Genetic Programming

2011 ◽  
pp. 926-931 ◽  
Author(s):  
William H. Hsu
Keyword(s):  
Genetic Programming ◽  
Programming Languages ◽  
Genetic Material ◽  
Biological Evolution ◽  
Validation Data ◽  
Functional Programming Languages ◽  
Real World Problem ◽  
Problem Solving Environment ◽  
Fitness Criterion ◽  
The Given

Genetic programming (GP) is a sub-area of evolutionary computation first explored by John Koza (1992) and independently developed by Nichael Lynn Cramer (1985). It is a method for producing computer programs through adaptation according to a user-defined fitness criterion, or objective function. Like genetic algorithms, GP uses a representation related to some computational model, but in GP, fitness is tied to task performance by specific program semantics. Instead of strings or permutations, genetic programs are most commonly represented as variable-sized expression trees in imperative or functional programming languages, as grammars (O’Neill & Ryan, 2001), or as circuits (Koza et al., 1999). GP uses patterns from biological evolution to evolve programs: • Crossover: Exchange of genetic material such as program subtrees or grammatical rules • Selection: The application of the fitness criterion to choose which individuals from a population will go on to reproduce • Replication: The propagation of individuals from one generation to the next • Mutation: The structural modification of individuals To work effectively, GP requires an appropriate set of program operators, variables, and constants. Fitness in GP is typically evaluated over fitness cases. In data mining, this usually means training and validation data, but cases can also be generated dynamically using a simulator or directly sampled from a real-world problem solving environment. GP uses evaluation over these cases to measure performance over the required task, according to the given fitness criterion.

scholarly journals Dynamic game semantics

2020 ◽  
pp. 1-60
Author(s):  
Norihiro Yamada ◽  
Samson Abramsky
Keyword(s):  
Programming Languages ◽  
Functional Programming ◽  
Operational Semantics ◽  
Dynamic Game ◽  
Standard Interpretation ◽  
Functional Programming Languages ◽  
Game Semantics ◽  
Wide Range ◽  
Cartesian Closed Categories ◽  
Cartesian Closed

Abstract The present work achieves a mathematical, in particular syntax-independent, formulation of dynamics and intensionality of computation in terms of games and strategies. Specifically, we give game semantics of a higher-order programming language that distinguishes programmes with the same value yet different algorithms (or intensionality) and the hiding operation on strategies that precisely corresponds to the (small-step) operational semantics (or dynamics) of the language. Categorically, our games and strategies give rise to a cartesian closed bicategory, and our game semantics forms an instance of a bicategorical generalisation of the standard interpretation of functional programming languages in cartesian closed categories. This work is intended to be a step towards a mathematical foundation of intensional and dynamic aspects of logic and computation; it should be applicable to a wide range of logics and computations.

Real-time MLton: A Standard ML runtime for real-time functional programs

2021 ◽  
Vol 31 ◽  
Author(s):  
BHARGAV SHIVKUMAR ◽  
JEFFREY MURPHY ◽  
LUKASZ ZIAREK
Keyword(s):  
Programming Languages ◽  
Real Time ◽  
Concurrency Control ◽  
General Purpose ◽  
Functional Languages ◽  
Control Mechanisms ◽  
Real Time Systems ◽  
Functional Programming Languages ◽  
Standard Ml ◽  
Time Systems

Abstract There is a growing interest in leveraging functional programming languages in real-time and embedded contexts. Functional languages are appealing as many are strictly typed, amenable to formal methods, have limited mutation, and have simple but powerful concurrency control mechanisms. Although there have been many recent proposals for specialized domain-specific languages for embedded and real-time systems, there has been relatively little progress on adapting more general purpose functional languages for programming embedded and real-time systems. In this paper, we present our current work on leveraging Standard ML (SML) in the embedded and real-time domains. Specifically, we detail our experiences in modifying MLton, a whole-program optimizing compiler for SML, for use in such contexts. We focus primarily on the language runtime, reworking the threading subsystem, object model, and garbage collector. We provide preliminary results over a radar-based aircraft collision detector ported to SML.

Computer Implementation of Genetic Programming

2018 ◽  
pp. 132-182
Keyword(s):  
Genetic Programming ◽  
Programming Languages ◽  
Programming Language ◽  
Object Oriented Programming ◽  
Computer Implementation ◽  
Classification Problems ◽  
Development Environment ◽  
Terminal Set ◽  
C Programming ◽  
Multiclass Classification Problems

This chapter presents the computer implementation of the tree-based genetic programming in C# programming language. Since C# is a common object-oriented programming language, with little modification the source code presented in the chapter can be easily transformed into Java or C++ programming languages. The chapter covers all aspects of the implementation: node, chromosome, population, function set, and terminal set class implementations. The chapter is carefully structured, so at the end of the chapter fully working GP computer program will be implemented which can solve regression and multiclass classification problems. The reader should not worry about specific operating system, or development environment, since all code implementations are based on cross-OS and open source integrated development environment visual studio code which can run on Windows, Mac, or Linux.

Enzyme Genetic Programming

2004 ◽  
Author(s):  
Michael A. Lones ◽  
Andy M. Tyrrell
Keyword(s):  
Genetic Programming ◽  
Biological Evolution ◽  
Target System ◽  
Search Process ◽  
Automatic Programming ◽  
Genetic Operators ◽  
Viable Systems ◽  
The Right ◽  
And Behavior ◽  
Environment Evolution

Programming is a process of optimization; taking a specification, which tells us what we want, and transforming it into an implementation, a program, which causes the target system to do exactly what we want. Conventionally, this optimization is achieved through manual design. However, manual design can be slow and error-prone, and recently there has been increasing interest in automatic programming; using computers to semiautomate the process of refining a specification into an implementation. Genetic programming is a developing approach to automatic programming, which, rather than treating programming as a design process, treats it as a search process. However, the space of possible programs is infinite, and finding the right program requires a powerful search process. Fortunately for us, we are surrounded by a monotonous search process capable of producing viable systems of great complexity: evolution. Evolution is the inspiration behind genetic programming. Genetic programming copies the process and genetic operators of biological evolution but does not take any inspiration from the biological representations to which they are applied. It can be argued that the program representation that genetic programming does use is not well suited to evolution. Biological representations, by comparison, are a product of evolution and, a fact to which this book is testament, describe computational structures. This chapter is about enzyme genetic programming, a form of genetic programming that mimics biological representations in an attempt to improve the evolvability of programs. Although it would be an advantage to have a familiarity with both genetic programming and biological representations, concise introductions to both these subjects are provided. According to modern biological understanding, evolution is solely responsible for the complexity we see in the structure and behavior of biological organisms. Nevertheless, evolution itself is a simple process that can occur in any population of imperfectly replicating entities where the right to replicate is determined by a process of selection. Consequently, given an appropriate model of such an environment, evolution can also occur within computers.

ПРИМЕНЕНИЕ МОДЕЛИ СИНТАКСИЧЕСКИ УПРАВЛЯЕМОГО ПЕРЕВОДА АРИФМЕТИЧЕСКИХ ВЫРАЖЕНИЙ В ПРОЦЕССЕ ОБУЧЕНИЯ СТУДЕНТОВ

2020 ◽  
Author(s):  
Igor Oblomov ◽  
Vyacheslav Rzhavin ◽  
Natalia Pervova ◽  
Alina Gerasimova
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Learning Process ◽  
Functional Programming ◽  
Syntactic Analysis ◽  
Simple Arithmetic ◽  
Functional Programming Languages ◽  
Source Data ◽  
Prolog Programming ◽  
Arithmetic Expressions

В статье рассматривается модель синтаксически управляемого перевода простых арифметических выражений и ее использование в процессе обучения. Атрибутно-транслируемая грамматика предполагает перевод последовательности актов в последовательность действий, которые, в свою очередь, будут являться исходными данными для следующих этапов трансляции. Раскрываются основные моменты обучения студентов декларативному языку программирования Пролог, делается упор на обработку множества символов действия. Дальнейшие исследования предполагают разработку моделей синтаксического анализа с помощью средств императивных и функциональных языков программирования с целью получения и анализа объективных оценок эффективности полученных моделей в процессе обучения будущих специалистов.This article discusses the model of syntactically controlled translation of simple arithmetic expressions and its use in the learning process. The attribute-translated grammar involves the translation of a sequence of acts into a sequence of actions, which will be the source data for the next stages of translation. The article reveals the main points of teaching students the Prolog programming language, focuses on the processing of many action symbols. Further research involves the development of models of syntactic analysis by means of imperative and functional programming languages in order to obtain and analyze the objective estimates of the effectiveness of the obtained models in the training of future specialists.

scholarly journals What more than a hundred project groups reveal about teaching visualization

2020 ◽  
Vol 23 (5) ◽  
pp. 895-911 ◽  
Author(s):  
Michael Burch ◽  
Elisabeth Melby
Keyword(s):  
Programming Languages ◽  
Information Visualization ◽  
Teaching Strategies ◽  
Data Science ◽  
Major Goal ◽  
Wide Range ◽  
Project Groups ◽  
Small Project ◽  
Programming Skills ◽  
The Given

Abstract The growing number of students can be a challenge for teaching visualization lectures, supervision, evaluation, and grading. Moreover, designing visualization courses by matching the different experiences and skills of the students is a major goal in order to find a common solvable task for all of them. Particularly, the given task is important to follow a common project goal, to collaborate in small project groups, but also to further experience, learn, or extend programming skills. In this article, we survey our experiences from teaching 116 student project groups of 6 bachelor courses on information visualization with varying topics. Moreover, two teaching strategies were tried: 2 courses were held without lectures and assignments but with weekly scrum sessions (further denoted by TS1) and 4 courses were guided by weekly lectures and assignments (further denoted by TS2). A total number of 687 students took part in all of these 6 courses. Managing the ever growing number of students in computer and data science is a big challenge in these days, i.e., the students typically apply a design-based active learning scenario while being supported by weekly lectures, assignments, or scrum sessions. As a major outcome, we identified a regular supervision either by lectures and assignments or by regular scrum sessions as important due to the fact that the students were relatively unexperienced bachelor students with a wide range of programming skills, but nearly no visualization background. In this article, we explain different subsequent stages to successfully handle the upcoming problems and describe how much supervision was involved in the development of the visualization project. The project task description is given in a way that it has a minimal number of requirements but can be extended in many directions while most of the decisions are up to the students like programming languages, visualization approaches, or interaction techniques. Finally, we discuss the benefits and drawbacks of both teaching strategies. Graphic abstract

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