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A Many Threaded CUDA Interpreter for Genetic Programming
"... Abstract. A Single Instruction Multiple Thread CUDA interpreter provides SIMD like parallel evaluation of the whole GP population of 1 4 million reverse polish notation (RPN) expressions on graphics cards and nVidia Tesla. Using submachine code tree GP a sustain peak performance of 665 billion GP o ..."
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Abstract. A Single Instruction Multiple Thread CUDA interpreter provides SIMD like parallel evaluation of the whole GP population of 1 4 million reverse polish notation (RPN) expressions on graphics cards and nVidia Tesla. Using submachine code tree GP a sustain peak performance of 665 billion GP operations per second (10,000 speed up) and an average of 22 peta GP ops per day is reported for a single GPU card on a Boolean induction benchmark never attempted before, let alone solved. 1
Automatic synthesis of both the topology and parameters for a robust controller for a nonminimal phase plant and a threelag plant by means of genetic programming
 Proceedings of 1999 IEEE Conference on Decision and Control. Pages 5292
, 1999
"... This paper describes how the process of synthesizing the design of both the topology and the numerical parameter values (tuning) for a controller can be automated by using genetic programming. Genetic programming can be used to automatically make the decisions concerning the total number of signal p ..."
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This paper describes how the process of synthesizing the design of both the topology and the numerical parameter values (tuning) for a controller can be automated by using genetic programming. Genetic programming can be used to automatically make the decisions concerning the total number of signal processing blocks to be employed in a controller, the type of each block, the topological interconnections between the blocks, and the values of all parameters for all blocks requiring parameters. In synthesizing the design of controllers, genetic programming can simultaneously optimize prespecified performance metrics (such as minimizing the time required to bring the plant output to the desired value), satisfy timedomain constraints (such as overshoot and disturbance rejection), and satisfy frequency domain constraints. Evolutionary methods have the advantage of not being encumbered by preconceptions that limit its search to welltraveled paths. Genetic programming is applied to an illustrative problem involving the design of a controller for a threelag plant with a significant (fivesecond) time delay in the external feedback from the plant to the controller. A delay in the feedback makes the design of an effective controller especially difficult. 1
Design of Decentralized Controllers for SelfReconfigurable Modular Robots Using Genetic
 Programming”, Proceedings of the 2nd NASA/DoD Workshop on Evolvable Hardware
, 2000
"... Advantages of selfreconfigurable modular robots over conventional robots include physical adaptability, robustness in the presence of failures, and economies of scale. Creating control software for modular robots is one of the central challenges to realizing their potential advantages. Modular robo ..."
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Advantages of selfreconfigurable modular robots over conventional robots include physical adaptability, robustness in the presence of failures, and economies of scale. Creating control software for modular robots is one of the central challenges to realizing their potential advantages. Modular robots differ enough from traditional robots that new techniques must be found to create software to control them. The novel difficulties are due to the fact that modular robots are ideally controlled in a decentralized manner, dynamically change their connectivity topology, may contain hundreds or thousands of modules, and are expected to perform tasks properly even when some modules fail. We demonstrate the use of genetic programming to automatically create distributed controllers for selfreconfigurable modular robots. 1
Evolution of a Controller with a Free Variable Using Genetic Programming
 Genetic Programming, Proceedings of EuroGP'000, volume 1802 of LNCS
, 2000
"... A mathematical formula containing one or more free variables is "general" in the sense that it provides a solution to an entire category of problems. For example, the familiar formula for solving a quadratic equation contains free variables representing the equation's coefficients. Previous work has ..."
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Cited by 4 (0 self)
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A mathematical formula containing one or more free variables is "general" in the sense that it provides a solution to an entire category of problems. For example, the familiar formula for solving a quadratic equation contains free variables representing the equation's coefficients. Previous work has demonstrated that genetic programming can automatically synthesize the design for a controller consisting of a topological arrangement of signal processing blocks (such as integrators, differentiators, leads, lags, gains, adders, inverters, and multipliers), where each block is further specified ("tuned") by a numerical component value, and where the evolved controller satisfies userspecified requirements. The question arises as to whether it is possible to use genetic programming to automatically create a "generalized" controller for an entire category of such controller design problems # instead of a single instance of the problem. This paper shows, for an illustrative problem, how genetic programming can be used to create the design for both the topology and tuning of controller, where the controller contains a free variable. 1
EHWPack: A Parallel Software/Hardware Environment for Evolvable Hardware
 Proceedings of the Genetics and Evolutionary Computation Conference (GECCO2000), July 812, 2000,pg.538539. Las Vegas
, 2000
"... This paper describes the EHWPack development system, a tool that performs the evolutionary synthesis of electronic circuits, using the SPICE simulator and the Field Programmable Transistor Array hardware (FPTA) developed at JPL. EHWPack integrates free and commercial software packages such as PGAPac ..."
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This paper describes the EHWPack development system, a tool that performs the evolutionary synthesis of electronic circuits, using the SPICE simulator and the Field Programmable Transistor Array hardware (FPTA) developed at JPL. EHWPack integrates free and commercial software packages such as PGAPack for the evolutionary algorithm, Spice for the circuit evaluation, TclTk for the graphic interface, and LabView for the hardware evaluation. The paper investigates the performance of the tool in two typical problems of EHW: evolutionary synthesis of a Gaussian computational function and the evolution of a bandpass filter. 1
MultiObjective Competitive Coevolution for Efficient GP Classifier Problem Decomposition ∗
, 2007
"... A novel approach to the classification of large and unbalanced multiclass data sets is presented where the widely acknowledged issues of scalability, solution transparency, and problem decomposition are addressed simultaneously within the context of the Genetic Programming (GP) paradigm. A cooperati ..."
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Cited by 4 (3 self)
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A novel approach to the classification of large and unbalanced multiclass data sets is presented where the widely acknowledged issues of scalability, solution transparency, and problem decomposition are addressed simultaneously within the context of the Genetic Programming (GP) paradigm. A cooperative coevolutionary training environment that employs multiobjective evaluation provides the basis for problem decomposition and reduced solution complexity, while scalability is achieved through a Pareto competitive coevolutionary framework, allowing the system to be applied to large data sets (tens or hundreds of thousands of exemplars) without recourse to hardwarespecific speedups. Moreover, a key departure from the canonical GP approach to classification is utilized in which the output of GP is expressed in terms of a nonbinary, local membership function (e.g. a Gaussian), where it is no longer necessary for an expression to represent an entire class. Decomposition is then achieved through reformulating the classification problem as one of cluster consistency, where an appropriate subset of the training patterns can be associated with each individual such that problems are solved by several specialist classifiers rather than by a single ‘super ’ individual. 1
Automatic design of both topology and tuning of a common parameterized controller for two families of plants using genetic programming
 In Proceedings of Eleventh IEEE International Symposium on ComputerAided Control System Design (CACSD) Conference and Ninth IEEE International Conference on Control Applications (CCA) Conference
"... This paper demonstrates that a technique of evolutionary computation can be used to automatically create the design for both the topology and parameter values (tuning) for a common controller (containing various parameters representing the overall characteristics of the plant) for two families of pl ..."
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This paper demonstrates that a technique of evolutionary computation can be used to automatically create the design for both the topology and parameter values (tuning) for a common controller (containing various parameters representing the overall characteristics of the plant) for two families of plants. The automatically designed controller is created by means of genetic programming using a fitness measure that attempts to optimize step response and disturbance rejection while simultaneously imposing constraints on maximum sensitivity and sensor noise attenuation. The automatically designed controller outperforms the controller designed with conventional techniques. In particular, the automatically designed controller is superior to the Astrom and Hagglund controller for all plants of both families for the integral of the timeweighted absolute error (ITAE) for a step input, the ITAE for disturbance rejection, and maximum sensitivity. Averaged over all plants of both families, the ITAE for the step input for the automatically designed controller is only 58 % of the value for the conventional controller; the ITAE for disturbance rejection is 91 % of the value for the conventional controller; and the maximum sensitivity, Ms. for the automatically designed controller is only 85 % of the value for the conventional controller. The automatically designed controller is "general " in the sense that it contains free variables and therefore provides a solution to an entire category of problems (i.e., all the plants in the two families) ⎯ not merely a single instance of the problem (i.e., a particular single plant). 1
Iterative Refinement of Computational Circuits Using Genetic Programming
 IEEE POSIX. IEEE POSIX 1003.1c Threads API
, 2002
"... Previous work has shown that genetic programming is capable of creating analog electrical circuits whose output equals common mathematical functions, merely by specifying the desired mathematical function that is to be produced. This paper extends this work by generating computational circuits ..."
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Previous work has shown that genetic programming is capable of creating analog electrical circuits whose output equals common mathematical functions, merely by specifying the desired mathematical function that is to be produced. This paper extends this work by generating computational circuits whose output is an approximation to the error function associated with an existing computational circuit (created by means of genetic programming or some other method). The output of the evolved circuit can then be added to the output of the existing circuit to produce a circuit that computes the desired function with greater accuracy. This process can be performed iteratively. We present a set of results showing the effectiveness of this approach over multiple iterations for generating squaring, square root, and cubing computational circuits. We also perform iterative refinement on a recently patented cubic signal generator circuit, obtaining a refined circuit that is 7.2 times more accurate than the original patented circuit. The iterative refinement process described herein can be viewed as a method for using previous knowledge (i.e. the existing circuit) to obtain an improved result.
Automatic synthesis of electrical circuits containing a free variable using genetic programming
 Proceedings of the Genetic and Evolutionary Computation Conference, 477–484. Las Vegas
, 2000
"... A mathematical formula containing one or more free variables is "general " in the sense that it represents the solution to all instances of a problem (instead of just the solution of a single instance of the problem). For example, the familiar formula for solving a quadratic equation contains free v ..."
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A mathematical formula containing one or more free variables is "general " in the sense that it represents the solution to all instances of a problem (instead of just the solution of a single instance of the problem). For example, the familiar formula for solving a quadratic equation contains free variables representing the coefficients of the tobesolved equation. This paper demonstrates, using an illustrative problem, that genetic programming can automatically create the design for both the topology and component values for an analog electrical circuit in which the value of each component in the evolved circuit is specified by a mathematical expression containing a free variable. That is, genetic programming is used to evolve a general parameterized circuit that satisfies the problem's highlevel requirements. The evolved circuit has been crossvalidated on unseen values of the free variable. 1
Use of TimeDomain Simulations in Automatic Synthesis of Computational Circuits Using Genetic Programming
 Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference. Las Vegas, NV
, 2000
"... Previously reported applications of genetic programming to the automatic synthesis of computational circuits have employed simulations based on DC sweeps. DC sweeps have the advantage of being considerably less timeconsuming than timedomain simulations. However, this type of simulation does ..."
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Previously reported applications of genetic programming to the automatic synthesis of computational circuits have employed simulations based on DC sweeps. DC sweeps have the advantage of being considerably less timeconsuming than timedomain simulations. However, this type of simulation does not necessarily lead to robust circuits that correctly perform the desired mathematical function over time. This paper addresses the problem of automatically synthesizing computational circuits using multiple timedomain simulations and presents results involving the synthesis of both the topology and sizing for a squaring, square root, and multiplier computational circuit and a lag circuit (from the field of control).