Let’s Get Ready to Rumble Redux:
 Crossover vs. Mutation Head to Head
  on Exponentially-Scaled Problems
       Kumara Sas...
Motivation
Great debate between crossover and mutation
When mutation works, it’s lightning quick
When crossover works, it ...
Outline

Related work
Assumption of known or discovered linkage
Objective
Algorithm Description
Scalability analysis: Cros...
Background

Emprical studies comparing crossover and mutation

Scalability of GAs and mutation-based hillclimber
[Mühlenbe...
Known or Discovered Linkage

Assumption of known or induced linkage
   Can use linkage-learning techniques
Linkage informa...
Algorithm Description

Selectorecombinative genetic algorithm
  Population of size n
  Binary tournament selection
  Unifo...
Crossover Versus Mutation: Uniform Scaling

Deterministic fitness:               Noisy fitness: Recombination
Mutation is ...
Objective

Crossover and mutation both have access to same
neighborhood information
  Known or discovered linkage
  Recomb...
Scaling and Noise Cover Most Problems
 Adversarial problem design [Goldberg, 2002]


                       Fluctuating


...
Convergence Time for Crossover:
                Deterministic Fitness Functions

Selection-Intensity based model [Rudnick,...
Population Sizing for Crossover:
           Deterministic Fitness Functions

Domino convergence [Rudnick, 1992]




      ...
Scalability Analysis of Crossover & Mutation:
           Deterministic Fitness Functions

 Selectorecombinative GA
   Popu...
Crossover vs. Mutation:
         Deterministic Fitness Functions

Speed-Up: Scalability ratio of mutation to that of cross...
Convergence Time for Crossover:
             Noisy Fitness Functions

Additive Gaussian noise with variance σ2N

Set propo...
Population Sizing for Crossover:
             Noisy Fitness Functions

Scaling dominated:




Noise dominated:
Scalability Analysis of Mutation:
                      Noisy Fitness Functions

 Fitness should be sampled to average out...
Scalability Analysis of Crossover & Mutation:
               Noisy Fitness Functions

 Selectorecombinative GA




 Select...
Crossover vs. Mutation: Noisy BinInt
Speed-Up: Scalability ratio of crossover to that of mutation
Summary

Deterministic fitness:       Noisy fitness: Recombination
Mutation is more efficient   is more efficient in noise...
Conclusions

Good neighborhood information is essential
  Quadratic scalability of crossover and mutation
  Exponential sc...
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Let's get ready to rumble redux: Crossover versus mutation head to head on exponentially scaled problems

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This paper analyzes the relative advantages between crossover and mutation on a class of deterministic and stochastic additively separable problems with substructures of non-uniform salience. This study assumes that the recombination and mutation operators have the knowledge of the building blocks (BBs) and effectively exchange or search among competing BBs. Facetwise models of convergence time and population sizing have been used to determine the scalability of each algorithm. The analysis shows that for deterministic exponentially-scaled additively separable, problems, the BB-wise mutation is more efficient than crossover yielding a speedup of Θ(l logl), where l is the problem size. For the noisy exponentially-scaled problems, the outcome depends on whether scaling on noise is dominant. When scaling dominates, mutation is more efficient than crossover yielding a speedup of Θ(l logl). On the other hand, when noise dominates, crossover is more efficient than mutation yielding a speedup of Θ(l).

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Let's get ready to rumble redux: Crossover versus mutation head to head on exponentially scaled problems

  1. 1. Let’s Get Ready to Rumble Redux: Crossover vs. Mutation Head to Head on Exponentially-Scaled Problems Kumara Sastry1,2 and David E. Goldberg1 1Illinois Genetic Algorithms Laboratory 2Materials Computation Center University of Illinois at Urbana-Champaign, Urbana IL 61801 http://www.illigal.uiuc.edu ksastry@uiuc.edu, deg@uiuc.edu Supported by AFOSR FA9550-06-1-0096 and NSF DMR 03-25939.
  2. 2. Motivation Great debate between crossover and mutation When mutation works, it’s lightning quick When crossover works, it tackles more complex problems Compare crossover and mutation where both operators have access to same neighborhood information Local search literature Emphasis on good neighborhood operators [Barnes et al, 2003; Watson, 2003; Hansen et al, 2001] Need for automatic induction of neighborhoods Leads to adaptive time continuation operator [Lima et al 2005, 2006, 2007]
  3. 3. Outline Related work Assumption of known or discovered linkage Objective Algorithm Description Scalability analysis: Crossover vs. Mutation Known or discovered linkage Exponentially scaled additively-separable problem with and without Gaussian noise Summary and Conclusions
  4. 4. Background Emprical studies comparing crossover and mutation Scalability of GAs and mutation-based hillclimber [Mühlenbein, 1991 & 1992; Mitchell, Holland, and Forrest, 1994; Baum, Boneh, and Garett, 2001; Dorste, 2002; Garnier, 1999; Jansen and Wegener, 2002, 2005] Single GA run with large population vs. multiple GA runs with small population at fixed computational cost [Goldberg, 1999; Srivastava & Goldberg, 2001; Srivastava, 2002; Cantú-Paz & Goldberg, 2003; Luke, 2001; Fuchs, 1999] Used fixed operators that don’t adapt linkage Did not consider problems of bounded difficulty Linkage and neighborhood information is critical
  5. 5. Known or Discovered Linkage Assumption of known or induced linkage Can use linkage-learning techniques Linkage information is critical for selectorecombinative GA success Exponential Polynomial Scalability Pelikan, Ph.D. Thesis, 2002 Provide the same information for mutation Mutation searches in the building-block subspace
  6. 6. Algorithm Description Selectorecombinative genetic algorithm Population of size n Binary tournament selection Uniform building-block-wise crossover BBs #1 and #3 exchanged Exchange BBs with probability 0.5 Selectomutative genetic algorithm Start with a random individual Enumerative BB-wise mutation Consider BB partitions – Arbitrary left-to-right order Choose the best schemata – Among the 2k possible ones
  7. 7. Crossover Versus Mutation: Uniform Scaling Deterministic fitness: Noisy fitness: Recombination Mutation is more efficient is more efficient [Sastry & Goldberg, 2004]
  8. 8. Objective Crossover and mutation both have access to same neighborhood information Known or discovered linkage Recombination exchanges building blocks Mutation searches for the best BB in each partition Compare scalability of crossover and mutation Additively separable problems with exponentially-scaled BBs With and without additive Gaussian noise Where do they excel? Derive, verify, and use facetwise models Convergence time and population sizing
  9. 9. Scaling and Noise Cover Most Problems Adversarial problem design [Goldberg, 2002] Fluctuating R P Noise Deception Scaling Noisy BinInt
  10. 10. Convergence Time for Crossover: Deterministic Fitness Functions Selection-Intensity based model [Rudnick, 1992; Thierens et al, 1998] Derived for the BinInt problem Applicable to additively-separable problems Selection Intensity Problem size (m·k )
  11. 11. Population Sizing for Crossover: Deterministic Fitness Functions Domino convergence [Rudnick, 1992] Proportion Most Least salient salient BB convergence in order of salience ... Drift bound dictates population sizing Drift time [Goldberg and Segrest, 1987] time Size the population such that: Population size:
  12. 12. Scalability Analysis of Crossover & Mutation: Deterministic Fitness Functions Selectorecombinative GA Population size: Convergence time: Number of function evaluations: Selectomutative GA Initial solution is evaluated once 2k –1 evaluations in each of m partitions
  13. 13. Crossover vs. Mutation: Deterministic Fitness Functions Speed-Up: Scalability ratio of mutation to that of crossover
  14. 14. Convergence Time for Crossover: Noisy Fitness Functions Additive Gaussian noise with variance σ2N Set proportional to maximum fitness variance Scaling dominated: Noise dominated:
  15. 15. Population Sizing for Crossover: Noisy Fitness Functions Scaling dominated: Noise dominated:
  16. 16. Scalability Analysis of Mutation: Noisy Fitness Functions Fitness should be sampled to average out noise What should the sample size, ns, be? BB-wise decision making [Goldberg, Deb, & Clark, 1992] Square of the ordinate of a one-sided Gaussian deviate with specified error probability, α
  17. 17. Scalability Analysis of Crossover & Mutation: Noisy Fitness Functions Selectorecombinative GA Selectomutative GA Fitness of each individual is sampled ns times 2k –1 evaluations in each of m partitions
  18. 18. Crossover vs. Mutation: Noisy BinInt Speed-Up: Scalability ratio of crossover to that of mutation
  19. 19. Summary Deterministic fitness: Noisy fitness: Recombination Mutation is more efficient is more efficient in noise dominated regime
  20. 20. Conclusions Good neighborhood information is essential Quadratic scalability of crossover and mutation Exponential scalability of simple crossover [Thierens & Goldberg, 1994] ekmk scalability of simple mutation [Mühlenbein, 1991] Leads to a theory of time continuation Key facet of efficiency enhancement Leads to principled design and development of adaptive time continuation operators Promise of yielding supermultiplicative speedups
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