Abstract
Novelty search is a tool in evolutionary and swarm robotics for maintaining the diversity of population needed for continuous robotic operation. It enables nature-inspired algorithms to evaluate solutions on the basis of the distance to their k-nearest neighbors in the search space. Besides this, the fitness function represents an additional measure for evaluating the solution, with the purpose of preserving the so-named novelty solutions into the next generation. In this study, a differential evolution was hybridized with novelty search. The differential evolution is a well-known algorithm for global optimization, which is applied to improve the results obtained by the other solvers on the CEC-14 benchmark function suite. Furthermore, functions of different dimensions were taken into consideration, and the influence of the various novelty search parameters was analyzed. The results of experiments show a great potential for using novelty search in global optimization.
| Original language | English |
|---|---|
| Pages (from-to) | 865-881 |
| Number of pages | 17 |
| Journal | Applied Mathematics and Computation |
| Volume | 347 |
| DOIs | |
| Publication status | Published - 15 Apr 2019 |
Keywords
- Artificial life
- Differential evolution
- Evolutionary robotics
- Novelty search
- Swarm intelligence