This paper describes an approach to the maintenance problem of central processors which minimizes the human role. This approach consists of a combination of 1) a programming system which computes the diagnostic progra...
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Contemporary genetic programming (GP) systems for general program synthesis have been primarily concerned with evolving programs that can manipulate values from a standard set of primitive data types and simple indexe...
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ISBN:
(纸本)9798400701191
Contemporary genetic programming (GP) systems for general program synthesis have been primarily concerned with evolving programs that can manipulate values from a standard set of primitive data types and simple indexed data structures. In contrast, human programmers do not limit themselves to a small finite set of data types and use polymorphism to express an unbounded number of types including nested data structures, product types, and generic functions. Code-building Genetic programming (CBGP) is a recently introduced method that compiles type-safe programs from linear genomes using stack-based compilation and a formal type system. Although prior work with CBGP has shown initial demonstrations of polymorphism inside evolved programs, we have provided a deeper exploration of these capabilities through the evolution of programs which make use of generic data types such as key-value maps, tuples, and sets, as well as higher order functions and functions with polymorphic type signatures. In our experiments, CBGP is able to solve problems with all of these properties, where every other GP system that we know of has restrictions that make it unable to even consider problems with these properties. This demonstration provides a significant step towards fully aligning the expressiveness of GP to real world programming.
Genetic Algorithms and Genetic programming have been used extensively in Evolutionary robotics (ER) with the goal of automatic programming of robotic controllers and has shown to be a promising approach. In this paper...
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ISBN:
(纸本)9781424481262
Genetic Algorithms and Genetic programming have been used extensively in Evolutionary robotics (ER) with the goal of automatic programming of robotic controllers and has shown to be a promising approach. In this paper, we demonstrate the use of Gene Expression programming, GEP, a newly developed evolutionary algorithm akin to GA and GP, to evolve robotic behaviours. We use the already well known obstacle avoidance behaviour for our initial work. The behaviour can be regarded as emergent when the main aim is to develop a wandering/exploratory behaviour. From our investigations, we show that GEP is able to learn controllers for a number of different environments. Moreover, standard GEP has never been used before in evolving robotic behaviours, however due to its reported good performances in other fields, we feel it has the capability to be used in ER.
Program generation and transformation techniques have gained considerable attention in the context of domain-specific languages (DSLs) and model-driven architecture (MDA). In this paper we compare domain-specific prog...
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ISBN:
(纸本)3540278842
Program generation and transformation techniques have gained considerable attention in the context of domain-specific languages (DSLs) and model-driven architecture (MDA). In this paper we compare domain-specific program generators with general-purpose aspectoriented languages. We argue that program generation techniques have severe disadvantages with respect to composability, scalability, understandability, and other important software engineering issues. Finally, we advocate general-purpose aspect-oriented languages as an alternative for the implementation of domain-specific languages.
RNAfold predicts the secondary structure of RNA molecules from their base sequence. We apply a mixture of manual and automated genetic improvements to its C source. GI gives a 1.6% improvement to parallel SSE4.1 code....
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ISBN:
(纸本)9781450349390
RNAfold predicts the secondary structure of RNA molecules from their base sequence. We apply a mixture of manual and automated genetic improvements to its C source. GI gives a 1.6% improvement to parallel SSE4.1 code. The automatic programming evolutionary system has access to Intel library code and previous revisions. On 4 666 curated structures from RNA_STRAND, GGGP gives a combined speed up of 31.9%, with no loss of accuracy (GI code run 1.4 10(11) times).
Symbolic regression is the process of finding a mathematical formula that fits a specific set of data by searching in different mathematical expressions. This process requires great accuracy in order to reach the corr...
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ISBN:
(纸本)9781728172064
Symbolic regression is the process of finding a mathematical formula that fits a specific set of data by searching in different mathematical expressions. This process requires great accuracy in order to reach the correct formula. In this paper, we will present a new method for solving symbolic regression problems based on the firefly algorithm. This method is called Firefly programming (FP). The results of applying firefly programming algorithm to some symbolic regression benchmark problems will be compared to the results of Genetic programming (GP) and Artificial Bee Colony programming (ABCP) methods.
Feature Oriented programming (FOP) is an emerging paradigm for application synthesis, analysis, and optimization. A target application is specified declaratively as a set of features, like many consumer products (e.g....
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ISBN:
(纸本)0769521630
Feature Oriented programming (FOP) is an emerging paradigm for application synthesis, analysis, and optimization. A target application is specified declaratively as a set of features, like many consumer products (e.g., personal computers, automobiles). FOP technology translates such declarative specifications into efficient programs. AHEAD is a model of FOP that is based on step-wise refinement, which advocates that complex programs can be synthesized from simple programs by incrementally adding features. The AHEAD Tool Suite (ATS) supports program development in AHEAD. AHEAD and ATS are among the most advanced models/tools for large-scale program synthesis.
In recent years the field of genetic programming has made significant advances towards automatic programming. Research and development of contemporary program synthesis methods, such as PushGP and Grammar Guided Genet...
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ISBN:
(纸本)9781450371285
In recent years the field of genetic programming has made significant advances towards automatic programming. Research and development of contemporary program synthesis methods, such as PushGP and Grammar Guided Genetic programming, can produce programs that solve problems typically assigned in introductory academic settings. These problems focus on a narrow, predetermined set of simple data structures, basic control flow patterns, and primitive, non-overlapping data types (without, for example, inheritance or composite types). Few, if any, genetic programming methods for program synthesis have convincingly demonstrated the capability of synthesizing programs that use arbitrary data types, data structures, and specifications that are drawn from existing codebases. In this paper, we introduce Code Building Genetic programming (CBGP) as a framework within which this can be done, by leveraging programming language features such as reflection and first-class specifications. CBGP produces a computational graph that can be executed or translated into source code of a host language. To demonstrate the novel capabilities of CBGP, we present results on new benchmarks that use non-primitive, polymorphic data types as well as some standard program synthesis benchmarks.
Ubiquitous computing increases the pressure on the software industry to produce ever more and error-free code. Two recipes from automated programming are available to meet this challenge: On the one hand, generative p...
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ISBN:
(纸本)0769521312
Ubiquitous computing increases the pressure on the software industry to produce ever more and error-free code. Two recipes from automated programming are available to meet this challenge: On the one hand, generative programming raises the level of abstraction in software development by describing problems in high-level domain-specific languages and making them executable. On the other hand, in situations where one needs to produce a family of similar programs, product line engineering supports code reuse by composing programs from a set of common assets (or features). AHEAD (Algebraic Hierarchical Equations for Application Design) is a framework for generative programming and product line engineering that achieves additional productivity gains by scaling feature composition up. Our contribution is GRAFT, a calculus that gives a formal foundation to AHEAD and provides several mechanisms for making sure that feature combinations are legal and that features in themselves are consistent.
Differential Evolution (DE) is an evolutionary heuristic for continuous optimization problems. In DE, solutions are coded as vectors of floats that evolve by crossover with a combination of best and random individuals...
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ISBN:
(纸本)9783642204067
Differential Evolution (DE) is an evolutionary heuristic for continuous optimization problems. In DE, solutions are coded as vectors of floats that evolve by crossover with a combination of best and random individuals from the current generation. Experiments to apply DE to automatic programming were made recently by Veenhuis, coding full program trees as vectors of floats (Tree Based Differential Evolution or TreeDE). In this paper, we use DE to evolve linear sequences of imperative instructions, which we call Linear Differential Evolutionary programming (LDEP). Unlike TreeDE, our heuristic provides constant management for regression problems and lessens the tree-depth constraint on the architecture of solutions. Comparisons with TreeDE and GP show that LDEP is appropriate to automatic programming.
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