Genetic Algorithms

1. Genetic Algorithms ML 9 Kristie Simpson CS536: Advanced Artificial Intelligence Montana State University

2. Overview • Learning based on simulated evolution. • Begins with initial population of hypotheses. • Population uses genetic operations to create the next generation. • Most fit hypotheses survive.

3. 9.1 Motivation • Successful, robust method for biological systems. • Hypotheses can contain complex interacting parts, where the impact of each part may be difficult to model. • Easily parallelized. • Easily understood.

4. Terminology • Population – collection of hypotheses. • Fitness – numerical measure of the strength of a hypothesis. • Crossover (recombination) – two or more hypotheses combine to create one or more offspring. • Mutation – random modifications to individual hypotheses.

5. 9.2 Genetic Algorithms • Table 9.1 pg. 251 • Population of initial hypotheses. • Fitness of each hypothesis is determined. • Create new generation. • Most fit hypotheses selected. • Crossover combines hypotheses to create offspring. • Mutation modifies individual hypotheses. • Update population.

6. Where have we seen this before? • ACO 2.4.6 (pg. 55-57) Evolutionary Computation (Other Metaheuristics) • ACO 3.7.1 (pg. 93-99) Lamarckian vs. Darwinian (ACO plus Local Search) • ML 3.6.2 (pg. 65-66) Occam’s razor (Inductive Bias)

7. 9.2.1 Representing Hypotheses • Hypotheses in GAs are often represented by bit-strings. IF Wind = Strong THEN PlayTennis = yes Outlook Wind PlayTennis 111 10 10

8. 9.2.2 Genetic Operators

9. 9.2.3 Fitness Function and Selection fitness proportionate selection: http://en.wikipedia.org/wiki/Fitness_proportionate_selection

10. 9.3 An Illustrative Example • GABIL uses a GA to learn boolean concepts represented by a disjunctive set of propositional rules. • Hypotheses represented by bit-strings which grow with the number of rules. • Variable length bit-strings requires modification to the crossover rule.

11. GAs for the TSP • http://www.ads.tuwien.ac.at/raidl/tspga/TSPGA.html

12. 9.4 Hypothesis Space Search • GAs do not move smoothly from hypothesis to hypothesis (like Backpropagation). • Instead, they move much more abruptly and are less likely to fall into local minima. • Problem: crowding - highly fit individuals take over population. • Solution: alter the selection function (tournament, rank, fitness sharing, subspecies)

13. 9.4.1 Population Evolution and the Schema Theorem • Mathematically characterize evolution. • Schemas – patterns that describe sets of bit strings (0s, 1s, *’s). • Evolution depends on selection, recombination, and mutation.

14. 9.5 Genetic Programming • Extends genetic algorithms to the evolution of complete computer programs. • Population consists of computer programs rather than bit-strings. • Population of hypotheses typically represented by parse trees. • Fitness determined by executing the program on training data. • Crossover performed by swapping subtrees.

15. 9.6 Models of Evolution and Learning • What is the relationship between learning during the lifetime of a single individual, and the longer time frame species-level learning afforded by evolution? • Lamarckian evolution – experiences of a single organism directly affect the genetic makeup of their offspring. • Scientific evidence contradicts this model.

16. 9.6.2 Baldwin Effect • Evolution favors individuals with the capability to learn. • Individuals who learn rely less strongly on their genetic code. • Individual learning supports more rapid evolutionary progress, thereby increasing the chance that the species will evolve genetic, non-learned traits.

17. 9.7 Parallelizing Genetic Algorithms • GAs naturally suited to parallel implementation. • Coarse grain – subdivide population into groups of individuals (demes). • Migration – individuals from one deme are copied/transferred to other demes. • Fine grain – one processor per individual in the population. • Recombination occurs among neighboring individuals.