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Evolution of numeric constants in Genetic Programming
Title
Evolution of numeric constants in Genetic Programming.
Creator
Fernandez, Thomas, Florida Atlantic University, Evett, Matthew P.
Abstract/Description
Genetic Programming is an evolutionary technique for searching through the space of S-expressions for programs that represent optimal or acceptable solutions to a given problem. Genetic Programming often has difficulty in finding the appropriate numeric constants to use in leaf nodes of the S-expressions. This thesis describes the use of local search algorithms to search for numeric constants that will improve the S-expressions found by Genetic Programming. Three methods, Multi-Dimensional...
Show moreGenetic Programming is an evolutionary technique for searching through the space of S-expressions for programs that represent optimal or acceptable solutions to a given problem. Genetic Programming often has difficulty in finding the appropriate numeric constants to use in leaf nodes of the S-expressions. This thesis describes the use of local search algorithms to search for numeric constants that will improve the S-expressions found by Genetic Programming. Three methods, Multi-Dimensional Hill Climbing, Vector Hill Climbing, and Numeric Mutation are combined with Genetic Programming to create hybrid systems. The performance of these hybrid systems is analyzed and future directions for improving Genetic Programming with the use of hybrid systems are discussed.
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Date Issued
1997
PURL
http://purl.flvc.org/fcla/dt/15493
Subject Headings
Genetic programming (Computer science)
Format
Document (PDF)
Novel Techniques in Genetic Programming
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.
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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)