1. Futility-Based Offspring Sizing André Nwamba June 13, 2015

2. Parameter Tuning Requires expert knowledge of EAs Time Consuming Sub-optimal

3. The Problem Dire need for automation Completely Parameterless EA Remove the need to specify offspring size, λ

4. The Solution FuBOS: Futility-Based Offspring Sizing Minimize wasted computation effort

5. The Solution Look at change in average fitness of the offspring  Change in average fitness not best metric Average fitness of all n offspring Average fitness of n-1 previously created offspring Threshold value

6. Experimental Setup Compared FuBOS-EA and manually tuned EA (OOS-EA)  FuBOS-EA uses.001 for epsilon Test problems: DTRAP, SAT, and ONEMAX Used population sizes of 100, 500, 1000 All tests used same parameters Performance compared using One-Way ANOVA

7. Experimental Setup ParameterValue InitializationEach bit is initialized to either a 0 or 1 with a uniform probability Parent SelectionRandom Survivor SelectionTruncation RecombinationUniform Crossover for SAT and ONEMAX and 2-point crossover for DTRAP Mutation Rate1/l (l being the length of the bitstring) Termination Condition100000 fitness evaluations for SAT and DTRAP, 25000 for ONEMAX

8. Results

13. Future Work The “epsilon problem” Fitness Diversity Parent Selection Combine with dynamic population sizing

14. Questions?