Novelty search for software improvement of a SLAM system

López-López, Víctor R.; Trujillo, Leonardo; Legrand, Pierrick

doi:10.1145/3205651.3208237

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…The most common method used is novelty search [8,16] in which unique solutions are saved for future reference. In the selection process, the GI system then performs a comparison with each previously saved solution and only uses those individuals in a new population which represent new areas of the search space not present in the stored list.…”

Section: Related Workmentioning

confidence: 99%

Using phylogenetic analysis to enhance genetic improvement

Rainford

Porter

2022

Proceedings of the Genetic and Evolutionary Computation Conference

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Genetic code improvement systems start from an existing piece of program code and search for alternative versions with better performance according to a metric of interest. The search space of source code is a large, rough fitness landscape which can be extremely difficult to navigate. Most approaches to enhancing search capability in this domain involve either novelty search, where low-fitness areas of the search space are remembered and avoided, or formal analysis which attempts to find high-utility parameterizations for the genetic improvement (GI) process. In this paper we propose the use of phylogenetic analysis over genetic history to understand how different mutations and crossovers affect the fitness of a population over time for a particular problem; we then use the results of that analysis to tune a GI process during its operation to enhance its ability to locate better program candidates. Using phylogenetic analysis on 600 runs of a genetic improver targeting a hash function, we demonstrate how the results of this analysis yield tuned mutation types over the course of a GI process (dynamically and continually set according to individual's ancestors' ranks within the population) to give hash functions with over 20% improved fitness compared to a baseline GI process.

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Section: Related Workmentioning

confidence: 99%

Using phylogenetic analysis to enhance genetic improvement

Rainford

Porter

2022

Proceedings of the Genetic and Evolutionary Computation Conference

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“…For our genetic programming [13,14,15] system we used GISMOE [5,16,17,18]. GISMOE ( Figure 5) creates a BNF grammar, which represents the original program's source code and legitimate changes to it.…”

Section: The Grammar Based Genetic Programming Systemmentioning

confidence: 99%

Evolving AVX512 Parallel C Code Using GP

Langdon¹,

Lorenz

2019

Lecture Notes in Computer Science

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“…As stated by Boussaa et al [54], that work is the first one in the literature to adopt NS to generate test data. L ópez-L ópez et al [55] used, for the first time, NS for software improvement by combining it with a genetic improvement system. Recently, Romero et al [53] have explored the use of NS for the next release problem [56].…”

Section: Introductionmentioning

confidence: 99%

Looking For Novelty in Search-Based Software Product Line Testing

Xiang

Huang

et al. 2022

IIEEE Trans. Software Eng.

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Testing software product lines (SPLs) is difficult due to a huge number of possible products to be tested. Recently, there has been a growing interest in similarity-based testing of SPLs, where similarity is used as a surrogate metric for the t-wise coverage. In this context, one of the primary goals is to sample, by using search-based algorithms, a subset of test cases (i.e., products) as dissimilar as possible, thus potentially making more t-wise combinations covered. Prior works have shown, by means of empirical studies, the great potential of current similarity-based testing approaches. However, the rationale of this testing technique deserves a more rigorous exploration. To this end, we perform a correlation analysis to investigate the internal relationship between similarity metrics and the t-wise coverage. We find that similarity metrics generally have a significantly positive correlation with the t-wise coverage. This well explains why similarity-based testing works, as the improvement on similarity metrics will potentially increase the t-wise coverage. Moreover, we explore, for the first time, the use of the novelty search (NS) algorithm for similarity-based SPL testing. The algorithm rewards "novel" individuals, i.e., those being different from individuals discovered previously, and this perfectly matches the goal of similarity-based SPL testing. We demonstrate that the novelty score in NS has a (much) stronger positive correlation with the t-wise coverage than the widely used fitness function employed in the genetic algorithm (GA). Empirical results on 31 software product lines, either realistic or artificial, validate the superiority of NS over GA, as well as other state-of-the-art approaches. Finally, we investigate how the performance of NS is affected by the ways of generating new products, and by its key parameters. In summary, looking for novelty provides an alternative way of generating test cases for SPL testing.

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Novelty search for software improvement of a SLAM system

Cited by 9 publications

References 22 publications

Using phylogenetic analysis to enhance genetic improvement

Using phylogenetic analysis to enhance genetic improvement

Evolving AVX512 Parallel C Code Using GP

Looking For Novelty in Search-Based Software Product Line Testing

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