EEEA
1.0.1Explicit Exploration Strategy for Evolutionary Algorithms
Overview
Implements an explicit exploration strategy for evolutionary algorithms in order to have a more effective search in solving optimization problems. Along with this exploration search strategy, a set of four different Estimation of Distribution Algorithms (EDAs) are also implemented for solving optimization problems in continuous domains. The implemented explicit exploration strategy in this package is described in Salinas-Gutiérrez and Muñoz Zavala (2023) doi:10.1016/j.asoc.2023.110230.
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Health
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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Code & Tests
- Cyclomatic complexity
- 1.0 median / 4 max
- Documented parameters
- 100%
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
28 21 exported
Complexity
1.6 avg / 4 max
Call network
28 nodes / 14 edges
Test coverage has not been measured for this package yet; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.0.1Latest
- 1.0.02025-04-24
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-04-24
- Total releases
- 2 / 1 yrs
- License
- GPL-3 OSI
- Download size
- 12 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet