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- Title
- Novel Techniques in Genetic Programming.
- Creator
- Fernandez, Thomas, Furht, Borko, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Three major problems make Genetic Programming unfeasible or impractical for real world problems. The first is the excessive time complexity.In nature the evolutionary process can take millions of years, a time frame that is clearly not acceptable for the solution of problems on a computer. In order to apply Genetic Programming to real world problems, it is essential that its efficiency be improved. The second is called overfitting (where results are inaccurate outside the training data). In a...
Show moreThree major problems make Genetic Programming unfeasible or impractical for real world problems. The first is the excessive time complexity.In nature the evolutionary process can take millions of years, a time frame that is clearly not acceptable for the solution of problems on a computer. In order to apply Genetic Programming to real world problems, it is essential that its efficiency be improved. The second is called overfitting (where results are inaccurate outside the training data). In a paper[36] for the Federal Reserve Bank, authors Neely and Weller state “a perennial problem with using flexible, powerful search procedures like Genetic Programming is overfitting, the finding of spurious patterns in the data. Given the well-documented tendency for the genetic program to overfit the data it is necessary to design procedures to mitigate this.” The third is the difficulty of determining optimal control parameters for the Genetic Programming process. Control parameters control the evolutionary process. They include settings such as, the size of the population and the number of generations to be run. In his book[45], Banzhaf describes this problem, “The bad news is that Genetic Programming is a young field and the effect of using various combinations of parameters is just beginning to be explored.” We address these problems by implementing and testing a number of novel techniques and improvements to the Genetic Programming process. We conduct experiments using data sets of various degrees of difficulty to demonstrate success with a high degree of statistical confidence.
Show less - Date Issued
- 2006
- PURL
- http://purl.flvc.org/fau/fd/FA00012570
- Subject Headings
- Evolutionary programming (Computer science), Genetic algorithms, Genetic programming (Computer science)
- Format
- Document (PDF)
- Title
- Applications of evolutionary algorithms in mechanical engineering.
- Creator
- Nelson, Kevin M., Florida Atlantic University, Huang, Ming Z., College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
- Abstract/Description
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Many complex engineering designs have conflicting requirements that must be compromised to effect a successful product. Traditionally, the engineering approach breaks up the complex problem into smaller sub-components in known areas of study. Tradeoffs occur between the conflicting requirements and a sub-optimal design results. A new computational approach based on the evolutionary processes observed in nature is explored in this dissertation. Evolutionary algorithms provide methods to solve...
Show moreMany complex engineering designs have conflicting requirements that must be compromised to effect a successful product. Traditionally, the engineering approach breaks up the complex problem into smaller sub-components in known areas of study. Tradeoffs occur between the conflicting requirements and a sub-optimal design results. A new computational approach based on the evolutionary processes observed in nature is explored in this dissertation. Evolutionary algorithms provide methods to solve complex engineering problems by optimizing the entire system, rather than sub-components of the system. Three standard forms of evolutionary algorithms have been developed: evolutionary programming, genetic algorithms and evolution strategies. Mathematical and algorithmic details are described for each of these methods. In this dissertation, four engineering problems are explored using evolutionary programming and genetic algorithms. Exploiting the inherently parallel nature of evolution, a parallel version of evolutionary programming is developed and implemented on the MasPar MP-1. This parallel version is compared to a serial version of the same algorithm in the solution of a trial set of unimodal and multi-modal functions. The parallel version had significantly improved performance over the serial version of evolutionary programming. An evolutionary programming algorithm is developed for the solution of electronic part placement problems with different assembly devices. The results are compared with previously published results for genetic algorithms and show that evolutionary programming can successfully solve this class of problem using fewer genetic operators. The finite element problem is cast into an optimization problem and an evolutionary programming algorithm is developed to solve 2-D truss problems. A comparison to LU-decomposition showed that evolutionary programming can solve these problems and that it has the capability to solve the more complex nonlinear problems. Finally, ordinary differential equations are discretized using finite difference representation and an objective function is formulated for the application of evolutionary programming and genetic algorithms. Evolutionary programming and genetic algorithms have the benefit of permitting over-constraining a problem to obtain a successful solution. In all of these engineering problems, evolutionary algorithms have been shown to offer a new solution method.
Show less - Date Issued
- 1997
- PURL
- http://purl.flvc.org/fcla/dt/12514
- Subject Headings
- Mechanical engineering, Genetic algorithms, Evolutionary programming (Computer science)
- Format
- Document (PDF)
- Title
- Cross-Layer Network Design using Controllers.
- Creator
- Slavik, Michael J., Mahgoub, Imad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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A cross-layer design architecture featuring a new network stack component called a controller is presented. The controller takes system status information from the protocol components and uses it to tune the behavior of the network stack to a given performance objective. A controller design strategy using a machine learning algorithm and a simulator is proposed, implemented, and tested. Results show the architecture and design strategy are capable of producing a network stack that outperforms...
Show moreA cross-layer design architecture featuring a new network stack component called a controller is presented. The controller takes system status information from the protocol components and uses it to tune the behavior of the network stack to a given performance objective. A controller design strategy using a machine learning algorithm and a simulator is proposed, implemented, and tested. Results show the architecture and design strategy are capable of producing a network stack that outperforms the existing protocol stack for arbitrary performance objectives. The techniques presented give network designers the flexibility to easily tune the performance of their networks to suit their application. This cognitive networking architecture has great potential for high performance in future wireless networks.
Show less - Date Issued
- 2007
- PURL
- http://purl.flvc.org/fau/fd/FA00012555
- Subject Headings
- Computer architecture, Wireless communication systems--Design and construction, Evolutionary programming (Computer science), Mathematical optimization
- Format
- Document (PDF)
- Title
- A Computational Study on Different Penalty Approaches for Constrained Optimization in Radiation Therapy Treatment Planning with a Simulated Annealing Algorithm.
- Creator
- Mohammadi Khoroushadi, Mohammad Sadegh, Kalantzis, Georgios, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
- Abstract/Description
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Intensity modulated radiation therapy (IMRT) is a cancer treatment method in which the intensities of the radiation beams are modulated; therefore these beams have non-uniform radiation intensities. The overall result is the delivery of the prescribed dose in the target volume. The dose distribution is conformal to the shape of the target and minimizes the dose to the nearby critical organs. An inverse planning algorithm is used to obtain those non-uniform beam intensities. In inverse...
Show moreIntensity modulated radiation therapy (IMRT) is a cancer treatment method in which the intensities of the radiation beams are modulated; therefore these beams have non-uniform radiation intensities. The overall result is the delivery of the prescribed dose in the target volume. The dose distribution is conformal to the shape of the target and minimizes the dose to the nearby critical organs. An inverse planning algorithm is used to obtain those non-uniform beam intensities. In inverse treatment planning, the treatment plan is achieved by using an optimization process. The optimized plan results to a high-quality dose distribution in the planning target volume (PTV), which receives the prescribed dose while the dose that is received by the organs at risk (OARs) is reduced. Accordingly, an objective function has to be defined for the PTV, while some constraints have to be considered to handle the dose limitations for the OARs.
Show less - Date Issued
- 2016
- PURL
- http://purl.flvc.org/fau/fd/FA00004765
- Subject Headings
- Image-guided radiation therapy., Radiation--Dosage., Mathematical optimization., Evolutionary programming (Computer science), Medical physics., Medical radiology--Data processing.
- Format
- Document (PDF)