Name
Abstract | ||
|---|---|---|
In this research we present a configurable novel framework based on an enhanced heterogeneous hierarchical social fabric influence function embedded in Cultural Algorithms, as a powerful vehicle for the solution of complex problems. We motivate the discussion by investigating the extent to which these emergent phenomena are also visible within novel hybrid complex composition environments whose properties and complexity can be blurred and controlled easily, for the sake of overcoming any shortcomings of existing test functions that some of the current algorithms take advantage of during the optimization process. This environmental complexity induces an increase in the complexity of social roles within our system. We demonstrate how the well-configured hierarchical social fabric enhances Cultural Algorithms performance relative to other evolutionary algorithms from the literature. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33185-5_8 | AIMSA |
Keywords | Field | DocType |
cultural algorithms performance,cultural hierarchical social network,novel hybrid complex composition,current algorithm,complex environment,configurable novel framework,cultural algorithms,environmental complexity,social role,heterogeneous hierarchical social fabric,well-configured hierarchical social fabric,complex problem,social evolution,social networks | Social network,Evolutionary algorithm,Social evolution,Computer science,Algorithms performance,Artificial intelligence,Influence function,Machine learning,Complex problems | Conference |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mostafa Z. Ali | 1 | 252 | 19.32 |
| Robert G. Reynolds | 2 | 610 | 188.20 |