Inductive bias and genetic programming

Whigham, Peter A.

doi:10.1049/cp:19951092

Cited by 70 publications

(61 citation statements)

References 2 publications

(1 reference statement)

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“…Whigham uses CFG's in GP 41,42) . As with Gruau's system and DCTG-GP, only nodes having the same nonterminal identifier are selectable for crossover exchange.…”

Section: Resultsmentioning

confidence: 99%

Logic-based genetic programming with definite clause translation grammars

Ross

2001

New Gener Comput

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DCTG-GP is a genetic programming system that uses definite clause translation grammars. A DCTG is a logical version of an attribute grammar that supports the definition of context-free languages, and it allows semantic information associated with a language to be easily accomodated by the grammar. This is useful in genetic programming for defining the interpreter of a target language, or incorporating both syntactic and semantic problem-specific contraints into the evolutionary search. The DCTG-GP system improves on other grammar-based GP systems by permitting non-trivial semantic aspects of the language to be defined with the grammar. It also automatically analyzes grammar rules in order to determine their minimal depth and termination characteristics, which are required when generating random program trees of varied shapes and sizes. An application using DCTG-GP is described. * 1

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“…Whigham uses CFG's in GP 41,42) . As with Gruau's system and DCTG-GP, only nodes having the same nonterminal identifier are selectable for crossover exchange.…”

Section: Resultsmentioning

confidence: 99%

Logic-based genetic programming with definite clause translation grammars

Ross

2001

New Gener Comput

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“…This should have the effect of less disruption to the individuals when the grammar is being modified and they are remapped. Modifying GE's grammar based on sub-trees which are considered valuable is similar to Whigham's [19] work, where he also identifies what are considered to be useful sections of parse trees and alters the grammar with additional productions containing the terminals and possibly non-terminals from these parse trees. However, his grammar-based form of GP does not employ the genotype-to-phenotype mapping used in GE.…”

Section: Methodsmentioning

confidence: 95%

Exploring Grammatical Modification with Modules in Grammatical Evolution

Swafford

O’Neill

Nicolau

et al. 2011

Lecture Notes in Computer Science

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There have been many approaches to modularity in the field of evolutionary computation, each tailored to function with a particular representation. This research examines one approach to modularity and grammar modification with a grammar-based approach to genetic programming, grammatical evolution (GE). Here, GE's grammar was modified over the course of an evolutionary run with modules in order to facilitate their appearance in the population. This is the first step in what will be a series of analysis on methods of modifying GE's grammar to enhance evolutionary performance. The results show that identifying modules and using them to modify GE's grammar can have a negative effect on search performance when done improperly. But, if undertaken thoughtfully, there are possible benefits to dynamically enhancing the grammar with modules identified during evolution.

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“…Whigham [103] built what would today be seen as a hybrid GP/EDA-GP system. It used a stochastic CFG, in which the success of individuals gradually biased the probabilities of productions, although the search still used mutation and crossover operators as well.…”

Section: Probabilistic Model-building Algorithmsmentioning

confidence: 99%

Grammar-based Genetic Programming: a survey

McKay

Hoài

Whigham

et al. 2010

Genet Program Evolvable Mach

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138

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Publication informationGenetic Programming and Evolvable Machines, Abstract Grammar formalisms are one of the key representation structures in Computer Science. So it is not surprising that they have also become important as a method for formalizing constraints in Genetic Programming (GP). Practical grammar-based GP systems first appeared in the mid 1990s, and have subsequently become an important strand in GP research and applications. We trace their subsequent rise, surveying the various grammar-based formalisms that have been used in GP and discussing the contributions they have made to the progress of GP. We illustrate these contributions with a range of applications of grammar-based GP, showing how grammar formalisms contributed to the solutions of these problems. We briefly discuss the likely future development of grammar-based GP systems, and conclude with a brief summary of the field.

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Inductive bias and genetic programming

Cited by 70 publications

References 2 publications

Logic-based genetic programming with definite clause translation grammars

Logic-based genetic programming with definite clause translation grammars

Exploring Grammatical Modification with Modules in Grammatical Evolution

Grammar-based Genetic Programming: a survey

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