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On Handling Ephemeral Resource Constraints in Evolutionary Search
- Source :
- Evolutionary Computation. 21:497-531
- Publication Year :
- 2013
- Publisher :
- MIT Press - Journals, 2013.
-
Abstract
- 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.
- Subjects :
- Mathematical optimization
Time Factors
Optimization problem
Models, Genetic
Computer science
Fitness landscape
media_common.quotation_subject
Constrained optimization
Evolutionary algorithm
Computational Biology
Epistasis, Genetic
Biological Evolution
Evolutionary computation
Computational Mathematics
Resource (project management)
Computer Simulation
Function (engineering)
Constraint (mathematics)
Algorithms
Software
media_common
Subjects
Details
- ISSN :
- 15309304 and 10636560
- Volume :
- 21
- Database :
- OpenAIRE
- Journal :
- Evolutionary Computation
- Accession number :
- edsair.doi.dedup.....07b09c1250b33b3807c0dfc4faf415f0