BibSonomy publications for /user/brazovayeye/automaticallyThu Jun 19 17:46:40 CEST 2008Seattle, Washington, USA{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation8-12 July911--918Embedded cartesian genetic programming and the lawnmower and hierarchical-if-and-only-if problems12006Cartesian acquisition, algorithms, automatically defined embedded evolution, functions, genetic hierarchical-if-and-only-if, lawnmower module problem, program programming, synthesis synthesis, Thu Jun 19 17:46:40 CEST 2008Seattle, Washington, USA{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation8-12 July903--910A multi-chromosome approach to standard and embedded cartesian genetic programming12006Cartesian ES, acquisition, algorithms, automatically circuits, defined digital embedded evolution, evolutionary functions, genetic module multi-chromosome multi-chromosome, program programming, strategy, synthesis synthesis, Thu Jun 19 17:46:40 CEST 2008San Francisco, California, USA2001 Genetic and Evolutionary Computation Conference Late Breaking Papers9-11 July359--365Subtree Encapsulation Versus {ADFs} in Genetic Programming for the Even-5-Parity Problem2001algorithms, automatically define functions genetic programming, Thu Jun 19 17:35:00 CEST 2008New YorkGECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference9-13 July1383--1390Code Factoring And The Evolution Of Evolvability2002automatically code defined dynamic engineering engineering, environment, evolution evolvability, factoring, functions, genetic of programming, search-based software Thu Jun 19 17:35:00 CEST 2008IEEE Transactions on Circuits and Systems {II}: Analog and Digital Signal ProcessingDecember12977--983Evolutionary synthesis of digital filter structures using genetic programming502003IIR S-expressions, algorithms, automatically computability, defined difference digital effects, equations, errors, evolutionary filter filter, filters, fitness fourth-order function functions, genetic global infinite-impulse low-coefficient low-output matrices, matrix measure, method, noise, optimization, programming, representation, response roundoff sensitivity, structures, subroutines, synthesis transfer wordlength This paper presents a synthesis method for infinite-impulse response (IIR) digital filter structures using genetic programming with automatically defined functions (GP-ADF). In the proposed method, digital filter structures are represented as S-expressions with subroutines, which are written directly from the set of difference equations. This paper also shows the condition for the constructing the S-expressions that represent the filter structures without delay-free loops. Numerical examples synthesize two-filter structures: the low-coefficient sensitivity fourth-order filter structure and the low-output roundoff noise second-order filter structure.Thu Jun 19 17:35:00 CEST 2008IEEE Signal Processing LettersApril483--85Synthesis of low-sensitivity second-order digital filters using genetic programming with automatically defined functions72000ADF IIR S-expressions, algorithms, automatically coefficient defined digital filter filter, filters, fitness functions, genetic low magnitude measure, method, programming, second-order sensitivity sensitivity, synthesis This letter proposes a synthesis method for low coefficient sensitivity second-order IIR digital filter structures using genetic programming with automatically defined functions (GP-ADF). In this letter, digital filter structures are represented as S-expressions with subroutines. It is easy to generate syntactically valid S-expressions and perform the genetic operations, because the representation is suitable for GP. A numerical example uses the fitness measure, including the magnitude sensitivity, and demonstrates that the proposed method can synthesize efficiently very low coefficient sensitivity filter structures.Thu Jun 19 17:35:00 CEST 2008Gower Street, London, WC1E 6BT, UKJanuaryRN/96/4Evolving AgentsResearch Note1996(ADF) AI, Agent Artificial Automatic Automatically Based Code Communication, Computing, Defined Distributed Evolution, Functions Generation, Learning, Machine Programming, algorithms, genetic programming, The paradigm of agent based computing is becoming increasingly popular both in distributed artificial intelligence and as a general software engineering technique. The difficulty with agent based computing is that success depends not on the correctness of any one agent, but on the emergent behaviour arising from the interaction of a society of agents. As a consequence, the problem of programming agents is non trivial and poorly understood. In this paper we show that genetic programming can be used to automatically program agents which communicate and interact to solve problems. The programs evolved simulataneously define when and what to communicate, and how to use the communicated information to solve the given problem.Thu Jun 19 17:35:00 CEST 2008Stanford University, CA, USAGenetic Programming 1996: Proceedings of the First Annual Conference28--31 July141--148Using Data Structures within Genetic Programming1996(ADF), Artificial Automatic Automatically CFG, Data Defined Demes Dyck Evolution, Expressions, Functions Learning, Machine Object Oriented Pareto Polish Programming, Push Reverse Stack, Structures, algorithms, automatic brackets, code context down fitness, free generation, genetic grammar induction, language language, matched programming, Provision of appropriately structured memory is shown, in some cases, to be advantageous to genetic programming (GP) in comparison with directly addressable indexed memory. Three ``classic'' problems are solved. The first two require the GP to distinguish between sentences that are in a context free language and those that are not given positive and negative training examples of the language. The two languages are, correctly nested brackets and a Dyck language (correctly nested brackets of different types). The third problem is to evaluate integer Reverse Polish (postfix) expressions. Comparisons are made between GP attempting to solve these problems when provided with indexed memory or with stack data structures.Thu Jun 19 17:35:00 CEST 2008Gower Street, London, WC1E 6BT, UKJanuaryRN/96/1Using Data Structures within Genetic ProgrammingResearch Note1996(ADF), Artificial Automatic Automatically Data Defined Demes Evolution, Expressions, Functions Learning, Machine Object Oriented Pareto Polish Programming, Push Reverse Stack, Structures, algorithms, automatic code context down fitness, free generation, genetic induction, language programming, In earlier work we showed that GP can automatically generate simple data types (stacks, queues and lists). The results presented herein show, in some cases, provision of appropriately structured memory can indeed be advantageous to GP in comparison with directly addressable indexed memory. Three ``classic'' problems are solved. The first two require the GP to distinguish between sentences that are in a language and those that are not given positive and negative training examples of the language. The two languages are, correctly nested brackets and a Dyck language (correctly nested brackets of different types). The third problem is to evaluate integer Reverse Polish (postfix) expressions. Comparisons are made between GP attempting to solve these problems when provided with indexed memory or with stack data structures.Thu Jun 19 17:35:00 CEST 2008Pittsburgh, PA, USAGenetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)15-19 July295--302Evolving Data Structures Using Genetic Programming1995(ADF), (FIFO) Artificial Automatic Automatically Data Defined Demes Evolution, First-in Functions Learning, Machine Object Oriented Pareto Programming, Push Queue, Stack, Structures, algorithms, down first-out fitness, genetic programming, Genetic programming (GP) is a subclass of genetic algorithms (GAs), in which evolving programs are directly represented in the chromosome as trees. Recently it has been shown that programs which explicitly use directly addressable memory can be generated using GP. It is established good software engineering practice to ensure that programs use memory via abstract data structures such as stacks, queues and lists. These provide an interface between the program and memory, freeing the program of memory management details which are left to the data structures to implement. The main result presented herein is that GP can automatically generate stacks and queues. Typically abstract data structures support multiple operations, such as put and get. We show that GP can simultaneously evolve all the operations of a data structure by implementing each such operation with its own independent program tree. That is, the chromosome consists of a fixed number of independent program trees. Moreover, crossover only mixes genetic material of program trees that implement the same operation. Program trees interact with each other only via shared memory and shared ``Automatically Defined Functions'' (ADFs). ADFs, ``pass by reference'' when calling them, Pareto selection, ``good software engineering practice'' and partitioning the genetic population into ``demes'' where also investigated whilst evolving the queue in order to improve the GP solutions.Thu Jun 19 17:35:00 CEST 2008Gower Street, London, WC1E 6BT, UKJanuaryRN/95/1Evolving Data Structures Using Genetic ProgrammingResearch Note1995(ADF), (FIFO) Artificial Automatic Automatically Data Defined Demes Evolution, First-in Functions Learning, Machine Object Oriented Pareto Programming, Push Queue, Stack, Structures, algorithms, down first-out fitness, genetic programming, Genetic programming (GP) is a subclass of genetic algorithms (GAs), in which evolving programs are directly represented in the chromosome as trees. Recently it has been shown that programs which explicitly use directly addressable memory can be generated using GP. It is established good software engineering practice to ensure that programs use memory via abstract data structures such as stacks, queues and lists. These provide an interface between the program and memory, freeing the program of memory management details which are left to the data structures to implement. The main result presented herein is that GP can automatically generate stacks and queues. Typically abstract data structures support multiple operations, such as put and get. We show that GP can simultaneously evolve all the operations of a data structure by implementing each such operation with its own independent program tree. That is, the chromosome consists of a fixed number of independent program trees. Moreover, crossover only mixes genetic material of program trees that implement the same operation. Program trees interact with each other only via shared memory and shared ``Automatically Defined Functions'' (ADFs). ADFs, ``pass by reference'' when calling them, Pareto selection, ``good software engineering practice'' and partitioning the genetic population into ``demes'' where also investigated whilst evolving the queue in order to improve the GP solutions.Thu Jun 19 17:35:00 CEST 2008Genetic Programming Theory and Practise14221--237Automated Synthesis by Means of Genetic Programming of Complex Structures Incorporating Reuse, Hierarchies, Development, and Parameterized Toplogies2003Hierarchy, algorithms, architecture-altering automatically circuits, controllers defined development, functions, genetic iterations, loops, operations, parameterized programming, recursions, reuse, stores, topologies, Genetic programming can be used as an automated invention machine to synthesise designs for complex structures. In particular, genetic programming has automatically synthesized complex structures that infringe, improve upon, or duplicate the functionality of 21 previously patented inventions (including analog electrical circuits, controllers, and mathematical algorithms). Genetic programming has also generated two patentable new inventions (involving controllers). Genetic programming has also generated numerous additional human-competitive results involving the design of quantum computing circuits as well as other substantial results involving antennae, networks of chemical reactions (metabolic pathways), and genetic networks. We believe that these results are the direct consequence of a group of techniques, many unique to genetic programming, that facilitate the automatic synthesis of complex structures. These techniques include automatic reuse, parameterised reuse, parameterised topologies, and developmental genetic programming. The paper describes these techniques and how they contribute to automated design.Thu Jun 19 17:35:00 CEST 2008New YorkGECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference9-13 July756--763Machine Vision: Exploring Context With Genetic Programming2002algorithms, analysis, automatically crawler, data defined detection functions, genetic image machine programming, target vision, Thu Jun 19 17:35:00 CEST 2008Seattle, WA, USALate breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2006)}8-12 JulyModel for Evolutionary Technology - An Automatically Defined Terminal Approach2006Agent-Based Automatically Defined Modeling Terminal, algorithms, genetic programming, automatically defined terminal (ADT) to keep ready and stable building blocks growing into complex structure. The idea is originated from the functional modularity approach. ADT is tested in an agent-based innovation model to see how it works and whether there is any improvement in searching new commodities for commercialising in the market; hence the market represents an environment for nourishing the development during innovative process. This paper will not only show how the capable producers with ADT work, but also how market selection plays an important role in the evolution of innovation. In other word, the agent-based modelling approach will present the evolutionary dynamic of interaction between producers and consumers in a commodity market.