Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Intelligent Zoning Design Using Multi-Objective Evolutionary Algorithms
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Evolution of a 3D Gallop in a Quadrupedal Model with Biological Characteristics
Journal of Intelligent and Robotic Systems
MFGA: a GA for complex real-world optimisation problems
International Journal of Innovative Computing and Applications
A fast steady-state ε-dominance multi-objective evolutionary algorithm
Computational Optimization and Applications
Compressed-objective genetic algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Journal of Mathematical Modelling and Algorithms
EA'05 Proceedings of the 7th international conference on Artificial Evolution
A two-level evolutionary approach to multi-criterion optimization of water supply systems
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
ICES'05 Proceedings of the 6th international conference on Evolvable Systems: from Biology to Hardware
Genetic algorithms for estimating longest path from inherently fuzzy data acquired with GPS
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
AMI Screening Using Linguistic Fuzzy Rules
Journal of Medical Systems
Design of a Fuzzy-based Decision Support System for Coronary Heart Disease Diagnosis
Journal of Medical Systems
A Multiobjective Particle Swarm Optimizer for Constrained Optimization
International Journal of Swarm Intelligence Research
Computers and Industrial Engineering
Engineering Applications of Artificial Intelligence
A comparative evaluation of multi-objective exploration algorithms for high-level design
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Adaptive heuristic search algorithm for discrete variables based multi-objective optimization
Structural and Multidisciplinary Optimization
A multi-objective hyper-heuristic based on choice function
Expert Systems with Applications: An International Journal
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Preserving elitism is found to be an important issue in the study of evolutionary multi-objective optimization (EMO). Although there exists a number of new elitist algorithms, where elitism is introduced in different ways, the extent of elitism is likely to be an important matter. The desired extent of elitism is directly related to the so-called exploitation-exploration issue of an evolutionary algorithm (EA). For a particular recombination and mutation operators, there may exist a selection operator with a particular extent of elitism that will cause a smooth working of an EA. In this paper, we suggest an approach where the extent of elitism can be controlled by fixing a user-defined parameter. By applying an elitist multi-objective EA (NSGA-II) to a number of difficult test problems, we show that the NSGA-II with controlled elitism has much better convergence property than the original NSGA-II. The need for a controlled elitism in evolutionary multi-objective optimization, demonstrated in this paper should encourage similar or other ways of implementing controlled elitism in other multi-objective evolutionary algorithms.