| Title |
Populations Can Be Essential in Tracking Dynamic Optima
|
|---|---|
| Published in |
Algorithmica, August 2016
|
| DOI | 10.1007/s00453-016-0187-y |
| Pubmed ID | |
| Authors |
Duc-Cuong Dang, Thomas Jansen, Per Kristian Lehre |
| Abstract |
Real-world optimisation problems are often dynamic. Previously good solutions must be updated or replaced due to changes in objectives and constraints. It is often claimed that evolutionary algorithms are particularly suitable for dynamic optimisation because a large population can contain different solutions that may be useful in the future. However, rigorous theoretical demonstrations for how populations in dynamic optimisation can be essential are sparse and restricted to special cases. This paper provides theoretical explanations of how populations can be essential in evolutionary dynamic optimisation in a general and natural setting. We describe a natural class of dynamic optimisation problems where a sufficiently large population is necessary to keep track of moving optima reliably. We establish a relationship between the population-size and the probability that the algorithm loses track of the optimum. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 11% |
| Unknown | 8 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 33% |
| Lecturer > Senior Lecturer | 1 | 11% |
| Student > Doctoral Student | 1 | 11% |
| Lecturer | 1 | 11% |
| Student > Bachelor | 1 | 11% |
| Other | 1 | 11% |
| Unknown | 1 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 6 | 67% |
| Mathematics | 1 | 11% |
| Agricultural and Biological Sciences | 1 | 11% |
| Engineering | 1 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 August 2016.
All research outputs
#13,796,561
of 22,903,988 outputs
Outputs from Algorithmica
#322
of 419 outputs
Outputs of similar age
#186,389
of 338,674 outputs
Outputs of similar age from Algorithmica
#3
of 6 outputs
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