2022 Proceeding- Editor:
- Jonathan E. Fieldsend,
- General Chair:
- Markus Wagner
GECCO is the leading, peer-reviewed conference in the field of evolutionary computation, and the main conference of the Special Interest Group on Genetic and Evolutionary Computation (SIGEVO) of the Association for Computing Machinery (ACM).
Modern hybrids - optimization using local search, population-based search, and machine learning
This talk summarizes our 15+ years of work on the use of Machine Learning for Search & Optimization. I review the four main approaches that we invented during this time. Since learning during search takes effort, it should not surprise that we designed ...
Emotion, social robots, and a new human-robot relationship
People have welcomed conversational AI technologies into our homes, workplaces, and institutions where we interact with them on a daily basis. The proliferation of digital assistants in a multitude of embodiments (e.g., speakers, displays, avatars, ...
An evolutionary optimizer's path to commercial success and some rocket science beyond it
Few EC technologies have gone from universities to commercial success. Goodman will describe the SHERPA algorithm, part of the HEEDS design exploration framework, and how Red Cedar Technology, which he co-founded, eventually succeeded. Beginning 20 years ...
Recommendations
Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| GECCO '17 | 462 | 178 | 39% |
| GECCO '16 | 381 | 137 | 36% |
| GECCO '16 Companion | 381 | 137 | 36% |
| GECCO '15 | 505 | 182 | 36% |
| GECCO '14 | 544 | 180 | 33% |
| GECCO Comp '14 | 544 | 180 | 33% |
| GECCO '13 | 570 | 204 | 36% |
| GECCO '07 | 577 | 266 | 46% |
| GECCO '06 | 446 | 205 | 46% |
| Overall | 4,410 | 1,669 | 38% |