Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Multi-objective test problems, linkages, and evolutionary methodologies
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Covariance Matrix Adaptation for Multi-objective Optimization
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
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
An Evolutionary Metaheuristic for Approximating Preference-Nondominated Solutions
INFORMS Journal on Computing
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
IEEE Transactions on Evolutionary Computation
A Jumping Gene Paradigm for Evolutionary Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
IEEE Transactions on Evolutionary Computation
Dominance-Based Multiobjective Simulated Annealing
IEEE Transactions on Evolutionary Computation
AbYSS: Adapting Scatter Search to Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
A territory defining multiobjective evolutionary algorithms and preference incorporation
IEEE Transactions on Evolutionary Computation
Bicriteria p-Hub Location Problems and Evolutionary Algorithms
INFORMS Journal on Computing
An interactive territory defining evolutionary algorithm: iTDEA
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
A multi-criteria sorting procedure with Tchebycheff utility function
Computers and Operations Research
High-dimensional objective optimizer: An evolutionary algorithm and its nonlinear analysis
Expert Systems with Applications: An International Journal
Diversity Guided Evolutionary Programming: A novel approach for continuous optimization
Applied Soft Computing
On the performance metrics of multiobjective optimization
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
A Modified micro Genetic Algorithm for undertaking Multi-Objective Optimization Problems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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In this paper, we present a favorable weight-based evolutionary algorithm for multiple criteria problems. The algorithm tries to both approximate the Pareto frontier and evenly distribute the solutions over the frontier. These two goals are common for many multiobjective evolutionary algorithms. To achieve these goals in our algorithm, each member selects its own weights for a weighted Tchebycheff distance function to define its fitness score. The fitness scores favor solutions that are closer to the Pareto frontier and that are located at underrepresented regions. We compare the performance of the algorithm with two leading evolutionary algorithms on various continuous test problems having different number of criteria.