2. March 23, 2003 AAAI symposium, Stanford. Jianjun Hu, Erik D. Goodman, Kisung Seo Zhun Fan, Ronald C. Rosenberg Genetic Algorithm Research & Applications Group (GARAGe) Michigan State University HFC: a Continuing EA Framework for Scalable Evolutionary Synthesis

3. March 23, 2003 AAAI symposium, Stanford. Evolutionary Synthesis: the problem Topology innovation: How many and what types of building blocks, How to connect them? Parameter innovation: How to size the elements? R S CI R1 Input: >Building blocks >Function Specification (Evaluation function) >EA settings (operators, parameters…) Output: Design solutions Evolution

4. March 23, 2003 AAAI symposium, Stanford. Sustainable & scalable evolutionary synthesis: Definition and Reality Definition: the capability to obtain better results or solve larger scale problems when given more computing resources Reality: not sustainable and not scalable Bloating and aging problem (e.g.:Innovation in 50 generations in GP) Demand for huge population size (GP) Premature convergence and local optima (all EA) But biological evolution is sustainable and scalable!

5. March 23, 2003 AAAI symposium, Stanford. Sustainable & scalable evolutionary synthesis: two types of obstacles Two types of obstacles: 1. Convergent nature of current EA framework one of major factors leading to : GP aging phenomenon premature convergence dependence on huge population size 2. Non-scalable compositional mechanisms for topological and parametric evolution possible solutions: modularity, hierarchical organization, biological developmental principles… This paper: addresses the EA convergence problem

6. March 23, 2003 AAAI symposium, Stanford. Comparison with biological evolution: how to achieve sustainable evolution Biological evolution Almost infinite population size Simultaneous evolution of all levels of organisms: bacteria coexist with humans Fair competition: different levels of organism coexist in different niches Sustainable innovation possible Artificial evolution (EA) Limited population size Focusing on current high- fitness individuals Unfair competition: highly- evolved individuals compete with new offspring of low fitness Innovation capability rapidly depleted

7. March 23, 2003 AAAI symposium, Stanford. Sustainable evolution: example & comparison Sustainable education system: how generations of talents are educated? Unsustainable EA: how generations of solutions are evolved?

8. March 23, 2003 AAAI symposium, Stanford. Assembly-line structured continuing EAs

9. March 23, 2003 AAAI symposium, Stanford. HFC-EA framework

10. March 23, 2003 AAAI symposium, Stanford. System synthesis problem: eigenvalue placement -2.0 -3.3 -2.0 3.3 -7.5 -4.5 -7.5 4.5 -3.5 -12.0 -3.5 12.0 -3.4 -12.0 -3.4 12.0 -10.0 -8.0 -10.0 8.0 -2.063 -3.005 -2.063 3.005 -7.205 -4.581 -7.205 4.581 -3.580 -11.835 -3.580 11.835 -3.334 -12.526 -3.334 12.526 -9.997 -8.040 -9.997 8.040 Max distance error 0.530 Average distance error 0.272 2 hours Sf 1Se1 1 0 C R I 1 R C I 0 R C C 0 RLRL

11. March 23, 2003 AAAI symposium, Stanford. Experiment result: sustainability& robustness Dynamic system synthesis problem with simultaneous topology and parameter search

12. March 23, 2003 AAAI symposium, Stanford. Experiment result: handling GP aging problem 10 eigenvalue dynamic system synthesis problem

13. March 23, 2003 AAAI symposium, Stanford. Experiment result: small population size works equally well 10-parity problem with function set {and, xor, or, not)

14. March 23, 2003 AAAI symposium, Stanford. Why HFC works: the explanation

15. March 23, 2003 AAAI symposium, Stanford. Conclusion: Hierarchical Fair Competition Principle for EA HFC is very effective in evolutionary synthesis Simultaneous evolution at all (fitness) levels, from the random population to best individuals Avoid premature convergence by allowing emergence of new optima rather than trying to jump out of local optima Allow use of strong selection pressure without risk of premature convergence Small population size also works

16. March 23, 2003 AAAI symposium, Stanford. Ongoing & future work Developing single population HFC (CHFC) (with continuous level segmentations) algorithm to achieve sustainable evolution Developed HFC-enhanced multi-objective EAs (GECCO2003) To develop hybrid parallel-HFC GA/GP system where each deme is implemented as a CHFC population To develop multi-level system synthesis: from framework evolution to complete solutions

17. March 23, 2003 AAAI symposium, Stanford. Generalization of HFC-EA Framework A generic framework for continuing sustainable evolutionary computation (GA, GP, ES, …) Especially good for Evolutionary Synthesis for Sustainable topological innovation which has extremely rugged fitness landscape. Especially effective for problems with: High multi-modality Strong tendency of premature convergence Requirement on robustness Requirement on adaptation in dynamic environment Also applicable for artificial life evolution

18. March 23, 2003 AAAI symposium, Stanford. Scaling Mechanism of HFC A natural parallel evolutionary computation model. Better than island parallel model Hybridizing with single-population HFC EAs (Each deme is a sustainable HFC subpop) Natural hybridizing with explicit hierarchical building block discovery mechanisms Allow using small population size and longer time to achieve good results No restart to waste computing effort