Abstract
This paper is about the cooperative co-evolutionary algorithms. This paper investigates cooperative co-evolutionary algorithms (CoEAs) for large-scale optimization problems, focusing on the runtime analysis to understand their behavior. By proving that the basic cooperative co-evolutionary (1+1) EA has an expected optimization time of Θ(n log n) on linear functions, it solves an open conjecture. Empirical analysis on NK-LANDSCAPE and k-MAXSAT problems shows that performance can be optimized by adjusting block length, providing more precise runtime bounds and insights on more complicated inseparable problems.
Original language | English |
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Title of host publication | IEEE Congress on Evolutionary Computation |
Publisher | IEEE |
Number of pages | 9 |
ISBN (Electronic) | 9798350314588 |
ISBN (Print) | 9798350314595 |
DOIs | |
Publication status | Published - 25 Sept 2023 |
Event | IEEE 2023 Congress on Evolutionary Computation - Swissotel Chicago, Chicago, United States Duration: 1 Jul 2023 → 5 Jul 2023 |
Conference
Conference | IEEE 2023 Congress on Evolutionary Computation |
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Abbreviated title | CEC 2023 |
Country/Territory | United States |
City | Chicago |
Period | 1/07/23 → 5/07/23 |
Keywords
- coevolution