Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
A Short Tutorial on Evolutionary Multiobjective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Genetic algorithms with a robust solution searching scheme
IEEE Transactions on Evolutionary Computation
A Steady-State Genetic Algorithm with Resampling for Noisy Inventory Control
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Multiobjective Optimization
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
An evolutionary computing approach to robust design in the presence of uncertainties
IEEE Transactions on Evolutionary Computation
Accumulative sampling for noisy evolutionary multi-objective optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Multi-objective reliability-based optimization with stochastic metamodels
Evolutionary Computation
A preliminary study on handling uncertainty in indicator-based multiobjective optimization
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Searching for robust pareto-optimal solutions in multi-objective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Two metaheuristics for multiobjective stochastic combinatorial optimization
SAGA'05 Proceedings of the Third international conference on StochasticAlgorithms: foundations and applications
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
Quantum control experiments as a testbed for evolutionary multi-objective algorithms
Genetic Programming and Evolvable Machines
On optimizing a bi-objective flowshop scheduling problem in an uncertain environment
Computers & Mathematics with Applications
A non-parametric statistical dominance operator for noisy multiobjective optimization
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
S-Race: a multi-objective racing algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Multi-objective optimization with estimation of distribution algorithm in a noisy environment
Evolutionary Computation
Information Sciences: an International Journal
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Real engineering optimisation problems are often subject to parameters whose values are uncertain or have noisy objective functions. Techniques such as adding small amounts of noise in order to identify robust solutions are also used. The process used in evolutionary algorithms to decide which solutions are better than others do not account for these uncertainties and rely on the inherent robustness of the evolutionary approach in order to find solutions. In this paper, the ranking process needed to provide probabilities of selection is re-formulated to begin to account for the uncertainties and noise present in the system being optimised. Both single and multi-objective systems are considered for rank-based evolutionary algorithms. The technique is shown to be effective in reducing the disturbances to the evolutionary algorithm caused by noise in the objective function, and provides a simple mathematical basis for describing the ranking and selection process of multi-objective and uncertain data.