An Evolved Antenna for Deployment on Nasa’s Space Technology 5 Mission

Lohn, Jason; Hornby, Gregory S.; Linden, D.S.

doi:10.1007/0-387-23254-0_18

Cited by 71 publications

(41 citation statements)

References 8 publications

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“…Similarly, for wire antennas [69,70,139], the representational scheme is based on the way a draftsman might draw an antenna (either on paper or using a two-or three-dimensional graphical interface for engineering drawings). In particular, the function set consists of one key function that inserts a straight segment of metal wire with a specified length (or no wire) and at a specified angle starting at a specified point, using a turtle [128,139].…”

Section: Native Representationsmentioning

confidence: 99%

Human-competitive results produced by genetic programming

Koza

2010

Genet Program Evolvable Mach

269

139

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Genetic programming has now been used to produce at least 76 instances of results that are competitive with human-produced results. These human-competitive results come from a wide variety of fields, including quantum computing circuits, analog electrical circuits, antennas, mechanical systems, controllers, game playing, finite algebras, photonic systems, image recognition, optical lens systems, mathematical algorithms, cellular automata rules, bioinformatics, sorting networks, robotics, assembly code generation, software repair, scheduling, communication protocols, symbolic regression, reverse engineering, and empirical model discovery. This paper observes that, despite considerable variation in the techniques employed by the various researchers and research groups that produced these human-competitive results, many of the results share several common features. Many of the results were achieved by using a developmental process and by using native representations regularly used by engineers in the fields involved. The best individual in the initial generation of the run of genetic programming often contains only a small number of operative parts. Most of the results that duplicated the functionality of previously issued patents were novel solutions, not infringing solutions. In addition, the production of human-competitive results, as well as the increased intricacy of the results, are broadly correlated to increased availability of computing power tracked by Moore's law. The paper ends by predicting that the increased availability of computing power (through both parallel computing and Moore's law) should result in the production, in the future, of an increasing flow of human-competitive results, as well as more intricate and impressive results.

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Section: Native Representationsmentioning

confidence: 99%

Human-competitive results produced by genetic programming

Koza

2010

Genet Program Evolvable Mach

269

139

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“…The Numerical Electromagnetics Code (NEC-2) is a MoM-based, user-oriented computer code for the analysis of the electromagnetic response of wire antennas and other metallic structures (Lohn et al, 2005). It is built around the numerical solution of the integral equations for the currents induced on the structure.…”

Section: Sed Applied To the Design Of Wire Antennasmentioning

confidence: 99%

“…First of all, the gain is the far-field parameter of interest and, second, this prevents from considering superdirective antennas, both during the evolution and as final solution, which is even worse. NEC has been successfully used to model a wide range of antennas, with high accuracy (Burke & Poggio, 1976a, 1976b, 1981Deadrick et al, 1977) and is now considered as one of the reference electromagnetic software (Lohn et al, 2005;Linden & Altshuler, 1996. However, since SED is by no means linked, or tailored, to NEC, a different, and most effective, EM software could be used, to reduce the total computational time, further improving the accuracy of the simulation.…”

Section: Sed Applied To the Design Of Wire Antennasmentioning

confidence: 99%

Structure-Based Evolutionary Design Applied to Wire Antennas

Casula¹,

Mazzarella²

2012

Genetic Programming - New Approaches and Successful Applications

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“…Given a set of functions and terminals and an initial population of randomly generated syntax trees (representing programs), the programs are evolved through genetic recombination and natural selection. GP has been applied to many different problems; from classical tasks, such as function fitting or pattern recognition, to non-trivial tasks that are competitive with significant human endeavours such as designing electrical circuits [25] or antennas [26].…”

Section: Previous Workmentioning

confidence: 99%

“…Similar to other applications of GP [24][25][26] for solving optimization problems, we use four basic operators: addition, subtraction, multiplication, and division for creating a CDR. Furthermore, we employ a well-known Automatically Defined Function (ADF) (proposed by Koza [28]).…”

Section: Function Setmentioning

confidence: 99%

Designing Dispatching Rules to Minimize Total Tardiness

Tay

2007

Evolutionary Scheduling

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Summary. We approximate optimal solutions to the Flexible Job-Shop Problem by using dispatching rules discovered through Genetic Programming. While Simple Priority Rules have been widely applied in practice, their efficacy remains poor due to lack of a global view. Composite Dispatching Rules have been shown to be more effective as they are constructed through human experience. In this work, we employ suitable parameter and operator spaces for evolving Composite Dispatching Rules using Genetic Programming, with an aim towards greater scalability and flexibility. Experimental results show that Composite Dispatching Rules generated by our Genetic Programming framework outperforms the Single and Composite Dispatching Rules selected from literature over large validation sets with respect to total tardiness. Further results on sensitivity to changes (in coefficient values and terminals) among the evolved rules indicate that their designs are optimal.

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An Evolved Antenna for Deployment on Nasa’s Space Technology 5 Mission

Cited by 71 publications

References 8 publications

Human-competitive results produced by genetic programming

Human-competitive results produced by genetic programming

Structure-Based Evolutionary Design Applied to Wire Antennas

Designing Dispatching Rules to Minimize Total Tardiness

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