Flight of the FINCH Through the Java Wilderness

Orlov, Michael; Sipper, Moshe

doi:10.1109/tevc.2010.2052622

Cited by 80 publications

(80 citation statements)

References 14 publications

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“…Due to space limitations we only provide a brief description of our results, with the full account available in (Orlov and Sipper, 2009;Orlov and Sipper, 2010). To date, we have successfully tackled several problems:…”

Section: A Summary Of Resultsmentioning

confidence: 99%

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Finch: A System for Evolving Java (Bytecode)

Orlov

Sipper

2010

Genetic Programming Theory and Practice VIII

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The established approach in genetic programming (GP) involves the definition of functions and terminals appropriate to the problem at hand, after which evolution of expressions using these definitions takes place. We have recently developed a system, dubbed FINCH (Fertile Darwinian Bytecode Harvester), to evolutionarily improve actual, extant software, which was not intentionally written for the purpose of serving as a GP representation in particular, nor for evolution in general. This is in contrast to existing work that uses restricted subsets of the Java bytecode instruction set as a representation language for individuals in genetic programming. The ability to evolve Java programs will hopefully lead to a valuable new tool in the software engineer's toolkit.

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Section: A Summary Of Resultsmentioning

confidence: 99%

“…The following chapter provides an overview of our system, ending with a précis of results. Additional information can be found in (Orlov and Sipper, 2009;Orlov and Sipper, 2010).…”

Section: Introductionmentioning

confidence: 99%

Finch: A System for Evolving Java (Bytecode)

Orlov

Sipper

2010

Genetic Programming Theory and Practice VIII

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“…However Orlov and Sipper [27] also show GP can improve Java byte code produced by the Java compiler from existing human written programs. Another approach might be to "seed" the initial population with a non-random starting point [22].…”

Section: Genetic Programming To Improve Human Written Programsmentioning

confidence: 99%

Genetic Improvement of Programs

Langdon

2014

2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing

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Abstract:Evolutionary computing, particularly genetic programming, can optimise software and software engineering, including evolving test benchmarks, search meta-heuristics, protocols, composing web services, improving hashing and garbage collection, redundant programming and even automatically fixing bugs. Often there are many potential ways to balance functionality with resource consumption. But a human programmer cannot try them all. Also the optimal trade off may be different on each hardware platform and it could vary over time or as usage changes. It may be genetic programming can automatically suggest different trade offs for each new market.

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“…This grow and graft development approach aims to reduce the amount of tedious effort required by human programmer in order to develop and add new functionality into an existing system. Our approach is inspired by the recent trend in Search Based Software Engineering (SBSE) called 'genetic improvement' [2,8,10,11,14,15]. Genetic Improvement (GI) uses existing code as 'genetic material' that helps to automatically improve existing software systems.…”

Section: Introduction and Backgroudmentioning

confidence: 99%

“…Genetic Improvement (GI) uses existing code as 'genetic material' that helps to automatically improve existing software systems. It has been used to repair broken functionality [10,14], and to achieve dramatic scale-ups for sets of small benchmarks [11,15], and also for a 50k LoC genome matching system [8], for graphic shaders [14] and for a CUDA stereo image processing system [7]. Related work on loop perforation has also reported dramatic speed-ups [13].…”

Section: Introduction and Backgroudmentioning

confidence: 99%

Babel Pidgin: SBSE Can Grow and Graft Entirely New Functionality into a Real World System

Harman

Jia

Langdon

2014

Search-Based Software Engineering

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Abstract.Adding new functionality to an existing, large, and perhaps poorly-understood system is a challenge, even for the most competent human programmer. We introduce a 'grow and graft' approach to Genetic Improvement (GI) that transplants new functionality into an existing system. We report on the trade offs between varying degrees of human guidance to the GI transplantation process. Using our approach, we successfully grew and transplanted a new 'Babel Fish' linguistic translation feature into the Pidgin instant messaging system, creating a genetically improved system we call 'Babel Pidgin'. This is the first time that SBSE has been used to evolve and transplant entirely novel functionality into an existing system. Our results indicate that our grow and graft approach requires surprisingly little human guidance.

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Flight of the FINCH Through the Java Wilderness

Cited by 80 publications

References 14 publications

Finch: A System for Evolving Java (Bytecode)

Finch: A System for Evolving Java (Bytecode)

Genetic Improvement of Programs

Babel Pidgin: SBSE Can Grow and Graft Entirely New Functionality into a Real World System

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