Paper Info
Title
Improving Convergence In Cartesian Genetic Programming Using Adaptive Crossover, Mutation And Selection
Abstract
Genetic programming (GP) can be defined as an evolutionary algorithm-based methodology which opens the automatic derivation of programs for problem solving. GP as popularized by Koza uses tree representation. The application of GP takes place on several types of complex problems and became very important for Symbolic Regression. Miller and Thomson introduced a new directed graph representation called Cartesian Genetic Programming (CGP). We use this representation for very complex problems. CGP enables a new application on classification and image processing problems. Previous research showed that CGP has a low convergence rate on complex problems. Like in other approaches of evolutionary computation, premature convergence is also a common issue. Modern GP systems produce population statistics in every iteration. In this paper we introduce a new adaptive strategy which uses population statistics to improve the convergence of CGP. A new metric for CGP is introduced to classify the healthy population diversity. Our strategy maintains population diversity by adapting the probabilities of the genetic operators and selection pressure. We demonstrate our strategy on several regression problems and compare it to the traditional algorithm of CGP. We conclude this paper by giving advice about parameterization of the adaptive strategy.
Year
DOI
Venue
2015
10.1109/SSCI.2015.201
2015 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI)
Keywords
Field
DocType
convergence,statistics,sociology,genetic programming,probability,mathematical model
Convergence (routing),Crossover,Premature convergence,Computer science,Evolutionary computation,Directed graph,Genetic programming,Artificial intelligence,Rate of convergence,Symbolic regression
Conference
Citations 
PageRank 
References 
1
0.35
6
Authors
3
Name
Order
Citations
PageRank
roman kalkreuth110.35
Günter Rudolph221948.59
jorg krone310.35