Computer-Automated Evolution of an X-Band Antenna for NASA's Space Technology 5 Mission

2011 ◽  
Vol 19 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Gregory. S. Hornby ◽  
Jason D. Lohn ◽  
Derek S. Linden
Keyword(s):  
Evolutionary Algorithms ◽  
Domain Knowledge ◽  
Current Practice ◽  
Design Space ◽  
Fitness Function ◽  
Antenna Design ◽  
Space Technology ◽  
X Band ◽  
Spacecraft Orbit

Whereas the current practice of designing antennas by hand is severely limited because it is both time and labor intensive and requires a significant amount of domain knowledge, evolutionary algorithms can be used to search the design space and automatically find novel antenna designs that are more effective than would otherwise be developed. Here we present our work in using evolutionary algorithms to automatically design an X-band antenna for NASA's Space Technology 5 (ST5) spacecraft. Two evolutionary algorithms were used: the first uses a vector of real-valued parameters and the second uses a tree-structured generative representation for constructing the antenna. The highest-performance antennas from both algorithms were fabricated and tested and both outperformed a hand-designed antenna produced by the antenna contractor for the mission. Subsequent changes to the spacecraft orbit resulted in a change in requirements for the spacecraft antenna. By adjusting our fitness function we were able to rapidly evolve a new set of antennas for this mission in less than a month. One of these new antenna designs was built, tested, and approved for deployment on the three ST5 spacecraft, which were successfully launched into space on March 22, 2006. This evolved antenna design is the first computer-evolved antenna to be deployed for any application and is the first computer-evolved hardware in space.

Generative Optimization for Automatic Creation of 3D CAD Platforms

2021 ◽  
Author(s):  
William F. Quintero-Restrepo ◽  
Brian K. Smith ◽  
Junfeng Ma
Keyword(s):  
Evolutionary Algorithms ◽  
Six Sigma ◽  
Design Space ◽  
Optimization Method ◽  
Efficiency Improvement ◽  
3D Cad ◽  
Systems Model ◽  
Electric Machinery ◽  
Definition Of ◽  
Six Sigma Approach

Abstract The efficient creation of 3D CAD platforms can be achieved by the optimization of their design process. The research presented in this article showcases a method for allowing such efficiency improvement. The method is based on the DMADV six sigma approach. During the Define step, the definition of the scope and design space is established. In the Measure step, the initial evaluation of the platforms to be improved is done with the help of a Metrics framework for 3D CAD platforms. The Analyze Step includes the identification and optimization of the systems’ model of the process based on the architecture and the multiple objectives required for the improvement. The optimization method used that is based on evolutionary algorithms allows for the identification of the best improvement alternatives for the next step. During Design step of the method, the improvement alternatives are planned and executed. In the final Verification step, the evaluation of the improved process is tested against the previous status with the help of the Metrics Framework for 3D CAD platforms. The method is explained with an example case of a 3D CAD platform for creating metallic boxes for electric machinery.

Performance evaluation of efficient multi-objective evolutionary algorithms for design space exploration of embedded computer systems

2011 ◽  
Vol 11 (1) ◽  
pp. 382-398 ◽  
Author(s):  
Giuseppe Ascia ◽  
Vincenzo Catania ◽  
Alessandro G. Di Nuovo ◽  
Maurizio Palesi ◽  
Davide Patti
Keyword(s):  
Performance Evaluation ◽  
Evolutionary Algorithms ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Computer Systems ◽  
Multi Objective ◽  
Embedded Computer

Comparison of Multi-Objective Evolutionary Algorithms to Prioritize Regression Test Cases

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1454
Keyword(s):  
Fault Detection ◽  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Optimal Solution ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Nsga Ii ◽  
Multi Objective ◽  
Regression Test

Regression testing is one of the most critical testing activities among software product verification activities. Nevertheless, resources and time constraints could inhibit the execution of a full regression test suite, hence leaving us in confusion on what test cases to run to preserve the high quality of software products. Different techniques can be applied to prioritize test cases in resource-constrained environments, such as manual selection, automated selection, or hybrid approaches. Different Multi-Objective Evolutionary Algorithms (MOEAs) have been used in this domain to find an optimal solution to minimize the cost of executing a regression test suite while obtaining maximum fault detection coverage as if the entire test suite was executed. MOEAs achieve this by selecting set of test cases and determining the order of their execution. In this paper, three Multi Objective Evolutionary Algorithms, namely, NSGA-II, IBEA and MoCell are used to solve test case prioritization problems using the fault detection rate and branch coverage of each test case. The paper intends to find out what’s the most effective algorithm to be used in test cases prioritization problems, and which algorithm is the most efficient one, and finally we examined if changing the fitness function would impose a change in results. Our experiment revealed that NSGA-II is the most effective and efficient MOEA; moreover, we found that changing the fitness function caused a significant reduction in evolution time, although it did not affect the coverage metric.

An Explorative Study of Virtual Product Placement

2011 ◽  
pp. 122-147
Author(s):  
Chia-Hu Chang ◽  
Ja-Ling Wu
Keyword(s):  
Domain Knowledge ◽  
Design Space ◽  
State Of The Art ◽  
Product Placement ◽  
The State ◽  
Explorative Study ◽  
Advertising Message ◽  
Computational Aesthetics ◽  
The Right ◽  
Virtual Product

With the aid of content-based multimedia analysis, virtual product placement opens up new opportunities for advertisers to effectively monetize the existing videos in an efficient way. In addition, a number of significant and challenging issues are raising accordingly, such as how to less-intrusively insert the contextually relevant advertising message (what) at the right place (where) and the right time (when) with the attractive representation (how) in the videos. In this chapter, domain knowledge in support of delivering and receiving the advertising message is introduced, such as the advertising theory, psychology and computational aesthetics. We briefly review the state of the art techniques for assisting virtual product placement in videos. In addition, we present a framework to serve the virtual spotlighted advertising (ViSA) for virtual product placement and give an explorative study of it. Moreover, observations about the new trend and possible extension in the design space of virtual product placement will also be stated and discussed. We believe that it would inspire the researchers to develop more interesting and applicable multimedia advertising systems for virtual product placement.

Evolutionary Algorithms

2016 ◽  
pp. 190-215 ◽  
Author(s):  
Ka-Chun Wong
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Real World ◽  
Fitness Function ◽  
Search Performance ◽  
Parallel Search ◽  
Survival Selection ◽  
Parent Selection ◽  
History Of ◽  
Search Capability

Inspired from nature, evolutionary algorithms have been proven effective and unique in different real world applications. Comparing to traditional algorithms, its parallel search capability and stochastic nature enable it to excel in search performance in a unique way. In this chapter, evolutionary algorithms are reviewed and discussed from concepts and designs to applications in bioinformatics. The history of evolutionary algorithms is first discussed at the beginning. An overview on the state-of-the-art evolutionary algorithm concepts is then provided. Following that, the related design and implementation details are discussed on different aspects: representation, parent selection, reproductive operators, survival selection, and fitness function. At the end of this chapter, real world evolutionary algorithm applications in bioinformatics are reviewed and discussed.

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