Introduction To Learnable Evolution Model

1. Introduction To Learnable Evolution Model Advisor: Dr. Mirzaei MohammadTaghiMoein Isfahan University of Technology

2. The Scheme of Evolution Model New Individuals Current Population New Population

3. Generating New Individuals in Darwinian-Type Evolution Model Generate New Individuals by mutation and recombination Current Population Candidate Parents

4. Generating New Individuals in LEM H-group High Performance Individuals Generate hypothesis for H-group Generate New Individuals by Instantiating the hypothesis Current Population L-group Low Performance Individuals Generate hypothesis for L-group

5. Extrema Generation • fitness-based • according to two thresholds, called HFT and LFT

6. Extrema Generation Cont. • population-based • according to two parameters, called HPT and LPT

7. Extrema Generation Cont. • The fitness-based and population-based methods can also be used in combination. • a global approach applies one of the above methods to the entire population. • a local approach applies one of the above methods in parallel to different subsets of the population. • The above methods can be enhanced by employing elitism.

8. Extrema Generation Cont. • In the above methods, the H-group and L-group were selected only from the current population. • H-group description that does not take into consideration past L-groups is likely to be too general. • L-group description that does not take into consideration past L-groups is likely to be too specific.

9. Considering History of Evolution • Population-lookback • union of the past L-groups plus the L-group in the current population is the actual L-group. • The number of past L-groups is specified by the p-lookback parameter. • High-group description-lookback • current H-group description is used to generate new candidate individuals. • past H-group descriptions serve as preconditions for accepting a candidate. • The number of H-group descriptions is specified by the d-lookback parameter.

10. Considering History of Evolution Cont. • Low-group description-lookback • maintains a collection of descriptions of L-groups. • uses them as constraints when generating H-group descriptions. • Incremental specialization • uses incremental learning algorithm to maintain one updated description of the H-group. • input to such an algorithm is a description of the previous H-group.

11. Generating Description(AQ) Seed Selection Star Generation Rule Selection Coverage Update any positive example Yes No Finish

12. Description instantiation • New individuals should satisfy all H-group descriptions. • A description instantiation is done by assigning different combinations of values to variables in the rules of a ruleset. • Each assignment must satisfy all conditions in at least one of the rules.

13. LEM Algorithm • Generate a population • Execute machine learning mode • Derive extrema • Create a hypothsis • Generate new individuals • Go to step (2-a) and continue until termination condition is met, if termination condition is met do: • If the LEM termination condition is met , end the evolution. • Repeate the process from step 1, this is called start-over . • Go to step 3

14. LEM Algorithm Cont. • Execute Darvinian Evolution mode • Alternate: Go to step 2, and continue alternating between step 2 and step 3 until the LEM termination condition is met.

15. Generating start-over population • Select-elite • Avoid-past-failures • Use-recommendatoins • Generate-a-variant

16. Any Questions? Thanks for your attention