Human-competitive evolved antennas

2008 ◽  
Vol 22 (3) ◽  
pp. 235-247 ◽  
Author(s):  
Jason D. Lohn ◽  
Gregory S. Hornby ◽  
Derek S. Linden
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Development Process ◽  
Space Technology ◽  
Circularly Polarized ◽  
Similar Performance ◽  
Traditional Design ◽  
Structured Representation

AbstractWe present a case study showing a human-competitive design of an evolved antenna that was deployed on a NASA spacecraft in 2006. We were fortunate to develop our antennas in parallel with another group using traditional design methodologies. This allowed us to demonstrate that our techniques were human-competitive because our automatically designed antenna could be directly compared to a human-designed antenna. The antennas described below were evolved to meet a challenging set of mission requirements, most notably the combination of wide beamwidth for a circularly polarized wave and wide bandwidth. Two evolutionary algorithms were used in the development process: one used a genetic algorithm style representation that did not allow branching in the antenna arms; the second used a genetic programming style tree-structured representation that allowed branching in the antenna arms. The highest performance antennas from both algorithms were fabricated and tested, and both yielded very similar performance. Both antennas were comparable in performance to a hand-designed antenna produced by the antenna contractor for the mission, and so we consider them examples of human-competitive performance by evolutionary algorithms. Our design was approved for flight, and three copies of it were successfully flown on NASA's Space Technology 5 mission between March 22 and June 30, 2006. These evolved antennas represent the first evolved hardware in space and the first evolved antennas to be deployed.

Multi-Objective Optimization of Manufacturing Processes Using Evolutionary Algorithms

2012 ◽  
pp. 44-66
Author(s):  
M. Kanthababu
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Multiple Objectives ◽  
Manufacturing Processes ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Local Optima ◽  
Multi Objective ◽  
Research Areas

Recently evolutionary algorithms have created more interest among researchers and manufacturing engineers for solving multiple-objective problems. The objective of this chapter is to give readers a comprehensive understanding and also to give a better insight into the applications of solving multi-objective problems using evolutionary algorithms for manufacturing processes. The most important feature of evolutionary algorithms is that it can successfully find globally optimal solutions without getting restricted to local optima. This chapter introduces the reader with the basic concepts of single-objective optimization, multi-objective optimization, as well as evolutionary algorithms, and also gives an overview of its salient features. Some of the evolutionary algorithms widely used by researchers for solving multiple objectives have been presented and compared. Among the evolutionary algorithms, the Non-dominated Sorting Genetic Algorithm (NSGA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) have emerged as most efficient algorithms for solving multi-objective problems in manufacturing processes. The NSGA method applied to a complex manufacturing process, namely plateau honing process, considering multiple objectives, has been detailed with a case study. The chapter concludes by suggesting implementation of evolutionary algorithms in different research areas which hold promise for future applications.

scholarly journals Optimal Design of Water Pipeline and Micro-Hydro Turbine by Genetic Algorithm

10.29007/vf78 ◽  
2018 ◽  
Author(s):  
Camillo Bosco ◽  
Giuseppe Pezzinga ◽  
Marco Sinagra ◽  
Tullio Tucciarelli
Keyword(s):  
Genetic Algorithm ◽  
Rotational Velocity ◽  
Economic Value ◽  
Cross Flow ◽  
Net Benefit ◽  
Traditional Design ◽  
Unit Energy ◽  
Hydropower Production ◽  
Water Pipeline

The economic value of the potential energy hidden in water resources is becoming more and more relevant for pipe design. In this work a new way to design drinking main waterlines, embedding also the potential hydroelectric production as pipeline benefit, is presented. The optimum design of a cross-flow turbine, on the basis of the available head jump and discharge is first outlined; the description of a genetic algorithm to minimize the total cost (pipeline plus machinery) minus the net benefit (hydropower production) is then presented. Finally, a comparison is carried out among the costs of a case study pipeline assuming a) no hydropower production and traditional design criteria and b) two different scenarios with different values of benefits per unit energy production. The two scenarios lead to hydropower production with constant impeller rotational velocity in one case and with variable impeller rotational velocity in the other one.

On Measuring the Attributes of Evolutionary Algorithms

2011 ◽  
pp. 332-348
Author(s):  
J. L. Fernandez-Villacanas Martin ◽  
P. Marrow ◽  
M. Shackleton
Keyword(s):  
Genetic Algorithm ◽  
Information Retrieval ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Selection Process ◽  
Relative Effectiveness ◽  
Evolutionary Process ◽  
Neutral Evolution ◽  
Adaptive Landscape ◽  
The Future

In this chapter we compare the performance of two contrasting evolutionary algorithms addressing a similar problem, of information retrieval. The first, BTGP, is based upon genetic programming, while the second, MGA, is a genetic algorithm. We analyze the performance of these evolutionary algorithms through aspects of the evolutionary process they undergo while filtering information. We measure aspects of the variation existing in the population undergoing evolution, as well as properties of the selection process. We also measure properties of the adaptive landscape in each algorithm, and quantify the importance of neutral evolution for each algorithm. We choose measures of these properties because they appear generally important in evolution. Our results indicate why each algorithm is effective at information retrieval, however they do not provide a means of quantifying the relative effectiveness of each algorithm. We attribute this difficulty to the lack of appropriate measures available to measure properties of evolutionary algorithms, and suggest some criteria for useful evolutionary measures to be developed in the future.

On Measuring the Attributes of Evolutionary Algorithms

2011 ◽  
pp. 281-302
Author(s):  
J.-L. Fernandez-Villacanas Martin ◽  
P. Marrow ◽  
M. Shackleton
Keyword(s):  
Genetic Algorithm ◽  
Information Retrieval ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Selection Process ◽  
Relative Effectiveness ◽  
Evolutionary Process ◽  
Neutral Evolution ◽  
Adaptive Landscape ◽  
The Future

In this chapter we compare the performance of two contrasting evolutionary algorithms addressing a similar problem, of information retrieval. The first, BTGP, is based upon genetic programming, while the second, MGA, is a genetic algorithm. We analyze the performance of these evolutionary algorithms through aspects of the evolutionary process they undergo while filtering information. We measure aspects of the variation existing in the population undergoing evolution, as well as properties of the selection process. We also measure properties of the adaptive landscape in each algorithm, and quantify the importance of neutral evolution for each algorithm. We choose measures of these properties because they appear generally important in evolution. Our results indicate why each algorithm is effective at information retrieval, however they do not provide a means of quantifying the relative effectiveness of each algorithm. We attribute this difficulty to the lack of appropriate measures available to measure properties of evolutionary algorithms, and suggest some criteria for useful evolutionary measures to be developed in the future.

CHAOS SYNTHESIS BY MEANS OF EVOLUTIONARY ALGORITHMS

2008 ◽  
Vol 18 (04) ◽  
pp. 911-942 ◽  
Author(s):  
IVAN ZELINKA ◽  
GUANRONG CHEN ◽  
SERGEJ CELIKOVSKY
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Differential Evolution ◽  
Cost Function ◽  
Case Studies ◽  
Chaotic System ◽  
Chaotic Systems ◽  
Reliability And Robustness ◽  
Eleven Case

This paper introduces the notion of chaos synthesis by means of evolutionary algorithms and develops a new method for chaotic systems synthesis. This method is similar to genetic programming and grammatical evolution and is being applied along with three evolutionary algorithms: differential evolution, self-organizing migration and genetic algorithm. The aim of this investigation is to synthesize new and "simple" chaotic systems based on some elements contained in a prechosen existing chaotic system and a properly defined cost function. The investigation consists of eleven case studies: the aforementioned three evolutionary algorithms in eleven versions. For all algorithms, 100 simulations of chaos synthesis were repeated and then averaged to guarantee the reliability and robustness of the proposed method. The most significant results were carefully selected, visualized and commented in this report.

Multi-Objective Optimization of Manufacturing Processes Using Evolutionary Algorithms

2012 ◽  
pp. 352-376 ◽  
Author(s):  
M. Kanthababu
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Multiple Objectives ◽  
Manufacturing Processes ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Local Optima ◽  
Multi Objective ◽  
Research Areas

Recently evolutionary algorithms have created more interest among researchers and manufacturing engineers for solving multiple-objective problems. The objective of this chapter is to give readers a comprehensive understanding and also to give a better insight into the applications of solving multi-objective problems using evolutionary algorithms for manufacturing processes. The most important feature of evolutionary algorithms is that it can successfully find globally optimal solutions without getting restricted to local optima. This chapter introduces the reader with the basic concepts of single-objective optimization, multi-objective optimization, as well as evolutionary algorithms, and also gives an overview of its salient features. Some of the evolutionary algorithms widely used by researchers for solving multiple objectives have been presented and compared. Among the evolutionary algorithms, the Non-dominated Sorting Genetic Algorithm (NSGA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) have emerged as most efficient algorithms for solving multi-objective problems in manufacturing processes. The NSGA method applied to a complex manufacturing process, namely plateau honing process, considering multiple objectives, has been detailed with a case study. The chapter concludes by suggesting implementation of evolutionary algorithms in different research areas which hold promise for future applications.

scholarly journals Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

scholarly journals Optimized Strategic Planning of Future Norwegian Low-Voltage Networks with a Genetic Algorithm Applying Empirical Electric Vehicle Charging Data

Electricity ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 91-109
Author(s):  
Julian Wruk ◽  
Kevin Cibis ◽  
Matthias Resch ◽  
Hanne Sæle ◽  
Markus Zdrallek
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Low Voltage ◽  
Network Planning ◽  
Voltage Regulation ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
The Impact

This article outlines methods to facilitate the assessment of the impact of electric vehicle charging on distribution networks at planning stage and applies them to a case study. As network planning is becoming a more complex task, an approach to automated network planning that yields the optimal reinforcement strategy is outlined. Different reinforcement measures are weighted against each other in terms of technical feasibility and costs by applying a genetic algorithm. Traditional reinforcements as well as novel solutions including voltage regulation are considered. To account for electric vehicle charging, a method to determine the uptake in equivalent load is presented. For this, measured data of households and statistical data of electric vehicles are combined in a stochastic analysis to determine the simultaneity factors of household load including electric vehicle charging. The developed methods are applied to an exemplary case study with Norwegian low-voltage networks. Different penetration rates of electric vehicles on a development path until 2040 are considered.

scholarly journals Exploring Factors Influencing Scenarios Evolution of Waste NIMBY Crisis: Analysis of Typical Cases in China

2021 ◽  
Vol 18 (4) ◽  
pp. 2006
Author(s):  
Ling He ◽  
Qing Yang ◽  
Xingxing Liu ◽  
Lingmei Fu ◽  
Jinmei Wang
Keyword(s):  
Network Theory ◽  
Development Process ◽  
Dynamic Bayesian Network ◽  
Impact Factors ◽  
Social Stability ◽  
Ground Theory ◽  
Evolution Path ◽  
Dynamic Scenario ◽  
The Impact

As the impact factors of the waste Not-In-My-Back Yard (NIMBY) crisis are complex, and the scenario evolution path of it is diverse. Once the crisis is not handled properly, it will bring adverse effects on the construction of waste NIMBY facilities, economic development and social stability. Consequently, based on ground theory, this paper takes the waste NIMBY crisis in China from 2006 to 2019 as typical cases, through coding analysis, scenario evolution factors of waste NIMBY crisis are established. Furtherly, three key scenarios were obtained, namely, external situation (E), situation state (S), emergency management (M), what is more, scenario evolution law of waste NIMBY crisis is revealed. Then, the dynamic Bayesian network theory is used to construct the dynamic scenario evolution network of waste NIMBY crisis. Finally, based on the above models, Xiantao waste NIMBY crisis is taken as a case study, and the dynamic process of scenario evolution network is visually displayed by using Netica. The simulation results show that the scenario evolution network of Xiantao waste NIMBY crisis is basically consistent with the actual incident development process, which confirms the effectiveness and feasibility of the model.

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