Slides for Introduction to Stochastic Search and Optimization ( ISSO ) by J. C. Spall. CHAPTER 10 E VOLUTIONARY C OMPUTATION II : G ENERAL M ETHODS AND T HEORY. Organization of chapter in ISSO Introduction Evolution strategy and evolutionary programming; comparisons with GAs
Slides for Introduction to Stochastic Search and Optimization (ISSO)by J. C. Spall
Organization of chapter in ISSO
Evolution strategy and evolutionary programming; comparisons with GAs
Schema theory for GAs
What makes a problem hard?
No free lunch theorems
Step 0 (initialization)Randomly or deterministically generate initial population of N values of and evaluate L for each of the values.
Step 1 (offspring)Generate offspring from current population of N candidate values such that all values satisfy direct or indirect constraints on .
Step 2 (selection)For (N+)-ES, select N best values from combined population of Noriginal values plus offspring; for (N,)-ES, select N best values from population of > N offspring only.
Step 3 (repeat or terminate) Repeat steps 1 and 2 or terminate.
(above limit on left-hand side exists by ergodicity)
unique states in Suzuki (1995),
2NB states in Rudolph (1994) (much larger than number of unique states above)
(dimension of differs according to definition of states, unique or nonunique as on previous slide)
Loss Function for Example 10.2 in Analysis for GAISSOMarkov chain theory provides probability of finding solution ( = 15) in given number of iterations
NFL theorems state that the sum of above probabilities over all loss functions is independent of A