Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Online computation and competitive analysis
Online computation and competitive analysis
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Using Genetic Algorithms to Solve NP-Complete Problems
Proceedings of the 3rd International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Optimal Interaction of Mutation and Crossover in the Breeder Genetic Algorithm
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Algorithms, Clustering, and the Breaking of Symmetry
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Superimposing direct search methods for parameter optimization onto dynamic simulation models
WSC '78 Proceedings of the 10th conference on Winter simulation - Volume 1
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Evolutionary multi-objective quantum control experiments with the covariance matrix adaptation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Closed-loop evolutionary multiobjective optimization
IEEE Computational Intelligence Magazine
Experimental optimization by evolutionary algorithms
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Learning the large-scale structure of the MAX-SAT landscape using populations
IEEE Transactions on Evolutionary Computation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Policy learning in resource-constrained optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Multiobjective evolutionary algorithm for the optimization of noisy combustion processes
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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We consider optimization problems where the set of solutions available for evaluation at any given time t during optimization is some subset of the feasible space. This model is appropriate to describe many closed-loop optimization settings i.e., where physical processes or experiments are used to evaluate solutions where, due to resource limitations, it may be impossible to evaluate particular solutions at particular times despite the solutions being part of the feasible space. We call the constraints determining which solutions are non-evaluable ephemeral resource constraints ERCs. In this paper, we investigate two specific types of ERC: one encodes periodic resource availabilities, the other models commitment constraints that make the evaluable part of the space a function of earlier evaluations conducted. In an experimental study, both types of constraint are seen to impact the performance of an evolutionary algorithm significantly. To deal with the effects of the ERCs, we propose and test five different constraint-handling policies adapted from those used to handle standard constraints, using a number of different test functions including a fitness landscape from a real closed-loop problem. We show that knowing information about the type of resource constraint in advance may be sufficient to select an effective policy for dealing with it, even when advance knowledge of the fitness landscape is limited.