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.
An algorithm of automatic designation of neural networks using gene expression programming (GEP) is presented The standard GEP is improved on so as to solve the problem of prematurity and slow convergence speed in opt...
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ISBN:
(纸本)9780769533049
An algorithm of automatic designation of neural networks using gene expression programming (GEP) is presented The standard GEP is improved on so as to solve the problem of prematurity and slow convergence speed in optimizing neural networks. In this paper, an application of designing neural networks for XOR problem is formulated and compared with others. The results demonstrated that the proposed GEP approach is an effective method for evolving neural networks, and the performance of improved GEP is much better than that Of standard GEP in that it not only has higher evolution efficiency, improving convergence rate from 45% to 81%, but has faster convergence speed with only 56% evolutionary number of standard GEP algorithm.
Code Building Genetic programming (CBGP) is a method for general inductive program synthesis that uses a genetic algorithm and a formal type system to evolve linear genomes that are compiled into type-safe programs in...
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ISBN:
(纸本)9798400704949
Code Building Genetic programming (CBGP) is a method for general inductive program synthesis that uses a genetic algorithm and a formal type system to evolve linear genomes that are compiled into type-safe programs in a host language. Prior work showed that CBGP can evolve programs that use arbitrary abstractions from existing codebases along with higher-order functions and polymorphism. In tests on benchmark problems, however, the problem solving capabilities of CBGP have been mixed. One hypothesized explanation for weak performance on some problems is that many functions encountered during the compilation process are typically not applied. Here we propose two modifications to the compilation algorithm, both of which make it more likely that functions will be applied when composing programs. The first modification changes how frequently CBGP attempts to perform function application, while the second allows the construction of function applications to backtrack. While both modifications increase solution rates on benchmark problems, the backtracking modification shows more promise with a modest increase in computational cost and no additional configuration requirements. We argue that this modification should be considered the new standard compilation algorithm for CBGP systems.
Assessing students' programming exercises has become a difficult activity that most educators encounter nowadays. The activity basically includes the tasks to construct questions and solution models in programming...
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This paper presents a model that simulates a self-assembly process for software components. Initial investigations on the Automated Self-Assembly programming Paradigm (ASAP2) is presented whereby software components a...
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As for the Siemens 840Dsl CNC system, this paper investigates the time required for manual programming and SHOPMILL automatic programming, as well as the machining efficiency of these two types of CNC programming base...
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Problems of symbolic regression aim to develop a function, described in symbolic form, that fits a given target fitcases. Artificial bee colony programing algorithm (ABCP) is one of the most feasible automatic program...
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For a computer to have the ability of automatic programming it is necessary to possess adequate knowledge. This knowledge is contained in the user's programmes written in the language of higher level. The aim of t...
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For a computer to have the ability of automatic programming it is necessary to possess adequate knowledge. This knowledge is contained in the user's programmes written in the language of higher level. The aim of the present paper is to determine acceptable user's programmes transformation into a symbolic form which constitutes an element of the knowledge basis. The knowledge basis of such a computer should be expressed in an adequate symbolic form, thus allowing maximum knowledge integration in the computer memory. To realise knowledge integration it is assumed that one and the very one element of knowledge should be represented once only, no matter in how many programmes and how many times it will appear. In the paper the concept of acquiring knowledge contained in statements and in operation code sequences has been formalised, as well as its representation in the knowledge basis. Organising and enlarging the knowledge basis in the range of statement part of the user's programme has been the author's main concern. Special attention has been paid to knowledge integration and the connection of elements in the knowledge basis.
A measurement-domain-specific language, which is based on a model-driven paradigm for measurement-test-procedure definition, instrument configurations, and task synchronization, is proposed. This formal language, whic...
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A measurement-domain-specific language, which is based on a model-driven paradigm for measurement-test-procedure definition, instrument configurations, and task synchronization, is proposed. This formal language, which is particular for a specific measurement field, aims at specifying complete, easy-to-understand, easy-to-reuse, and easy-to-maintain applications efficiently and quickly by means of a script. The script is checked and integrated into the existing software framework automatically by a specific parser-builder chain, in order to produce the measurement application. Constructs for abstracting key concepts of the domain allow the test engineer to write more concise and higher level programs by natural language-like sentences in a shorter time without being a skilled programmer. As an experimental case study, the proposed language has been applied to the flexible framework for magnetic measurements at the European Organization for Nuclear Research (CERN).
automatic Design of Algorithms through Evolution (ADATE) is a program synthesis system that creates recursive programs in a functional language with automatic invention of recursive help functions and self-adaptive op...
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automatic Design of Algorithms through Evolution (ADATE) is a program synthesis system that creates recursive programs in a functional language with automatic invention of recursive help functions and self-adaptive optimization of numerical values. We implement a neuron in a pulse coupled neural network (PCNN) as a recursive function in the ADATE language and then use ADATE to automatically evolve better PCNN neurons for image segmentation. Our technique is generally applicable for automatic improvement of most image processing algorithms and neural computing methods. It may be used either to generally improve a given implementation or to tailor that implementation to a specific problem, which with respect to image segmentation for example can be road following for autonomous vehicles or infrared image segmentation for heat seeking missiles that are to distinguish the heat source of the target from flares. (C) 2008 Elsevier B.V. All rights reserved.
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