Name
Abstract | ||
|---|---|---|
Genetic algorithms are a group of powerful tools for solving ill-posed global optimization problems in continuous domains. When insensitivity in the fitness function is an obstacle, the most desired feature of a genetic algorithm is its ability to explore plateaus of the fitness function surrounding its minimizers. The authors suggest a way of maintaining diversity of the population in the plateau regions based on a new approach for selection according to the theory of multiwinner elections among autonomous agents. The article delivers a detailed description of the new selection algorithm, computational experiments that put the choice of the proper multiwinner rule to use, and a preliminary experiment showing the proposed algorithm's effectiveness in exploring a fitness function's plateau. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/MIS.2017.5 | IEEE Intelligent Systems |
Keywords | Field | DocType |
Social factors,Statistics,Artificial intelligence,Economics,Genetic algorithms | Population,Autonomous agent,Evolutionary algorithm,Computer science,Selection algorithm,Fitness function,Fitness approximation,Artificial intelligence,Cultural algorithm,Genetic algorithm,Machine learning | Journal |
Volume | Issue | ISSN |
32 | 1 | 1541-1672 |
Citations | PageRank | References |
8 | 0.56 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Piotr Faliszewski | 1 | 1395 | 94.15 |
| Jakub Sawicki | 2 | 20 | 3.68 |
| Robert Schaefer | 3 | 101 | 10.99 |
| Maciej Smołka | 4 | 107 | 13.60 |