Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Multiobjective Evolutionary Algorithm for Car Front End Design
Selected Papers from the 5th European Conference on Artificial Evolution
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A new method of constraint handling for multi-objective Pareto optimization is proposed. The method is compared to an approach in which each constraint function is treated as a separate objective in a Pareto optimization. The new method reduces the dimensionality of the optimization problem by representing the constraint violations by a single "infeasibility objective". The performance of the method is examined using two constrained multi-objective test problems. It is shown that the method results in solutions that are equivalent to the constrained Pareto optimal solutions for the true objective functions. It is also concluded that the reduction in dimensionality of the problem results in a more transparent set of solutions. The method retains elegance of the underlying Pareto optimization and does not preclude the representation of a constraint as an objective function where this is considered important. The method is easily implemented and has no parameters to be tuned.