MetaCartSign in to MyCiteSeer

Include Citations | Advanced Search | Help

Include Citations | Advanced Search | Help

  Biases introduced by adaptive recombination operators (1999) [4 citations — 1 self]

Download:
Download as a PDF | Download as a PS
by Kanta Vekaria, Chris Clack
In GECCO-99
http://www.cs.ucl.ac.uk/staff/C.Clack/papers/Published/fred/printgecco.ps
Add To MetaCart

Abstract:

The broad goal of adaptive techniques is to acquire knowledge dynamically about the search space and to use this knowledge to bias the evolutionary process. The effectiveness of any adaptive technique is therefore determined by the biases being used. In this paper we identify four key biases introduced by adaptive recombination operators and analyse the relationship between these biases. Three adaptive recombination operators are characterised in terms of four biases: directional, credit, initialisation and hitchhiker. We show that the biases introduced by adaptive recombination are not always beneficial to GA performance and we explore methods for minimising the detrimental effects. 1.

Citations

244 Messy Genetic Algorithms: Motivation, Analysis and First Results – Goldberg, Korb, et al. - 1989
148 Adapting operator probabilities in genetic algorithm – Davis - 1989
89 An adaptive crossover distribution mechanism for genetic algorithms – Schaffer, Morishima - 1987
78 Adaptation on rugged fitness landscapes – Kauffman - 1989
64 Crossover’s niche – Eshelman, Schaffer - 1993
60 Crossover or mutation – Spears - 1992
47 Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms – Harik - 1997
45 Recombination distributions for genetic algorithms – Booker - 1992
42 Adapting crossover in evolutionary algorithms – Spears - 1995
31 Designer Genetic Algorithms: Genetic Algorithms – Louis, Rawlins - 1991
23 On crossover as an evolutionary viable strategy – Schaffer, Eshelman - 1991
19 The Role of Mutation and Recombination in the Evolutionary Algorithms – Spears - 1998
15 Relative Building-Block Fitness and the Building Block Hypothesis – Forrest - 1993
10 Self Adaptation in Evolutionary Algorithms – Smith - 1998
7 Productive recombination and propagating and preserving schemata – Eshelman - 1995
2 Recombination Parameters. The Handbook of Evolutionary Computation – Spears - 1997