1. 5. Implementing a GA 4 학습목표 GA 를 사용해 실제 문제를 해결할 때 고려해야 하는 사항에 대해 이해한다 Huge number of choices with little theoretical guidance Implementation issues + sophisticated GA techniques

2. Outline  When should a GA be used?  Encoding a problem for a GA  Adapting the encoding  Selection methods  Genetic operators  Parameters for GAs

3. When should a GA be Used?  Search space  large  known not perfectly smooth and unimodal  not well understood  Fitness function  noisy  Task  not require global optimum

4. Encoding a Problem for a GA (1)  Binary encodings: historical but unnatural for many problems  Extensions  Bethke’s gray coding  Hillis’s diploid binary encoding scheme  Holland’s theoretical justification for binary encoding  Small number of alleles and long strings  higher implicit parallelism  Large number of alleles and short strings Fixed-length, fixed-order bit strings

5. Encoding a Problem for a GA (2)  Many-character and Real-valued encodings  Kitano’s many-character rep for graph-generation grammar  Meyer’s real-valued rep for condition sets  Montana’s real-valued rep for neural-network weights  Schultz-Kremer’s real-valued rep for torsion angles in proteins  Tree encodings  Koza’s scheme for representing computer programs  open-ended search space  Guessing at an appropriate encodings and trying out a GA Trial and Error!!  Adapting the encoding!!

6. Adapting the Encoding (1)  Inversion  Deal with the inkage problem in fixed-length strings  Reordering operator: each allele be given an index indicating its “real” position  produce orderings where beneficial schemas are more likely to survive  Full set of loci after crossover?  permit crossover only between chromosomes with the same permutation of the loci  employ a “master/slave” approach  Applications: ordering problems like DNA fragment assembly Linkage problem (how best to order the bits ahead of time)  Functionally related loci be more likely to stay together on the string under crossover  No change of fitness but linkages

7. Adapting the Encoding (2)  Evolving crossover “Hot Spots”  evolve not the order of bits but the positions where crossover was allowed  mutation on both chromosomes and attached crossover templates  coevolve good crossover templates  Messy GAs  explicitly building up increasingly longer, highly fit strings from well-tested shorter building blocks  each bit is tagged with its “real” locus  Overspecification problem: left-to-right, first-come-first- served scheme  candidate schema  evolve candidate schemas, gradually building up longer ones until a solution is formed

8. Selection Methods  Exploitation / exploration balance  Selection has to be balanced with variation from crossover and mutation  Too strong selection: suboptimal highly fit individuals  reducing diversity  Too-weak selection: too slow evolution  Fitness-proportionate Selection  Expected value of an individual = its fitness / average fitness  Roulette wheel sampling: bad in small populations  Stochastic universal sampling: spin the wheel once, but with N equally spaced pointers for N parents  Problem: premature convergence (some multiply quickly in population)  too much emphasis on exploitation of highly fit

9. Selection Methods (2)  Sigma Scaling  mapping raw fitness values to expected values to prevent premature convergence  Elitism  retain some number of best individuals at each generation  Boltzmann Selection  different amounts of selection pressure for different times in a run  Rank Selection  expected value of individual depends on rank  Tournament Selection  Steady-state Selection

10. Genetic Operators  Crossover  single point, two-point crossover, …  recombine highly fit schemas  Mutation  known as less important, but balance among crossover, mutation, and selection is important  Other Operators and Mating Strategies  Crowding operator: newly formed offspring replaced the existing individual most similar to itself  preventing too many similar individuals (crowds)  Fitness sharing: each individual’s fitness was decreased by the presence of other population members  Speciation!  Restrictions on mating for diversity: only sufficiently similar individuals are allowed to mate  mating tag approach

11. Parameters for GAs  population size, crossover rate, mutation rate  typically interact with one another nonlinearly  no way to optimize one at a time  De Jong’s guideline  population size: 50~100 individuals  crossover rate: ~0.6  mutation rate: 0.001  Meta-level GA  population size: 30  crossover rate: 0.95  mutation rate: 0.01  elitist selection  Parameter values adapt in real time to the ongoing search  self-adaptation methods required!

12. Schedule for Remaining Lectures  11/5: Co-Evolution and Speciation  11/10: Interactive Evolutionary Computation  11/12: 정태민, 차옥균  11/17: 이승현  11/19: 황주원, 윤종원  11/24: 박지인  11/26: 고유선, 오근현  12/1: 안재균  12/3: 박수상, 이영설  12/10: Term Project 최종발표