Exploring Grammatical Modification with Modules in Grammatical Evolution

Swafford, John Mark; O’Neill, Michael; Nicolau, Miguel; Brabazon, Anthony

doi:10.1007/978-3-642-20407-4_27

Cited by 13 publications

(11 citation statements)

References 12 publications

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“…Modifying the grammar during an evolutionary run can potentially be highly destructive to the population's fitness, even if beneficial information is used for the modification [22]. This is due to the genotype-to-phenotype mapping process GE employs.…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, the module identification approach is based on that adopted by Swafford et al [22]. The first step in identifying a module is choosing a parent individual to provide a candidate module.…”

Section: Module Identificationmentioning

confidence: 99%

“…Swafford et al [22] show how this approach to modifying the grammar in GE has the potential to be extremely destructive to the fitness of the entire population. In an attempt to remedy this, a "genotype repair" was also implemented.…”

Section: Grammar Enhancement By Modulesmentioning

confidence: 99%

“…By examining the derivation trees created by this mapping process, "good" information discovered by GE can be identified, encapsulated, and placed directly into the grammar to be used in the appropriate context. Previous research by Swafford et al [22] has shown the potential advantages and drawbacks of this approach to modularity in GE. Here, an improved method for enhancing GE's grammar with modules is proposed in order to alleviate such drawbacks.…”

Section: Introductionmentioning

confidence: 99%

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A non-destructive grammar modification approach to modularity in grammatical evolution

Swafford

Hemberg

O’Neill

et al. 2011

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

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Modularity has proven to be an important aspect of evolutionary computation. This work is concerned with discovering and using modules in one form of grammar-based genetic programming, grammatical evolution (GE). Previous work has shown that simply adding modules to GE's grammar has the potential to disrupt fit individuals developed by evolution up to that point. This paper presents a solution to prevent the disturbance in fitness that can come with modifying GE's grammar with previously discovered modules. The results show an increase in performance from a previously examined grammar modification approach and also an increase in performance when compared to standard GE.

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Section: Resultsmentioning

confidence: 99%

Section: Module Identificationmentioning

confidence: 99%

Section: Grammar Enhancement By Modulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A non-destructive grammar modification approach to modularity in grammatical evolution

Swafford

Hemberg

O’Neill

et al. 2011

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

Self Cite

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“…Building blocks (Swafford et al 2011) are of great concern in the analysis of genetic programming. To guarantee the convergence of solutions, many approaches choose to prevent damage to building blocks of the chromosome as far as possible.…”

Section: Explanationmentioning

confidence: 99%

Model approach to grammatical evolution: theory and case study

Deng

Wang

et al. 2015

Soft Comput

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Many deficiencies with grammatical evolution (GE) such as inconvenience in solution derivations, modularity analysis, and semantic computing can partly be explained from the angle of genotypic representations. In this paper, we deepen some of our previous work in visualizing concept relationships, individual structures and total evolutionary process, contributing new ideas, perspectives, and methods in these aspects; reveal the principle hidden in early work so that to develop a practical methodology; provide formal proofs for issues of concern which will be helpful for understanding of mathematical essence of issues, establishing of Communicated by V. Loia. B Pei He an unified formal framework as well as practical implementation; exploit genotypic modularity like modular discovery systematically which for the lack of supporting mechanism, if not impossible, is done poorly in many existing systems, and finally demonstrate the possible gains through semantic analysis and modular reuse. As shown in this work, the search space and the number of nodes in the parser tree are reduced using concepts from building blocks, and concepts such as the codon-to-grammar mapping and the integer modulo arithmetic used in most existing GE can be abnegated.

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Exploiting abstractions for grammar‐based learning of complex multi‐agent behaviours

Samarasinghe

Barlow

Lakshika

et al. 2021

Int J of Intelligent Sys

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This paper presents a grammar‐based evolutionary approach that incorporates abstractions to learn complex collective behaviours through their simpler representations. We propose modifications to the grammar syntax design and genome structure to facilitate evolution of abstractions in separate genome partitions. Two abstraction techniques based on behavioural decomposition and environmental scaffolding are presented to derive these simpler representations. Parallel and incremental learning architectures incorporated with grammatical evolution (GE) are investigated with three complex problems to evaluate their potential in generating collective multi‐agent behaviours. The results infer that both learning architectures surpass a generic GE model in performance for evolving complex behaviours. Furthermore, using environmental scaffolding reduces the robustness of the model than when only the behavioural decomposition technique is used. However, it has more potential to generate solutions with better fitness than when scaffoldings are not used. The evaluations suggest that, by incorporating abstraction learning architectures with grammar‐based evolution can significantly improve the performance of an agent system in complex problem domains.

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Exploring Grammatical Modification with Modules in Grammatical Evolution

Cited by 13 publications

References 12 publications

A non-destructive grammar modification approach to modularity in grammatical evolution

A non-destructive grammar modification approach to modularity in grammatical evolution

Model approach to grammatical evolution: theory and case study

Exploiting abstractions for grammar‐based learning of complex multi‐agent behaviours

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