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Introducing a Perl Genetic Programming System - and Can Meta-evolution Solve the Bloat Problem? [chapter]

Robert M. MacCallum
2003 Lecture Notes in Computer Science  
The supplied algorithm is strongly typed tree-based GP with homologous crossover.  ...  The effect of per-node, fixed and self-adapting crossover and mutation rates on code growth and fitness is studied.  ...  Macromutation is a little more complex.  ... 
doi:10.1007/3-540-36599-0_34 fatcat:dgmcunkgvfcelonan2fmlcsfra

A Rigorous Evaluation of Crossover and Mutation in Genetic Programming [chapter]

David R. White, Simon Poulding
2009 Lecture Notes in Computer Science  
We find that crossover does not significantly outperform mutation on most of the problems examined.  ...  The role of crossover and mutation in Genetic Programming (GP) has been the subject of much debate since the emergence of the field.  ...  Angeline [3] compared crossover to macromutation, arguing that the crossover operator may in fact function as a mutation operator of sorts.  ... 
doi:10.1007/978-3-642-01181-8_19 fatcat:dhpfuf4xafe7rjxmnpukgazhhu

Multi-task code reuse in genetic programming

Wojciech Jaskowski, Krzysztof Krawiec, Bartosz Wieloch
2008 Proceedings of the 2008 GECCO conference companion on Genetic and evolutionary computation - GECCO '08  
The technical means of code reuse is a crossbreeding operator which works very similar to standard tree-swapping crossover.  ...  The population contains 500 individuals and evolves for 200 generations using crossover with probability 0.9 and mutation (substituting subtree with a new random one) with probability 0.1.  ...  From the internal perspective of an evolutionary run that solves A, crossbreeding is a mutation (macromutation) operator, as it replaces a subtree in an individual in A by another subtree; the only difference  ... 
doi:10.1145/1388969.1389040 dblp:conf/gecco/JaskowskiKW08a fatcat:ickxeofvkzgrxp2xj7vwodkkfq

Instruction-Matrix-Based Genetic Programming

Gang Li, Jin Feng Wang, Kin Hong Lee, Kwong-Sak Leung
2008 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
The testing errors are also comparable to or better than those obtained with well-known classifiers.  ...  IMGP maintains an IM to evolve tree nodes and subtrees separately. IMGP extracts program trees from an IM and updates the IM with the information of the extracted program trees.  ...  The crossover is similar to context-preserving crossover [10] because the two subtrees of the parents must be in the same position to reduce the macromutation effect of the standard crossover [1] .  ... 
doi:10.1109/tsmcb.2008.922054 pmid:18632395 fatcat:refyzh5nx5efzgsms3abbrjmmi

Opposites Attract: Complementary Phenotype Selection for Crossover in Genetic Programming [chapter]

Brad Dolin, M.G. Arenas, J.J. Merelo
2002 Lecture Notes in Computer Science  
His conclusion is that crossover plays the functional role of "macromutation" rather than "building block engine," as in GAs.  ...  Standard crossover in genetic programming (GP) selects two parents independently, based on fitness, and swaps randomly chosen portions of genetic material (subtrees).  ...  Following [12] , in each parent we randomly select an internal node with 90% probability or a leaf node with 10% probability, and swap the subtrees rooted in these nodes.  ... 
doi:10.1007/3-540-45712-7_14 fatcat:uq67lggnbndlbo7xanb5male3q

Bloat Control Operators and Diversity in Genetic Programming: A Comparative Study

E. Alfaro-Cid, J. J. Merelo, F. Fernández de Vega, A. I. Esparcia-Alcázar, K. Sharman
2010 Evolutionary Computation  
This comparison study can provide the practitioner with some relevant clues as to which bloat control method is better suited to a particular problem and whether the advantage of a method does or does  ...  The aim of the work was to demonstrate that macromutation could perform as well as subtree crossover under identical experimental conditions.  ...  The hitchhiking theory is proposed by Tackett (1994) , who proved that random selection in conjunction with standard subtree crossover (i.e., when fitness is completely ignored) does not cause code growth  ... 
doi:10.1162/evco.2010.18.2.18206 pmid:20210598 fatcat:27m6pnrkdbgabmqwdkvza3bncu

Evolving controllers for simulated car racing using object oriented genetic programming

Alexandros Agapitos, Julian Togelius, Simon Mark Lucas
2007 Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07  
Several different controller representations are compared on a non-trivial problem in simulated car racing, with respect to learning speed and final fitness.  ...  These are Subtree macro-mutation (MM -substituting a node in the tree with an entirely randomly generated subtree of the same return type, under depth or size contraints), and homologous Uniform Crossover  ...  In this paper we are therefore comparing this representation with larger feed-forward neural networks, recurrent networks, and several types of GP.  ... 
doi:10.1145/1276958.1277271 dblp:conf/gecco/AgapitosTL07 fatcat:eku4lglf5fh4tpldvmtmhskgmu

Multiobjective Evolutionary Search of Difference Equations-based Models for Understanding Chaotic Systems [chapter]

Luciano Sánchez, José R. Villar
2008 Foundations of Generic Optimization  
Unless we include in the objective function some terms that depend on the properties on the reconstructed attractor, we may end up with a non chaotic model.  ...  In previous works [42] , we have proposed to implement the macromutation in the SA algorithm by means of a subtree crossover with a randomly generated individual [21, 37] .  ...  Each node of this tree will encode the name of the production rule that originated each subtree. This information will be used later to define a typed crossover.  ... 
doi:10.1007/978-1-4020-6668-9_4 fatcat:lragipoprbgqjbjafvgropq6uu

Fitness distributions in evolutionary computation: motivation and examples in the continuous domain

Kumar Chellapilla, David B. Fogel
1999 Biosystems (Amsterdam. Print)  
Subtree Crossover: Building Block En- Forms of Crossover in Evolutionary Computation on Lin- gine or Macromutation? Genetic Programming 1997. Pro- ear Systems of Equations.  ...  A preliminary investigation into evolving sions derived from comparing different variation modular programs without subtree crossover.  ... 
doi:10.1016/s0303-2647(99)00057-x pmid:10658834 fatcat:a5yxosxan5hrdebvyaedjlpwky

Using Symbolic Regression to Infer Strategies from Experimental Data [chapter]

John Duffy, Jim Engle-Warnick
2002 Studies in Fuzziness and Soft Computing  
It then cuts the two subtrees at these nodes, swaps them and recombines the subtrees with the parent trees.  ...  itself serves as a kind of macromutation.  ... 
doi:10.1007/978-3-7908-1784-3_4 fatcat:njchsy3clvecviuzdy5qurkscu

Neutral Variations Cause Bloat in Linear GP [chapter]

Markus Brameier, Wolfgang Banzhaf
2003 Lecture Notes in Computer Science  
A mutation-based variant of linear GP is applied that operates with minimum structural step sizes.  ...  The potential destruction caused by removing a subtree depends on the subtree size. The effect of the replacing subtree on the fitness, instead, is independent from its size.  ...  Contrary to this, the performance never drops in the nononeff experiment (compared to the baseline result).  ... 
doi:10.1007/3-540-36599-0_26 fatcat:jki7h4jlzzf2tfecej7x3txlia

Using genetic programming for the induction of novice procedural programming solution algorithms

Nelishia Pillay
2002 Proceedings of the 2002 ACM symposium on Applied computing - SAC '02  
However, the large-scale use of intelligent programming tutors has been impeded by the high developmental costs associated with building intelligent programming tutors.  ...  The crossover operator implemented by Langdon [LANG94] selects a subtree in one individual and replaces it with a randomly selected subtree from another individual.  ...  [BANZ98] and Angeline [ANGE97] describe the macromutation operator, also known as "headless chicken crossover", as a possible improvement over the standard crossover operator.  ... 
doi:10.1145/508895.508903 fatcat:4djzv5q6brhexojtygzxvaoeri

The molecule evoluator

Eric-Wubbo Lameijer, Ad IJzerman, Joost Kok
2005 Proceedings of the 2005 conference on Genetic and evolutionary computation - GECCO '05  
Additionally, we use interaction with the user as a fitness function, which is new in evolutionary algorithms in drug design.  ...  CROSSOVER AND MUTATION Crossover is implemented like crossover in standard genetic programming [2] : subtrees of two different molecules are selected and swapped.  ...  crossover.  ... 
doi:10.1145/1068009.1068339 dblp:conf/gecco/LameijerIK05 fatcat:gk2q7g6azzc2vpwbhx3rkdvk2y

A review on the application of evolutionary computation to information retrieval

O. Cordón, E. Herrera-Viedma, C. López-Pujalte, M. Luque, C. Zarco
2003 International Journal of Approximate Reasoning  
It is composed of a roulette-wheel selection, an usual GP crossover and a mutation operator based on swapping subtrees.  ...  approach and with a dimensionality and updating speed comparable to those of the existing indexing techniques.  ... 
doi:10.1016/j.ijar.2003.07.010 fatcat:iyccqeqltnbbzcul5aggjbnehm

A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems

O. Cordón, F. Moya, C. Zarco
2002 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
The performance of the proposed technique will be compared with the only previous existing approach to perform this task, Kraft et al.'  ...  Genetic operators: The usual GP crossover is considered [21] , which is based on randomly selecting one edge in each parent and exchanging both subtrees from these edges between the both parents.  ...  Apart from adapting the crossover and mutation operators to deal with the specific coding scheme considered, the remaining algorithm components remain the same.  ... 
doi:10.1007/s00500-002-0184-8 fatcat:fc3l2hxqajbanhbjyuo3qdosni
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