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multi-Objective Machine Learning (Studies in Computational Intelligence) (Studies in Computational Intelligence)
Evolutionary algorithms in multiply-specified engineering. The MOEAs and WCES strategies
Advanced Engineering Informatics
Introducing robustness in multi-objective optimization
Evolutionary Computation
EURASIP Journal on Applied Signal Processing
Conflict, harmony, and independence: relationships in evolutionary multi-criterion optimisation
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Many-Objective optimization: an engineering design perspective
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
On the performance of (1, λ)-evolution strategies for theridge function class
IEEE Transactions on Evolutionary Computation
Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multiobjective Evolutionary Algorithms: Applications in Real Problems
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
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This paper addresses a real-world engineering design requiring the application of effective and global optimization techniques. The problem it deals with is the design of nonlinear tracking filters under up to several hundreds of performance specifications. The suitability of different evolutionary computation techniques for solving multiobjective problems is explored, contrasting the performance achieved with recent multiobjective evolutionary algorithm (MOEAs) proposals and different aggregation schemes. In particular, a new scheme is proposed to build a fitness function based on an operator that selects worst cases of multiple specifications in different situations. They have been evaluated in the design of an air traffic control (ATC) tracking filter that should accomplish a specific normative with 264 specifications. Results show their performance in terms of effectiveness and computational load, comparing their capability to scale the problem with respect to problem size.