Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Niching and Elitist Models for MOGAs
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Selective Breeding in a Multiobjective Genetic Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)
Multiobjective Satisfaction within an Interactive Evolutionary Design Environment
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Reference point based multi-objective optimization using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A multi-objective evolutionary algorithm with weighted-sum niching for convergence on knee regions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Multiobjective Optimization in Bioinformatics and Computational Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Reference point based multi-objective evolutionary algorithms for group decisions
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Approximating the Knee of an MOP with Stochastic Search Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Consideration of Partial User Preferences in Evolutionary Multiobjective Optimization
Multiobjective Optimization
Multiobjective evolutionary algorithm with controllable focus on the knees of the Pareto front
IEEE Transactions on Evolutionary Computation
Multiobjective genetic algorithm-based fuzzy clustering of categorical attributes
IEEE Transactions on Evolutionary Computation
Data Clustering Using Multi-objective Differential Evolution Algorithms
Fundamenta Informaticae
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Dynamic clustering using multi-objective evolutionary algorithm
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Many-Objective optimization: an engineering design perspective
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Data Clustering Using Multi-objective Differential Evolution Algorithms
Fundamenta Informaticae
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Since the beginning of the 1990s, research and application of muitiobjective evolutionary algorithms (MOEAs) have attracted increasing attention. This is mainly due to the ability of evolutionary algorithms to find multiple Pareto-optimal solutions in one single simulation run. In this chapter, we present an overview of MOEAs and then discuss a particular algorithm in detail. Although MOEAs can find multiple Pareto-optimal solutions, often, users need to impose a particular order of priority to objectives. In this chapter, we present a few classical techniques to identify a preferred or a compromise solution, and finally suggest a biased sharing technique which can be used during the optimization phase to find a biased distribution of Pareto-optimal solutions in the region of interest. The results are encouraging and suggest further application of the proposed strategy to more complex multi-objective optimization problems.