Conglomeration of Reptile Search Algorithm and Differential Evolution Algorithm for Optimal Designing of FIR Filter

Chauhan, Sumika; Vashishtha, Govind; Kumar, Anil; Abualigah, Laith

doi:10.1007/s00034-022-02255-5

Cited by 11 publications

(1 citation statement)

References 35 publications

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“…[ 20 ] According to the comprehensive survey on program synthesis with evolutionary algorithms conducted by Sobania et al, the most frequently used approaches for code generation involve stack-based GP, using mostly Push as a representation language, grammar-guided GP (including tree-based and linearized grammar-based approaches), and linear GP [ 21 ] Although the stack-based GP approach makes up the largest proportion of the identified studies (37 in-scope papers), due to its most common language representation (Forth, Push, or Postscript programming language), it is not considered relevant in real-world software projects, especially from the perspective of software development [ 21 ]. Other meta-heuristic algorithms introduced in specific fields of automated programming, especially regarding optimization tasks, may include the particle swarm optimizer, gravitational search algorithm, artificial bee colony algorithm, grey wolf optimizer [ 22 ], or differential evolution [ 23 ] and slime mould algorithm [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Automated code development based on genetic programming in graphical programming language: A pilot study

Kodytek,

Bodzas,

Zidek

2024

PLoS ONE

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Continual technological advances associated with the recent automation revolution have tremendously increased the impact of computer technology in the industry. Software development and testing are time-consuming processes, and the current market faces a lack of specialized experts. Introducing automation to this field could, therefore, improve software engineers’ common workflow and decrease the time to market. Even though many code-generating algorithms have been proposed in textual-based programming languages, to the best of the authors’ knowledge, none of the studies deals with the implementation of such algorithms in graphical programming environments, especially LabVIEW. Due to this fact, the main goal of this study is to conduct a proof-of-concept for a requirement-based automated code-developing system within the graphical programming environment LabVIEW. The proposed framework was evaluated on four basic benchmark problems, encompassing a string model, a numeric model, a boolean model and a mixed-type problem model, which covers fundamental programming scenarios. In all tested cases, the algorithm demonstrated an ability to create satisfying functional and errorless solutions that met all user-defined requirements. Even though the generated programs were burdened with redundant objects and were much more complex compared to programmer-developed codes, this fact has no effect on the code’s execution speed or accuracy. Based on the achieved results, we can conclude that this pilot study not only proved the feasibility and viability of the proposed concept, but also showed promising results in solving linear and binary programming tasks. Furthermore, the results revealed that with further research, this poorly explored field could become a powerful tool not only for application developers but also for non-programmers and low-skilled users.

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