As a general comment, the talks on the Evolution session in #ALIFE2023 have been super interesting as a #gecco2023 person, because the talks here seem to involve a lot of lateral thinking about EC, while the GECCO talks felt more like going deeper in well threaded paths.

After the talks here, I feel like asking myself questions like "but what are the implications of this paper for algorithm X or algorithm Y", and the answers are always fun to think about...

Fun facts from the closing notes:
- on-site attendants are ~57 x more heavy in CO2 emissions over 5 days than online, making all attempts to limit emissions outside travel all but meaningless

49/🧵 #GECCO2023 #GECCO

23. Black/Gray/White Box applications:
- High-intensity applications:
- Spiking NNs to data
- Mapping protein energy landscapes

24. Mix all of the above & Evolve by interacting with other communities.

48/🧵 #GECCO2023 #GECCO

19. Hybrid systems:
- Neuroevolution
- Evo-optimized gradient descent

20. Use of cultural artifacts instead of population:
- Hall of Fame of populations
- Archives of populations
- Multi-run memory
- auto-restarts allowing to learn from previous runs (meta-learning)

21. Landscapes analysis
- Landscape categories
- Exploit landscape structures
- Meta-EA for landscapes exploitations
- Adaptive EAs for Landscapes

22. "No Free Lunch"
- Algo/Problem fit

47/🧵 #GECCO2023 #GECCO

17. Rant about communication: Generative Learning is sexy, but not the EA flavor:

=> Tbh, the sexy part of generative learning is the same as was for CovNets; it's the fact that it works, and it works outside academia; which is not the case for EA-based solutions

18. Time-varying environment for the optimization problems: adaptation and tracking.

15.2. Evolving Agent-based models:
- Robotics
- Cyber-Sec
- Viral Evolution
=> that's Mut. Landscapes, not EC

46/🧵 #GECCO2023 #GECCO

13. Practitioner-friendliness:
- Design
- Parallelization:
(=> Consensus FTW)
- Isolated islands to be taken advantage on
- finely grained/cellular models
- Asynchrony
(=> Consensus FTW)

14. Co-evolutionary settings:
- Arms race not achieved yet (GANs failed)
- Instead a transation towards solving Min-Max Problems
- Cooperative co-evolution

15. Agent-based models with evolution

16. Transition from parameters to design spaces to design patterns

45/🧵 #GECCO2023 #GECCO

8. Unification: Evolutionary Computation book by Kenneth De Jong; definition of EAs and domains as algorithms instantiated from heuristics

9. Transition from named to property-base EAs

10. EAs got too complicated. Algorithms got explosively complicate due to meta-parameters, no visibility for eg biologists;

11. Evolution towards robust EAs; to self-adapting EAs;

12. Automating EA design itself => meta/hyper-heuristics

44/🧵 #GECCO2023 #GECCO