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BackgroundGenetic studies are increasingly based on short noisy next generation scanners. Typically complete DNA sequences are assembled by matching short NextGen sequences against reference genomes. Despite considerable algorithmic gains since the turn of the millennium, matching both single ended and paired end strings to a reference remains computationally demanding. Further tailoring Bioinformatics tools to each new task or scanner remains highly skilled and labour intensive. With this in mind, we recently demonstrated a genetic programming based automated technique which generated a version of the state-of-the-art alignment tool Bowtie2 which was considerably faster on short sequences produced by a scanner at the Broad Institute and released as part of The Thousand Genome Project.ResultsBowtie2 GP and the original Bowtie2 release were compared on bioplanet’s GCAT synthetic benchmarks. Bowtie2 GP enhancements were also applied to the latest Bowtie2 release (2.2.3, 29 May 2014) and retained both the GP and the manually introduced improvements.ConclusionsOn both singled ended and paired-end synthetic next generation DNA sequence GCAT benchmarks Bowtie2GP runs up to 45% faster than Bowtie2. The lost in accuracy can be as little as 0.2–0.5% but up to 2.5% for longer sequences.

We show that the genetic improvement of programs (GIP) can scale by evolving increased performance in a widelyused and highly complex 50 000 line system. Genetic improvement of software for multiple objective exploration (GISMOE) found code that is 70 times faster (on average) and yet is at least as good functionally. Indeed, it even gives a small semantic gain.Index Terms-Automatic software reengineering, Bowtie2 GP , genetic programming (GP), multiple objective exploration, search based software engineering (SBSE).

We review the main results obtained in the theory of schemata in genetic programming (GP), emphasizing their strengths and weaknesses. Then we propose a new, simpler definition of the concept of schema for GP, which is closer to the original concept of schema in genetic algorithms (GAs). Along with a new form of crossover, one-point crossover, and point mutation, this concept of schema has been used to derive an improved schema theorem for GP that describes the propagation of schemata from one generation to the next. We discuss this result and show that our schema theorem is the natural counterpart for GP of the schema theorem for GAs, to which it asymptotically converges.

Genetic improvement uses automated search to find improved versions of existing software. We present a comprehensive survey of this nascent field of research with a focus on the core papers in the area published between 1995 and 2015. We identified core publications including empirical studies, 96% of which use evolutionary algorithms (genetic programming in particular). Although we can trace the foundations of genetic improvement back to the origins of computer science itself, our analysis reveals a significant upsurge in activity since 2012. Genetic improvement has resulted in dramatic performance improvements for a diverse set of properties such as execution time, energy and memory consumption, as well as results for fixing and extending existing system functionality. Moreover, we present examples of research work that lies on the boundary between genetic improvement and other areas, such as program transformation, approximate computing, and software repair, with the intention of encouraging further exchange of ideas between researchers in these fields.Index Terms-genetic improvement, survey 1089-778X (c)

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Genetic Improvement (GI) is a form of Genetic Programming that improves an existing program. We use GI to evolve a faster version of a C++ program, a Boolean satisfiability (SAT) solver called MiniSAT, specialising it for a particular problem class, namely Combinatorial Interaction Testing (CIT), using automated code transplantation. Our GI-evolved solver achieves overall 17% improvement, making it comparable with average expert human performance. Additionally, this automatically evolved solver is faster than any of the human-improved solvers for the CIT problem.

It is said ninety percent of faults that survive manufacturer's testing procedures are complex. That is, the corresponding bug fix contains multiple changes. Higher order mutation testing is used to study defect interactions and their impact on software testing for fault finding. We adopt a multi-objective Pareto optimal approach using Monte Carlo sampling, genetic algorithms and genetic programming to search for higher order mutants which are both hard-to-kill and realistic. The space of complex faults (higher order mutants) is much larger than that of traditional first order mutations which correspond to simple faults, nevertheless search based approaches make this scalable. The problems of non-determinism and efficiency are overcome. Easy to detect faults may become harder to detect when they interact and impossible to detect single faults may be brought to light when code contains two such faults. We use strong typing and BNF grammars in search based mutation testing to find examples of both in ancient heavily optimised every day C code.