A simple two-module problem to exemplify building-block assembly under crossover
A simple two-module problem to exemplify building-block assembly under crossover
  Theoretically and empirically it is clear that a genetic algorithm with crossover will outperform a genetic algorithm without crossover in some fitness landscapes, and vice versa in other landscapes. Despite an extensive literature on the subject, and recent proofs of a principled distinction in the abilities of crossover and non-crossover algorithms for a particular theoretical landscape, building general intuitions about when and why crossover performs well when it does is a different matter. In particular, the proposal that crossover might enable the assembly of good building-blocks has been difficult to verify despite many attempts at idealized building-block landscapes. Here we show the first example of a two-module problem that shows a principled advantage for cross-over. This allows us to understand building-block assembly under crossover quite straightforwardly and build intuition about more general landscape classes favoring crossover or disfavoring it.
  
  161-171
  
  
    
      Watson, Richard A.
      
        ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
      
     
  
  
    
  
    
  
   
  
  
    
      2004
    
    
  
  
    
      Watson, Richard A.
      
        ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
      
     
  
    
  
    
  
       
    
 
  
    
      
  
  
  
  
    Watson, Richard A.
  
  
  
  
   
    (2004)
  
  
    
    A simple two-module problem to exemplify building-block assembly under crossover.
  
  
  
    
      Yao, X. and , et al. 
      (eds.)
    
  
  
   In Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. 
  vol. 3242, 
      Springer. 
          
          
        .
    
  
  
  
   (doi:10.1007/978-3-540-30217-9_17).
  
   
  
    
      Record type:
      Conference or Workshop Item
      (Paper)
      
      
    
   
    
    
      
        
          Abstract
          Theoretically and empirically it is clear that a genetic algorithm with crossover will outperform a genetic algorithm without crossover in some fitness landscapes, and vice versa in other landscapes. Despite an extensive literature on the subject, and recent proofs of a principled distinction in the abilities of crossover and non-crossover algorithms for a particular theoretical landscape, building general intuitions about when and why crossover performs well when it does is a different matter. In particular, the proposal that crossover might enable the assembly of good building-blocks has been difficult to verify despite many attempts at idealized building-block landscapes. Here we show the first example of a two-module problem that shows a principled advantage for cross-over. This allows us to understand building-block assembly under crossover quite straightforwardly and build intuition about more general landscape classes favoring crossover or disfavoring it.
         
      
      
        
          
            
  
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 watson_astmp_ppsn_2004.pdf
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      Published date: 2004
 
    
  
  
    
  
    
  
    
  
    
  
    
  
    
  
    
     
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        Agents, Interactions & Complexity
      
    
  
    
  
  
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        Local EPrints ID: 262005
        URI: http://eprints.soton.ac.uk/id/eprint/262005
        
          
        
        
        
        
          PURE UUID: d49189c6-5ece-4009-b407-477f7e2d1d2e
        
  
    
        
          
            
              
            
          
        
    
        
          
            
          
        
    
        
          
            
          
        
    
  
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  Date deposited: 21 Feb 2006
  Last modified: 16 Mar 2024 03:42
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      Contributors
      
          
          Author:
          
            
              
              
                Richard A. Watson
              
              
                
              
            
            
          
         
      
          
          Editor:
          
            
              
              
                X. Yao
              
              
            
            
          
        
      
          
          Editor:
          
            
              
              
                et al. 
              
              
            
            
          
        
      
      
      
    
  
   
  
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