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On handling ephemeral resource constraints in evolutionary search.
Richard Allmendinger1, Joshua Knowles
1Department of Biochemical Engineering, University College London, London, WC1E 7JE, UK. r.allmendinger@ucl.ac.uk
Evolutionary Computation
|November 21, 2012
Summary
Ephemeral resource constraints (ERCs) limit solution evaluation in closed-loop optimization. This study shows specific ERC types significantly impact evolutionary algorithms, but effective constraint-handling policies can be selected with prior knowledge of the constraint type.
Area of Science:
- Optimization Theory
- Computational Science
- Algorithm Design
Background:
- Many real-world optimization problems, particularly in closed-loop systems, involve evaluating solutions through physical processes or experiments.
- Resource limitations in these systems can restrict which solutions are evaluable at any given time, creating ephemeral resource constraints (ERCs).
- These ERCs differ from standard constraints as they dynamically alter the available solution space during the optimization process.
Purpose of the Study:
- To investigate the impact of two specific types of ephemeral resource constraints (periodic availability and commitment constraints) on optimization performance.
- To propose and evaluate novel constraint-handling policies for effectively managing ERCs in evolutionary algorithms.
- To determine if knowledge of ERC type can guide the selection of appropriate constraint-handling strategies.
Main Methods:
- An experimental study was conducted using an evolutionary algorithm framework.
- Two types of ERCs (periodic and commitment constraints) were implemented and tested.
- Five different constraint-handling policies were adapted and applied to address the ERCs across various test functions, including a real-world closed-loop problem fitness landscape.
Main Results:
- Both periodic and commitment-based ERCs were found to significantly affect the performance of the evolutionary algorithm.
- The proposed constraint-handling policies demonstrated varying degrees of effectiveness in mitigating the impact of ERCs.
- A key finding is that advance knowledge of the ERC type, even with limited fitness landscape information, is sufficient for selecting an effective handling policy.
Conclusions:
- Ephemeral resource constraints pose a significant challenge in closed-loop optimization settings.
- The choice of constraint-handling policy is crucial for maintaining optimization performance under ERCs.
- Strategic selection of constraint-handling policies based on the type of ERC can lead to improved optimization outcomes.
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